Awesome-Robust-Machine-Learning

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Awesome-Robust-Machine-Learning

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A curated list of Robust Machine Learning papers/articles and recent advancements.

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# Awesome-Robust-Machine-Learning

Robust machine learning typically refers to the robustness of machine learning algorithms. For a machine learning algorithm to be considered robust, either the testing error has to be consistent with the training error, or the performance is stable after adding some noise to the dataset. This repo contains a curated list of papers/articles and recent advancements in Robust Machine Learning.

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
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  <img width="350" src="./images/dishit.png">
</p>

##### Table of Contents

1. [Papers](#Robust-Machine-Learning)
   - [Robust machine learning](#robust-machine-learning)
   - [Robust Machine Learning in Medical Domain](#robust-machine-learning-in-medical-domain)
   - [Distribution Shift](#distribution-shift)
   - [Distribution shift in question answering](#distribution-shift-in-question-answering)
   - [Clinical Distribution Shift](#clinical-distribution-shift)
   - [Medical Distribution Shift](#medical-distribution-shift)
   
2. [Code](#Code)
3. [datasets](#Datasets)
4. [Tutorials](#Tutorials)
5. [Researchers](#Researchers)

## Code

- **Estimating Generalization** 
  - [[Code]](https://github.com/chingyaoc/estimating-generalization)
  
- **Adversarial Robustness Toolbox (ART)**
  - [[Code]](https://github.com/Trusted-AI/adversarial-robustness-toolbox)
  
- **Robustness Gym**
  - [[Code]](https://github.com/robustness-gym/robustness-gym)
  
- **WILDS: A benchmark of in-the-wild distribution shifts**
  - [[Code]](https://github.com/p-lambda/wilds)
 
  
## Tutorials

- **CS282R: Robust Machine Learning Workshop**
  - [[Course]](https://github.com/kojino/Harvard-Robust-Machine-Learning)
  
- **Understand Robustness**
  - [[Course]](https://github.com/Nathanlauga/understand-robustness)

## Papers

## Robust machine learning

- **robust machine learning systems: reliability and security for ...**
  - [[Paper]](https://ieeexplore.ieee.org/document/8474192)
- **robust machine comprehension models via adversarial ...**
  - [[Paper]](https://aclanthology.org/N18-2091)
- **robust distant supervision relation extraction via deep ...**
  - [[Paper]](https://aclanthology.org/P18-1199)
- **towards robust neural machine translation - acl anthology**
  - [[Paper]](https://aclanthology.org/P18-1163)
- **trainable, scalable summarization using ... - acl anthology**
  - [[Paper]](https://aclanthology.org/C98-1009.pdf)
- **learning robust representations of text - acl anthology**
  - [[Paper]](https://aclanthology.org/D16-1207.pdf)
- **robust learning, smoothing, and parameter tying on ...**
  - [[Paper]](https://aclanthology.org/J95-3002.pdf)
- **robust machine reading comprehension by learning soft ...**
  - [[Paper]](https://aclanthology.org/2020.coling-main.248)
- **evaluating neural model robustness for machine ...**
  - [[Paper]](https://aclanthology.org/2021.eacl-main.210.pdf)
- **improving robustness of neural machine translation with ...**
  - [[Paper]](https://aclanthology.org/W19-5368)
- **learning to reweight examples for robust deep learning**
  - [[Paper]](https://proceedings.mlr.press/v80/ren18a/ren18a.pdf)
- **robust learning under uncertain test distributions: relating ...**
  - [[Paper]](http://proceedings.mlr.press/v32/wen14.html)
- **robust reinforcement learning: a constrained game ...**
  - [[Paper]](https://proceedings.mlr.press/v144/yu21a.html)
- **overfitting in adversarially robust deep learning**
  - [[Paper]](http://proceedings.mlr.press/v119/rice20a/rice20a.pdf)
- **robust deep learning as optimal control: insights and ...**
  - [[Paper]](http://proceedings.mlr.press/v120/seidman20a/seidman20a.pdf)
- **selfie: refurbishing unclean samples for robust deep ...**
  - [[Paper]](https://proceedings.mlr.press/v97/song19b.html)
- **minimax regret optimization for robust machine learning ...**
  - [[Paper]](https://proceedings.mlr.press/v178/agarwal22b.html)
- **improving robustness of deep-learning-based image ...**
  - [[Paper]](https://proceedings.mlr.press/v119/raj20a.html)
- **efficient and robust automated machine learning**
- **robustness in machine learning - jerry li**
  - [[Paper]](https://jerryzli.github.io/robust-ml-fall19.html)
- **robust ml**
  - [[Paper]](https://www.robust-ml.org/)
- **robust machine learning models and their applications**
  - [[Paper]](https://dspace.mit.edu/handle/1721.1/130760)
- **robust machine learning - data analytics and ... - tum**
  - [[Paper]](https://www.cs.cit.tum.de/daml/forschung/robust-machine-learning/)
- **what is the definition of the robustness of a machine learning ...**
  - [[Paper]](https://www.researchgate.net/post/What_is_the_definition_of_the_robustness_of_a_machine_learning_algorithm)
- **robust intelligence**
  - [[Paper]](https://www.robustintelligence.com/)
- **robust machine learning | the alan turing institute**
  - [[Paper]](https://www.turing.ac.uk/research/interest-groups/robust-machine-learning)
  - [[Paper]](https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning)
- **robust reinforcement learning**
  - [[Paper]](https://papers.nips.cc/paper/1841-robust-reinforcement-learning.pdf)
- **a closer look at accuracy vs. robustness**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/61d77652c97ef636343742fc3dcf3ba9-Paper.pdf)
- **provably robust deep learning via adversarially trained ...**
  - [[Paper]](https://papers.nips.cc/paper/9307-provably-robust-deep-learning-via-adversarially-trained-smoothed-classifiers.pdf)
- **robust reinforcement learning via adversarial training with ...**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/5cb0e249689cd6d8369c4885435a56c2-Paper.pdf)
- **robustness of classifiers: from adversarial to random noise**
  - [[Paper]](http://papers.nips.cc/paper/6331-robustness-of-classifiers-from-adversarial-to-random-noise.pdf)
- **boundary thickness and robustness in learning models**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/44e76e99b5e194377e955b13fb12f630-Paper.pdf)
- **a causal view on robustness of neural networks**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/02ed812220b0705fabb868ddbf17ea20-Paper.pdf)
- **robust reinforcement learning as a stackelberg game via ...**
  - [[Paper]](https://www.ijcai.org/proceedings/2022/0430.pdf)
- **global robustness evaluation of deep neural networks with ...**
  - [[Paper]](https://www.ijcai.org/proceedings/2019/0824.pdf)
- **improving the robustness of deep neural networks ... - ijcai**
  - [[Paper]](https://www.ijcai.org/proceedings/2019/403)
- **robust reinforcement learning as a stackelberg ... - ijcai**
  - [[Paper]](https://www.ijcai.org/proceedings/2022/430)
- **on guaranteed optimal robust explanations for nlp models**
  - [[Paper]](https://www.ijcai.org/proceedings/2021/366)
- **robust learning from noisy side-information by semidefinite ...**
  - [[Paper]](https://www.ijcai.org/proceedings/2019/349)
- **towards accurate and robust domain adaptation under ...**
  - [[Paper]](https://www.ijcai.org/proceedings/2020/0314.pdf)
- **high-robustness, low-transferability fingerprinting of neural ...**
  - [[Paper]](https://www.ijcai.org/proceedings/2021/0080.pdf)
- **on guaranteed optimal robust explanations for nlp ... - ijcai**
  - [[Paper]](https://www.ijcai.org/proceedings/2021/0366.pdf)
- **towards robust resnet: a small step but a giant leap - ijcai**
  - [[Paper]](https://www.ijcai.org/proceedings/2019/595)
- **learning perturbation sets for robust ma**
  - [[Paper]](https://openreview.net/pdf?id=MIDckA56aD)
- **analyzing the robustness of open-world machine learning.**
  - [[Paper]](https://openreview.net/forum?id=laa530q5gUR)
- **card: certifiably robust machine learning pipeline via ...**
  - [[Paper]](https://openreview.net/forum?id=roaZrQMGsd6&referrer=%5Bthe%20profile%20of%20Bo%20Li%5D(%2Fprofile%3Fid%3D~Bo_Li19))
- **sample selection for fair and robust training - openreview**
  - [[Paper]](https://openreview.net/pdf?id=IZNR0RDtGp3)
- **robustness between the worst and average case - nips papers**
  - [[Paper]](https://openreview.net/pdf?id=Y8YqrYeFftd)
- **machine learning explainability and robustness - openreview**
  - [[Paper]](https://openreview.net/forum?id=REwtJ_Af00Z)
- **secure byzantine-robust machine learning | openreview**
  - [[Paper]](https://openreview.net/forum?id=69EFStdgTD2&referrer=%5Bthe%20profile%20of%20Lie%20He%5D(%2Fprofile%3Fid%3D~Lie_He1))
- **robust deep reinforcement learning through adversarial loss**
  - [[Paper]](https://openreview.net/forum?id=eaAM_bdW0Q)
- **[2101.02559] robust machine learning systems - arxiv**
  - [[Paper]](https://arxiv.org/abs/2101.02559)
- **[2112.00639] robustness in deep learning for computer vision**
  - [[Paper]](https://arxiv.org/abs/2112.00639)
- **robust machine learning approach for predicting kinase ...**
  - [[Paper]](https://arxiv.org/abs/2111.08008)
- **towards efficient data-centric robust machine learning with ...**
  - [[Paper]](https://arxiv.org/abs/2203.03810)
- **why robust generalization in deep learning is difficult - arxiv**
  - [[Paper]](http://arxiv.org/abs/2205.13863)
- **rethinking machine learning robustness via its link with the ...**
  - [[Paper]](https://arxiv.org/abs/2202.08944)
- **sample-efficient training of robust deep learning models**
  - [[Paper]](https://arxiv.org/abs/2112.02542)
- **when doubly robust methods meet machine learning ... - arxiv**
  - [[Paper]](https://arxiv.org/abs/2204.10969)
- **[2202.05395] robust, deep, and reinforcement learning for ...**
  - [[Paper]](https://arxiv.org/abs/2202.05395)
- **robust learning from observation with model misspecification**
  - [[Paper]](https://arxiv.org/abs/2202.06003)
- **neural network for determining an asteroid mineral composition from reflectance spectra**
  - [[Paper]](https://arxiv.org/abs/2210.01006)
- **understanding adversarial robustness against on-manifold adversarial examples**
  - [[Paper]](https://arxiv.org/abs/2210.00430)
- **learning-based design of luenberger observers for autonomous nonlinear systems**
  - [[Paper]](https://arxiv.org/abs/2210.01476)
- **interpretable option discovery using deep q-learning and variational autoencoders**
  - [[Paper]](https://arxiv.org/abs/2210.01231)
- **secoe: alleviating sensors failure in machine learning-coupled iot systems**
  - [[Paper]](https://arxiv.org/abs/2210.02144)
- **robust fair clustering: a novel fairness attack and defense framework**
  - [[Paper]](https://arxiv.org/abs/2210.01953)
- **a closer look at robustness to l-infinity and spatial perturbations and their composition**
  - [[Paper]](https://arxiv.org/abs/2210.02577)
- **anomaly detection using data depth: multivariate case**
  - [[Paper]](https://arxiv.org/abs/2210.02851)
- **flow matching for generative modeling**
  - [[Paper]](https://arxiv.org/abs/2210.02747)
- **subspace learning for feature selection via rank revealing qr factorization: unsupervised and hybrid approaches with non-negative matrix factorization and evolutionary algorithm**
  - [[Paper]](https://arxiv.org/abs/2210.00418)
- **evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification**
  - [[Paper]](https://arxiv.org/abs/2210.00002)
- **latent state marginalization as a low-cost approach for improving exploration**
  - [[Paper]](https://arxiv.org/abs/2210.00999)
- **differentiable parsing and visual grounding of verbal instructions for object placement**
  - [[Paper]](https://arxiv.org/abs/2210.00215)
- **random data augmentation based enhancement: a generalized enhancement approach for medical datasets**
  - [[Paper]](https://arxiv.org/abs/2210.00824)
- **robust self-healing prediction model for high dimensional data**
  - [[Paper]](https://arxiv.org/abs/2210.01788)
- **strength-adaptive adversarial training**
  - [[Paper]](https://arxiv.org/abs/2210.01288)
- **feddig: robust federated learning using data digest to represent absent clients**
  - [[Paper]](https://arxiv.org/abs/2210.00737)
- **distributionally adaptive meta reinforcement learning**
  - [[Paper]](https://arxiv.org/abs/2210.03104)
- **blockchain-based monitoring for poison attack detection in decentralized federated learning**
  - [[Paper]](https://arxiv.org/abs/2210.02873)
- **ncvx: a general-purpose optimization solver for constrained machine and deep learning**
  - [[Paper]](https://arxiv.org/abs/2210.00973)
- **force-aware interface via electromyography for natural vr/ar interaction**
  - [[Paper]](https://arxiv.org/abs/2210.01225)
- **tikhonov regularization is optimal transport robust under martingale constraints**
  - [[Paper]](https://arxiv.org/abs/2210.01413)
- **multiguard: provably robust multi-label classification against adversarial examples**
  - [[Paper]](https://arxiv.org/abs/2210.01111)
- **unsupervised model selection for time-series anomaly detection**
  - [[Paper]](https://arxiv.org/abs/2210.01078)
- **perceptual attacks of no-reference image quality models with human-in-the-loop**
  - [[Paper]](https://arxiv.org/abs/2210.00933)
- **robust $q$-learning algorithm for markov decision processes under wasserstein uncertainty**
  - [[Paper]](https://arxiv.org/abs/2210.00898)
- **stability via adversarial training of neural network stochastic control of mean-field type**
  - [[Paper]](https://arxiv.org/abs/2210.00874)
- **comparison of data representations and machine learning architectures for user identification on arbitrary motion sequences**
  - [[Paper]](https://arxiv.org/abs/2210.00527)
- **spectral augmentation for self-supervised learning on graphs**
  - [[Paper]](https://arxiv.org/abs/2210.00643)
- **causal estimation for text data with (apparent) overlap violations**
  - [[Paper]](https://arxiv.org/abs/2210.00079)
- **codedsi: differentiable code search**
  - [[Paper]](https://arxiv.org/abs/2210.00328)
- **solving practical multi-body dynamics problems using a single neural operator**
  - [[Paper]](https://arxiv.org/abs/2210.00222)
- **on attacking out-domain uncertainty estimation in deep neural networks**
  - [[Paper]](https://arxiv.org/abs/2210.02191)
- **stock volatility prediction using time series and deep learning approach**
  - [[Paper]](https://arxiv.org/abs/2210.02126)
- **robust estimation of loss-based measures of model performance under covariate shift**
  - [[Paper]](https://arxiv.org/abs/2210.01980)
- **meta-ensemble parameter learning**
  - [[Paper]](https://arxiv.org/abs/2210.01973)
- **tree mover's distance: bridging graph metrics and stability of graph neural networks**
  - [[Paper]](https://arxiv.org/abs/2210.01906)
- **bayesft: bayesian optimization for fault tolerant neural network architecture**
  - [[Paper]](https://arxiv.org/abs/2210.01795)
- **paging with succinct predictions**
  - [[Paper]](https://arxiv.org/abs/2210.02775)
- **dynamical systems' based neural networks**
  - [[Paper]](https://arxiv.org/abs/2210.02373)
- **practical adversarial attacks on spatiotemporal traffic forecasting models**
  - [[Paper]](https://arxiv.org/abs/2210.02447)
- **chemalgebra: algebraic reasoning on chemical reactions**
  - [[Paper]](https://arxiv.org/abs/2210.02095)
- **null hypothesis test for anomaly detection**
  - [[Paper]](https://arxiv.org/abs/2210.02226)
- **rethinking lipschitz neural networks for certified l-infinity robustness**
  - [[Paper]](https://arxiv.org/abs/2210.01787)
- **learning robust kernel ensembles with kernel average pooling**
  - [[Paper]](https://arxiv.org/abs/2210.00062)
- **rap: risk-aware prediction for robust planning**
  - [[Paper]](https://arxiv.org/abs/2210.01368)
- **adaptive weight decay: on the fly weight decay tuning for improving robustness**
  - [[Paper]](https://arxiv.org/abs/2210.00094)
- **hip fracture prediction using the first principal component derived from fea-computed fracture loads**
  - [[Paper]](https://arxiv.org/abs/2210.01032)
- **uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles**
  - [[Paper]](https://arxiv.org/abs/2210.00336)
- **adversarial robustness of representation learning for knowledge graphs**
  - [[Paper]](https://arxiv.org/abs/2210.00122)
- **building robust machine learning systems: current progress, research challenges, and opportunities**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3316781.3323472)
- **building reproducible, reusable, and robust machine learning software**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3401025.3407941)
- **towards lightweight and robust machine learning for cdn caching**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3286062.3286082)
- **a robust machine learning technique to predict low-performing students**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3277569)
- **energy-efficient and adversarially robust machine learning with selective dynamic band filtering**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3453688.3461756)
- **robust machine learning-enabled routing for highly mobile vehicular networks with parrot in ns-3**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3460797.3460810)
- **the raise of machine learning hyperparameter constraints in python code**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3533767.3534400)
- **adversarial scrutiny of evidentiary statistical software**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3531146.3533228)
- **continuous lwe**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3406325.3451000)
- **rgrecsys: a toolkit for robustness evaluation of recommender systems**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3488560.3502192)
- **sentiment analysis using xlm-r transformer and zero-shot transfer learning on resource-poor indian language**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3461764)
- **power-attack: a comprehensive tool-chain for modeling and simulating attacks in power systems**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3470481.3472705)
- **discovering invariant and changing mechanisms from data**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539479)
- **the road to explainability is paved with bias: measuring the fairness of explanations**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3531146.3533179)
- **predicting cognitive load in an emergency simulation based on behavioral and physiological measures**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340555.3353735)
- **structack: structure-based adversarial attacks on graph neural networks**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3465336.3475110)
- **learning on the rings: self-supervised 3d finger motion tracking using wearable sensors**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534587)
- **ensemble learning for effective run-time hardware-based malware detection: a comprehensive analysis and classification**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3195970.3196047)
- **web-based startup success prediction**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3269206.3272011)
- **robust federated learning based on metrics learning and unsupervised clustering for malicious data detection**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3476883.3520221)
- **when video meets inertial sensors: zero-shot domain adaptation for finger motion analytics with inertial sensors**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3450268.3453537)
- **analyzing hardware based malware detectors**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3061639.3062202)
- **third workshop on adversarial learning methods for machine learning and data mining (advml 2021)**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3469455)
- **the fourth workshop on adversarial learning methods for machine learning and data mining (advml 2022)**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3542897)
- **robust large-scale machine learning in the cloud**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2939672.2939790)
- **adversarial robustness in deep learning: from practices to theories**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3470812)
- **machine learning and data cleaning: which serves the other?**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3506712)
- **a robust mature tomato detection in greenhouse scenes using machine learning and color analysis**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3318299.3318338)
- **privacy risks of securing machine learning models against adversarial examples**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3319535.3354211)
- **fair, robust, and data-efficient machine learning in healthcare**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3514094.3539552)
- **machine learning @ amazon**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2778865.2778867)
- **debiased-cam to mitigate image perturbations with faithful visual explanations of machine learning**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3491102.3517522)
- **robust high dimensional learning for lipschitz and convex losses.**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3455716.3455949)
- **constructivist design for interactive machine learning**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2851581.2892547)
- **machine learning robustness, fairness, and their convergence**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3470799)
- **machine learning explainability and robustness: connected at the hip**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3470806)
- **using machine learning to detect cancer early**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3394486.3409553)
- **security engineering for machine learning (keynote)**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3416507.3428118)
- **trustworthy machine learning: fairness and robustness**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3488560.3502211)
- **hidden stratification causes clinically meaningful failures in machine learning for medical imaging**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3368555.3384468)
- **making machine learning robust against adversarial inputs**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3134599)
- **towards a framework for validating machine learning results in medical imaging: opening the black box**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3332186.3332193)
- **a robust learning approach for regression models based on distributionally robust optimization**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3291125.3291138)
- **set-to-sequence methods in machine learning: a review**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1613/jair.1.12839)
- **machine learning in tourism**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3426826.3426837)
- **incorporating unlabeled data into distributionally-robust learning**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3546258.3546314)
- **attacks and defenses towards machine learning based systems**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3207677.3277988)
- **appflow: using machine learning to synthesize robust, reusable ui tests**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3236024.3236055)
- **towards robust production machine learning systems: managing dataset shift**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3324884.3415281)
- **secure and robust machine learning for healthcare: a survey**
  - [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9153891)
- **learning perturbation sets for robust machine learning**
  - [[Paper]](https://arxiv.org/pdf/2007.08450.pdf)
- **robust machine learning systems: challenges,current trends, perspectives, and the road ahead**
  - [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8979377)
- **secure byzantine-robust machine learning**
  - [[Paper]](https://arxiv.org/pdf/2006.04747.pdf)
- **adversarially robust machine learning with guarantees**
  - [[Paper]](https://searchworks.stanford.edu/view/13974182)
- **towards deploying robust machine learning systems**
  - [[Paper]](https://doi.org/10.7936/P1G4-5433)
- **a robust machine learning framework for diabetes prediction**
  - [[Paper]](https://doi.org/10.1007/978-3-030-89880-9_58)
- **impact-learning: a robust machine learning algorithm**
  - [[Paper]](http://dl.acm.org/citation.cfm?id=3411185)
- **regularization helps with mitigating poisoning attacks: distributionally-robust machine learning using the wasserstein distance**
  - [[Paper]](https://arxiv.org/pdf/2001.10655.pdf)
- **robust machine learning for colorectal cancer risk prediction and stratification**
  - [[Paper]](https://www.ncbi.nlm.nih.gov/pubmed/33693381)

## Robust Machine Learning in Medical Domain

- **robust machine learning variable importance analyses of ...**
  - [[Paper]](https://pubmed.ncbi.nlm.nih.gov/29527659/)
- **secure and robust machine learning for healthcare ... - core**
  - [[Paper]](https://core.ac.uk/download/pdf/328760438.pdf)
- **rethink robustness of deep learning models for medical ...**
  - [[Paper]](https://aimi.stanford.edu/events/research-meeting/aimi-research-meeting-rethink-robustness-deep-learning-models-medical-image)
- **identification of robust deep neural network models of ... - nature**
  - [[Paper]](https://www.nature.com/articles/s41746-022-00651-4)
- **robustness of ai-based prognostic and systems health ...**
  - [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S1367578821000195)
- **scalable few-shot learning of robust biomedical name ...**
  - [[Paper]](https://aclanthology.org/2021.bionlp-1.3.pdf)
- **applied medical code mapping with character-based deep ...**
  - [[Paper]](https://aclanthology.org/2021.naloma-1.2.pdf)
- **enhancing model robustness and fairness with causality**
  - [[Paper]](https://aclanthology.org/2021.cinlp-1.3.pdf)
- **identifying patients with pain in emergency departments ...**
  - [[Paper]](https://aclanthology.org/U19-1015.pdf)
- **robust machine translation evaluation with entailment ...**
  - [[Paper]](https://aclanthology.org/P09-1034.pdf)
- **does robustness improve fairness? approaching fairness ...**
  - [[Paper]](https://aclanthology.org/2021.findings-acl.294.pdf)
- **on the lack of robust interpretability of neural text classifiers**
  - [[Paper]](https://aclanthology.org/2021.findings-acl.327.pdf)
- **supervised machine learning for extractive query based ...**
  - [[Paper]](https://aclanthology.org/W18-5604.pdf)
- **robust benchmarking for machine learning of clinical entity ...**
  - [[Paper]](http://proceedings.mlr.press/v126/agrawal20a/agrawal20a.pdf)
- **feature robustness in non-stationary health records:**
  - [[Paper]](http://proceedings.mlr.press/v106/nestor19a/nestor19a.pdf)
- **just train twice: improving group robustness without ...**
  - [[Paper]](http://proceedings.mlr.press/v139/liu21f/liu21f.pdf)
- **addressing the false negative problem of deep learning ...**
  - [[Paper]](https://proceedings.mlr.press/v121/cheng20a.html)
- **what clinicians want: contextualizing explainable machine ...**
  - [[Paper]](http://proceedings.mlr.press/v106/tonekaboni19a/tonekaboni19a.pdf)
- **smoothed geometry for robust attribution - nips papers**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/9d94c8981a48d12adfeecfe1ae6e0ec1-Paper.pdf)
- **distilling robust and non-robust features in adversarial ...**
  - [[Paper]](https://papers.nips.cc/paper/2021/file/8e5e15c4e6d09c8333a17843461041a9-Paper.pdf)
- **exploring architectural ingredients of adversarially robust ...**
  - [[Paper]](https://papers.nips.cc/paper/2021/file/2bd7f907b7f5b6bbd91822c0c7b835f6-Paper.pdf)
- **adversarial training helps transfer learning via better ...**
  - [[Paper]](https://papers.nips.cc/paper/2021/file/d3aeec875c479e55d1cdeea161842ec6-Paper.pdf)
- **on single source robustness in deep fusion models**
  - [[Paper]](https://papers.nips.cc/paper/8728-on-single-source-robustness-in-deep-fusion-models.pdf)
- **coresets for robust training of neural networks against ...**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/8493eeaccb772c0878f99d60a0bd2bb3-Paper.pdf)
- **robust classification under sample selection bias**
  - [[Paper]](http://papers.nips.cc/paper/5458-robust-classification-under-sample-selection-bias.pdf)
- **metric learning for adversarial robustness**
  - [[Paper]](https://papers.nips.cc/paper/8339-metric-learning-for-adversarial-robustness.pdf)
- **robust high-dimensional classification from few positive ...**
  - [[Paper]](https://www.ijcai.org/proceedings/2022/271)
- **robust high-dimensional classification from few ... - ijcai**
  - [[Paper]](https://www.ijcai.org/proceedings/2022/0271.pdf)
- **multimodal attentional neural networks for diagnosis prediction**
  - [[Paper]](https://www.ijcai.org/proceedings/2019/0823.pdf)
- **towards adversarially robust deep image denoising - ijcai**
  - [[Paper]](https://www.ijcai.org/proceedings/2022/0211.pdf)
- **hybrid learning system for large-scale medical image analysis**
  - [[Paper]](https://www.ijcai.org/proceedings/2022/0824.pdf)
- **ai-powered posture training: application of machine learning ...**
  - [[Paper]](https://www.ijcai.org/proceedings/2019/0805.pdf)
- **certified robustness via randomized smoothing over ... - ijcai**
  - [[Paper]](https://www.ijcai.org/proceedings/2022/0467.pdf)
- **robust interpretable text classification against spurious ...**
  - [[Paper]](https://www.ijcai.org/proceedings/2022/0616.pdf)
- **robust and sparse fuzzy k-means clustering - ijcai**
  - [[Paper]](https://www.ijcai.org/Proceedings/16/Papers/317.pdf)
- **carben: composite adversarial robustness benchmark**
  - [[Paper]](https://www.ijcai.org/proceedings/2022/0851.pdf)
- **robust medical image segmentation by adapting neural ...**
  - [[Paper]](https://openreview.net/pdf?id=tv_pkmFzdC)
- **measuring robustness in deep learning based compressive ...**
  - [[Paper]](https://openreview.net/pdf?id=HqUeGFCQzX5)
- **conditional synthetic data generation for robust machine ...**
  - [[Paper]](https://openreview.net/pdf?id=o4JWdxYTjL8)
- **robust training of recurrent neural networks to handle missing ...**
  - [[Paper]](https://openreview.net/forum?id=S1jqMb2oM)
- **reliable and trustworthy machine learning for ... - nips papers**
  - [[Paper]](https://openreview.net/pdf?id=hNMOSUxE8o6)
- **robust neural networks are more interpretable for genomics**
  - [[Paper]](https://openreview.net/pdf/6b9befb80f2336b2c81716f766c4d4572c2fc827.pdf)
- **robust image segmentation quality assessment - openreview**
  - [[Paper]](https://openreview.net/pdf?id=nyhZXiaotm)
- **out of distribution detection and adversarial attacks on deep ...**
  - [[Paper]](https://openreview.net/forum?id=1iy7rdPCt_)
- **[2103.08291] robust machine learning in critical care - arxiv**
  - [[Paper]](https://arxiv.org/abs/2103.08291)
- **secure and robust machine learning for healthcare ... - arxiv**
  - [[Paper]](https://arxiv.org/pdf/2001.08103)
- **robust machine learning in critical care - arxiv**
  - [[Paper]](https://arxiv.org/pdf/2103.08291)
- **online reflective learning for robust medical image ... - arxiv**
  - [[Paper]](http://arxiv.org/abs/2207.00476)
- **style curriculum learning for robust medical image ... - arxiv**
  - [[Paper]](https://arxiv.org/abs/2108.00402)
- **deep learning models are not robust against noise in clinical ...**
  - [[Paper]](https://arxiv.org/abs/2108.12242)
- **evaluating the robustness of self-supervised learning in ...**
  - [[Paper]](https://arxiv.org/abs/2105.06986)
- **towards to robust and generalized medical image ... - arxiv**
  - [[Paper]](https://arxiv.org/abs/2108.03823)
- **uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective**
  - [[Paper]](https://arxiv.org/abs/2210.00589)
- **feddar: federated domain-aware representation learning**
  - [[Paper]](https://arxiv.org/abs/2209.04007)
- **3d ux-net: a large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation**
  - [[Paper]](https://arxiv.org/abs/2209.15076)
- **stacking ensemble learning in deep domain adaptation for ophthalmic image classification**
  - [[Paper]](https://arxiv.org/abs/2209.13420)
- **identifying differential equations to predict blood glucose using sparse identification of nonlinear systems**
  - [[Paper]](https://arxiv.org/abs/2209.13852)
- **robust and efficient imbalanced positive-unlabeled learning with self-supervision**
  - [[Paper]](https://arxiv.org/abs/2209.02459)
- **generalizability of adversarial robustness under distribution shifts**
  - [[Paper]](https://arxiv.org/abs/2209.15042)
- **fairness and robustness in anti-causal prediction**
  - [[Paper]](https://arxiv.org/abs/2209.09423)
- **feature selection integrated deep learning for ultrahigh dimensional and highly correlated feature space**
  - [[Paper]](https://arxiv.org/abs/2209.07011)
- **de-identification of french unstructured clinical notes for machine learning tasks**
  - [[Paper]](https://arxiv.org/abs/2209.09631)
- **boxshrink: from bounding boxes to segmentation masks**
  - [[Paper]](https://arxiv.org/abs/2208.03142)
- **rrwavenet: a compact end-to-end multi-scale residual cnn for robust ppg respiratory rate estimation**
  - [[Paper]](https://arxiv.org/abs/2208.08672)
- **geoecg: data augmentation via wasserstein geodesic perturbation for robust electrocardiogram prediction**
  - [[Paper]](https://arxiv.org/abs/2208.01220)
- **adaptive temperature scaling for robust calibration of deep neural networks**
  - [[Paper]](https://arxiv.org/abs/2208.00461)
- **rethinking degradation: radiograph super-resolution via aid-srgan**
  - [[Paper]](https://arxiv.org/abs/2208.03008)
- **an intertwined neural network model for eeg classification in brain-computer interfaces**
  - [[Paper]](https://arxiv.org/abs/2208.08860)
- **machine learning-based eeg applications and markets**
  - [[Paper]](https://arxiv.org/abs/2208.05144)
- **bayesian pseudo labels: expectation maximization for robust and efficient semi-supervised segmentation**
  - [[Paper]](https://arxiv.org/abs/2208.04435)
- **deformation equivariant cross-modality image synthesis with paired non-aligned training data**
  - [[Paper]](https://arxiv.org/abs/2208.12491)
- **federated learning for medical applications: a taxonomy, current trends, challenges, and future research directions**
  - [[Paper]](https://arxiv.org/abs/2208.03392)
- **fast-aid brain: fast and accurate segmentation tool using artificial intelligence developed for brain**
  - [[Paper]](https://arxiv.org/abs/2208.14360)
- **slice-level detection of intracranial hemorrhage on ct using deep descriptors of adjacent slices**
  - [[Paper]](https://arxiv.org/abs/2208.03403)
- **vector-based data improves left-right eye-tracking classifier performance after a covariate distributional shift**
  - [[Paper]](https://arxiv.org/abs/2208.00465)
- **learning from imperfect training data using a robust loss function: application to brain image segmentation**
  - [[Paper]](https://arxiv.org/abs/2208.04941)
- **predicting microsatellite instability and key biomarkers in colorectal cancer from h&e-stained images: achieving sota predictive performance with fewer data using swin transformer**
  - [[Paper]](https://arxiv.org/abs/2208.10495)
- **bpfish: blockchain and privacy-preserving fl inspired smart healthcare**
  - [[Paper]](https://arxiv.org/abs/2207.11654)
- **decorrelative network architecture for robust electrocardiogram classification**
  - [[Paper]](https://arxiv.org/abs/2207.09031)
- **liver segmentation using turbolift learning for ct and cone-beam c-arm perfusion imaging**
  - [[Paper]](https://arxiv.org/abs/2207.10167)
- **online reflective learning for robust medical image segmentation**
  - [[Paper]](https://arxiv.org/abs/2207.00476)
- **representation learning with information theory for covid-19 detection**
  - [[Paper]](https://arxiv.org/abs/2207.01437)
- **suppressing poisoning attacks on federated learning for medical imaging**
  - [[Paper]](https://arxiv.org/abs/2207.10804)
- **advances in prediction of readmission rates using long term short term memory networks on healthcare insurance data**
  - [[Paper]](https://arxiv.org/abs/2207.00066)
- **vector quantisation for robust segmentation**
  - [[Paper]](https://arxiv.org/abs/2207.01919)
- **pose-based tremor classification for parkinson's disease diagnosis from video**
  - [[Paper]](https://arxiv.org/abs/2207.06828)
- **machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids**
  - [[Paper]](https://arxiv.org/abs/2207.12478)
- **towards accurate and robust classification in continuously transitioning industrial sprays with mixup**
  - [[Paper]](https://arxiv.org/abs/2207.09609)
- **identifying the context shift between test benchmarks and production data**
  - [[Paper]](https://arxiv.org/abs/2207.01059)
- **pro-tip: phantom for robust automatic ultrasound calibration by tip detection**
  - [[Paper]](https://arxiv.org/abs/2206.05962)
- **a review of causality for learning algorithms in medical image analysis**
  - [[Paper]](https://arxiv.org/abs/2206.05498)
- **decentralized distributed learning with privacy-preserving data synthesis**
  - [[Paper]](https://arxiv.org/abs/2206.10048)
- **self-supervision on images and text reduces reliance on visual shortcut features**
  - [[Paper]](https://arxiv.org/abs/2206.07155)
- **amos: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation**
  - [[Paper]](https://arxiv.org/abs/2206.08023)
- **from labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels**
  - [[Paper]](https://arxiv.org/abs/2206.05288)
- **breast cancer classification using deep learned features boosted with handcrafted features**
  - [[Paper]](https://arxiv.org/abs/2206.12815)
- **cass: cross architectural self-supervision for medical image analysis**
  - [[Paper]](https://arxiv.org/abs/2206.04170)
- **adaptive adversarial training to improve adversarial robustness of dnns for medical image segmentation and detection**
  - [[Paper]](https://arxiv.org/abs/2206.01736)
- **learning underrepresented classes from decentralized partially labeled medical images**
  - [[Paper]](https://arxiv.org/abs/2206.15353)
- **independent evaluation of state-of-the-art deep networks for mammography**
  - [[Paper]](https://arxiv.org/abs/2206.12407)
- **robust monitoring for medical cyber-physical systems**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3446913.3460318)
- **detection of nasopharyngeal carcinoma using routine medical tests via machine learning**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3524086.3524102)
- **a machine learning approach for medical device classification**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3494193.3494232)
- **machine learning for the developing world**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3210548)
- **prediction of adverse drug reaction using machine learning and deep learning based on an imbalanced electronic medical records dataset**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3472813.3472817)
- **explainability methods for machine learning systems for multimodal medical datasets: research proposal**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3524273.3533925)
- **analyzing the robustness of open-world machine learning**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3338501.3357372)
- **validation methods to promote real-world applicability of machine learning in medicine**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3441369.3441372)
- **machine learning approaches for extracting genetic medical data information**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3018896.3066906)
- **deep learning for medical anomaly detection – a survey**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3464423)
- **evaluation of applied machine learning for health misinformation detection via survey of medical professionals on controversial topics in pediatrics**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3472813.3472814)
- **robust i/o-compute concurrency for machine learning pipelines in constrained cyber-physical devices**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3461648.3463842)
- **the early detection of subclinical ketosis in dairy cows using machine learning methods**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3055635.3056625)
- **text classification of diseases treated by traditional chinese medicine prescription based on machine learning**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3429889.3429895)
- **deep learning in medical imaging: fmri big data analysis via convolutional neural networks**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3219104.3229250)
- **multi-layer representation learning for medical concepts**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2939672.2939823)
- **a robust framework for accelerated outcome-driven risk factor identification from ehr**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330718)
- **improving early prognosis of dementia using machine learning methods**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3502433)
- **on the need of machine learning as a service for the internet of things**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3109761.3109783)
- **federated multi-view learning for private medical data integration and analysis**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3501816)
- **federated learning in a medical context: a systematic literature review**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3412357)
- **automatic processing of electronic medical records using deep learning**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3240925.3240961)
- **a multi-agent feature selection and hybrid classification model for parkinson's disease diagnosis**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3433180)
- **object detection and classification using machine learning techniques: a comparison of haar cascades and neural networks**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3484824.3484895)
- **automatic differentiation in machine learning: a survey**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3122009.3242010)
- **an adversarial approach for the robust classification of pneumonia from chest radiographs**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3368555.3384458)
- **analysis of machine learning models predicting quality of life for cancer patients**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3444757.3485103)
- **diagnosis of methylmalonic acidemia using machine learning methods**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340997.3341000)
- **assuring the machine learning lifecycle: desiderata, methods, and challenges**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3453444)
- **bringing machine learning closer to non-experts: proposal of a user-friendly machine learning tool in the healthcare domain**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3486011.3486469)
- **robust machine learning in critical care — software engineering and medical perspectives**
  - [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9474414)
- **open source robust machine learning software for medical patient data analysis and cloud storage**
  - [[Paper]](https://doi.org/10.1007/978-3-030-64610-3_104)
- **robust machine learning variable importance analyses of medical conditions for health care spending**
  - [[Paper]](https://doi.org/10.1111/1475-6773.12848)
- **robust machine learning against adversarial samples at test time**
  - [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9149002)
- **lung cancer prediction using robust machine learning and image enhancement methods on extracted gray-level co-occurrence matrix features**
  - [[Paper]](https://doi.org/10.3390/app12136517)
- **a robust machine learning predictive model for maternal health risk**
  - [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9885515)
- **robust medical image registration and motion modeling based on machine learning. (le recalage robuste d'images médicales et la modélisation du mouvement basée sur l'apprentissage profond)**
  - [[Paper]](https://tel.archives-ouvertes.fr/tel-02954033)
- **a robust and stable gene selection algorithm based on graph theory and machine learning**
  - [[Paper]](https://doi.org/10.1186/s40246-021-00366-9)

### Distribution Shift

- **difference between distribution shift and data shift, concept ...**
  - [[Paper]](https://stats.stackexchange.com/questions/548405/difference-between-distribution-shift-and-data-shift-concept-drift-and-model-dr)
- **understanding dataset shift - towards data science**
  - [[Paper]](https://towardsdatascience.com/understanding-dataset-shift-f2a5a262a766)
- **distribution shift framework - deepmind**
  - [[Paper]](https://www.deepmind.com/open-source/distribution-shift-framework)
- **4.7. environment and distribution shift**
  - [[Paper]](https://d2l.ai/chapter_linear-classification/environment-and-distribution-shift.html)
- **learning to predict and make decisions under distribution shift**
  - [[Paper]](https://www.ml.cmu.edu/research/phd-dissertation-pdfs/yw4_phd_ml_2021.pdf)
- **dataset shift in machine learning - mit press**
  - [[Paper]](https://mitpress.mit.edu/9780262545877/dataset-shift-in-machine-learning/)
- **mechanical mnist – distribution shift - openbu**
  - [[Paper]](https://open.bu.edu/handle/2144/44485)
- **principles of distribution shift (pods) - icml 2022**
  - [[Paper]](https://icml.cc/Conferences/2022/ScheduleMultitrack?event=13465)
- **microsoft/distribution-shift-latent-representations - github**
  - [[Paper]](https://github.com/microsoft/distribution-shift-latent-representations)
- **neurips distshift workshop 2021 - google sites**
  - [[Paper]](https://sites.google.com/view/distshift2021)
- **types of out-of-distribution texts and how to detect them**
  - [[Paper]](https://aclanthology.org/2021.emnlp-main.835.pdf)
- **shifted label distribution matters in distantly supervised ...**
  - [[Paper]](https://aclanthology.org/D19-1397.pdf)
- **to annotate or not? predicting performance drop under ...**
  - [[Paper]](https://aclanthology.org/D19-1222.pdf)
- **contrastive out-of-distribution detection for pretrained ...**
  - [[Paper]](https://aclanthology.org/2021.emnlp-main.84.pdf)
- **on continual model refinement in out-of-distribution data ...**
  - [[Paper]](https://aclanthology.org/2022.acl-long.223)
- **distributionally robust recurrent decoders with random ...**
  - [[Paper]](https://aclanthology.org/2022.repl4nlp-1.1)
- **estimating the impact of domain shift on parser error**
  - [[Paper]](https://aclanthology.org/2022.findings-acl.68/)
- **social media text classification under negative covariate shift**
  - [[Paper]](https://aclanthology.org/D15-1282.pdf)
- **distributionally robust finetuning bert for covariate drift in ...**
  - [[Paper]](https://aclanthology.org/2022.acl-long.139.pdf)
- **adversarial adaptation of synthetic or stale data**
  - [[Paper]](https://aclanthology.org/P17-1119)
- **semi-supervised domain adaptation for dependency parsing ...**
  - [[Paper]](https://aclanthology.org/2022.acl-long.74)
- **joint and conditional estimation of tagging and parsing models**
  - [[Paper]](https://aclanthology.org/P01-1042.pdf)
- **measure and improve robustness in nlp models: a survey**
  - [[Paper]](https://aclanthology.org/2022.naacl-main.339.pdf)
- **unlearn dataset bias in natural language inference by fitting ...**
  - [[Paper]](https://aclanthology.org/D19-6115)
- **2022.findings-naacl.13.pdf - acl anthology**
  - [[Paper]](https://aclanthology.org/2022.findings-naacl.13.pdf)
- **an investigation of the (in)effectiveness of counterfactually ...**
  - [[Paper]](https://aclanthology.org/2022.acl-long.256)
- **methods for estimating and improving robustness of ...**
  - [[Paper]](https://aclanthology.org/2022.naacl-srw.6.pdf)
- **evaluating lottery tickets under distributional shifts**
  - [[Paper]](https://aclanthology.org/D19-6117.pdf)
- **test-time training can close the natural distribution shift ...**
  - [[Paper]](https://proceedings.mlr.press/v162/darestani22a.html)
- **examining and combating spurious features under ...**
  - [[Paper]](https://proceedings.mlr.press/v139/zhou21g.html)
- **on distribution shift in learning-based bug detectors**
  - [[Paper]](https://proceedings.mlr.press/v162/he22a/he22a.pdf)
- **estimating generalization under distribution shifts via domain ...**
  - [[Paper]](https://proceedings.mlr.press/v119/chuang20a.html)
- **a label transformation framework for correcting label shift**
  - [[Paper]](https://proceedings.mlr.press/v119/guo20d.html)
- **bayesian adaptation for covariate shift**
  - [[Paper]](https://papers.nips.cc/paper/2021/hash/07ac7cd13fd0eb1654ccdbd222b81437-Abstract.html)
- **rethinking importance weighting for deep learning under ...**
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  - [[Paper]](https://papers.nips.cc/paper/2021/file/0d441de75945e5acbc865406fc9a2559-Paper.pdf)
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  - [[Paper]](https://papers.nips.cc/paper/2021/file/73fed7fd472e502d8908794430511f4d-Paper.pdf)
- **revisiting the calibration of modern neural networks**
  - [[Paper]](https://papers.nips.cc/paper/2021/file/8420d359404024567b5aefda1231af24-Paper.pdf)
- **reducing unimodal biases for visual question answering**
  - [[Paper]](https://papers.nips.cc/paper/8371-rubi-reducing-unimodal-biases-for-visual-question-answering.pdf)
- **debiased visual question answering from feature and ...**
  - [[Paper]](https://papers.nips.cc/paper/2021/file/1f4477bad7af3616c1f933a02bfabe4e-Paper.pdf)
- **on the value of out-of-distribution testing: an example of ...**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/045117b0e0a11a242b9765e79cbf113f-Paper.pdf)
- **mitigating covariate shift in imitation learning via offline data ...**
  - [[Paper]](https://papers.nips.cc/paper/2021/file/07d5938693cc3903b261e1a3844590ed-Paper.pdf)
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  - [[Paper]](https://papers.nips.cc/paper/2021/file/01894d6f048493d2cacde3c579c315a3-Paper.pdf)
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  - [[Paper]](https://papers.nips.cc/paper/8435-learning-imbalanced-datasets-with-label-distribution-aware-margin-loss.pdf)
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  - [[Paper]](http://www.ijcai.org/Proceedings/16/Papers/374.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2021/0585.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2022/0141.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2019/0625.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2020/0725.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2022/0275.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2022/0677.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2022/0085.pdf)
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  - [[Paper]](http://www.ijcai.org/Proceedings/71/Papers/048.pdf)
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  - [[Paper]](https://openreview.net/forum?id=GrZmKDYCp6H)
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  - [[Paper]](https://openreview.net/forum?id=2JFVnWuvrvV)
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  - [[Paper]](https://openreview.net/pdf?id=Dl4LetuLdyK)
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  - [[Paper]](https://openreview.net/forum?id=qM45LHaWM6E)
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  - [[Paper]](https://openreview.net/pdf?id=9hgEG-k57Zj)
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  - [[Paper]](https://openreview.net/forum?id=rbrouCKPiej)
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  - [[Paper]](https://openreview.net/forum?id=9hgEG-k57Zj)
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  - [[Paper]](https://openreview.net/forum?id=-440wKL2oJV)
- **generative question answering: learning to ...**
  - [[Paper]](https://openreview.net/pdf?id=Bkx0RjA9tX)
- **if your data distribution shifts, use self-learning**
  - [[Paper]](https://openreview.net/pdf?id=1oEvY1a67c1)
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  - [[Paper]](https://arxiv.org/abs/2004.14444)
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  - [[Paper]](https://arxiv.org/pdf/2201.00299)
- **toward a fine-grained analysis of distribution shifts in ... - arxiv**
  - [[Paper]](https://arxiv.org/pdf/2205.02870)
- **arxiv:2006.05121v3 [cs.cv] 7 apr 2021**
  - [[Paper]](https://arxiv.org/pdf/2006.05121)
- **afine-grained analysis on distribution shift - arxiv**
  - [[Paper]](https://arxiv.org/pdf/2110.11328)
- **arxiv:2207.08739v1 [cs.cv] 18 jul 2022**
  - [[Paper]](https://arxiv.org/pdf/2207.08739)
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  - [[Paper]](https://arxiv.org/pdf/2107.11576)
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  - [[Paper]](https://arxiv.org/pdf/2207.01168)
- **how good are deep models in understanding\\ the generated ...**
  - [[Paper]](https://arxiv.org/abs/2208.10760)
- **task formulation matters when learning continually: a case study in visual question answering**
  - [[Paper]](https://arxiv.org/abs/2210.00044)
- **complexity-based prompting for multi-step reasoning**
  - [[Paper]](https://arxiv.org/abs/2210.00720)
- **test-time adaptation for visual document understanding**
  - [[Paper]](https://arxiv.org/abs/2206.07240)
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  - [[Paper]](https://arxiv.org/abs/2205.01397)
- **rethinking evaluation practices in visual question answering: a case study on out-of-distribution generalization**
  - [[Paper]](https://arxiv.org/abs/2205.12191)
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  - [[Paper]](https://arxiv.org/abs/2205.14334)
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  - [[Paper]](https://arxiv.org/abs/2204.07496)
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  - [[Paper]](https://arxiv.org/abs/2202.08836)
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  - [[Paper]](https://arxiv.org/abs/2201.03233)
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  - [[Paper]](https://arxiv.org/abs/2201.09639)
- **local distributional chaos**
  - [[Paper]](https://arxiv.org/abs/2112.01457)
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  - [[Paper]](https://arxiv.org/abs/2111.09831)
- **dair: data augmented invariant regularization**
  - [[Paper]](https://arxiv.org/abs/2110.11205)
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  - [[Paper]](https://arxiv.org/abs/2109.07377)
- **aggregate or not? exploring where to privatize in dnn based federated learning under different non-iid scenes**
  - [[Paper]](https://arxiv.org/abs/2107.11954)
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  - [[Paper]](https://arxiv.org/abs/2107.12580)
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  - [[Paper]](https://arxiv.org/abs/2106.06607)
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  - [[Paper]](https://arxiv.org/abs/2106.09129)
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  - [[Paper]](https://arxiv.org/abs/2105.00773)
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  - [[Paper]](https://arxiv.org/abs/2102.02811)
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  - [[Paper]](https://arxiv.org/abs/2102.07461)
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  - [[Paper]](https://arxiv.org/abs/2012.01478)
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  - [[Paper]](https://arxiv.org/abs/2010.12230)
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  - [[Paper]](https://arxiv.org/abs/2010.04636)
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  - [[Paper]](https://arxiv.org/abs/2008.11147)
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  - [[Paper]](https://arxiv.org/abs/2006.05121)
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  - [[Paper]](https://arxiv.org/abs/2001.02165)
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  - [[Paper]](https://arxiv.org/abs/1912.03960)
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  - [[Paper]](https://arxiv.org/abs/1908.09450)
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  - [[Paper]](https://arxiv.org/abs/1907.11775)
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  - [[Paper]](https://arxiv.org/abs/1905.00360)
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  - [[Paper]](https://arxiv.org/abs/1901.08746)
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  - [[Paper]](https://arxiv.org/abs/1812.04189)
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  - [[Paper]](https://arxiv.org/abs/1811.03744)
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  - [[Paper]](https://arxiv.org/abs/1809.04369)
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  - [[Paper]](https://arxiv.org/abs/1807.09665)
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  - [[Paper]](https://arxiv.org/abs/1806.09473)
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  - [[Paper]](https://arxiv.org/abs/1709.00640)
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  - [[Paper]](https://arxiv.org/abs/1602.05437)
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  - [[Paper]](https://arxiv.org/abs/1601.06241)
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  - [[Paper]](https://arxiv.org/abs/1505.07483)
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  - [[Paper]](https://arxiv.org/abs/1505.08142)
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  - [[Paper]](https://arxiv.org/abs/1307.3178)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3474085.3475350)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3471158.3472249)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3404835.3463259)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330770)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3432689)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3357384.3357905)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3295750.3298946)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2988238)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3184558.3191542)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3269206.3271765)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3321505)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340531.3411904)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3442381.3449814)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2983323.2983377)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531926)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2948063)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3077136.3080645)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3498557)
- **conversational question answering on heterogeneous sources**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531815)
- **open-retrieval conversational question answering**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397271.3401110)
- **fast parameter adaptation for few-shot image captioning and visual question answering**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3240508.3240527)
- **a corpus for hybrid question answering systems**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3184558.3191540)
- **explainable conversational question answering over heterogeneous sources**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531688)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3487553.3526023)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3529372.3533287)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3459637.3482416)
- **qanswer: towards question answering search over websites**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3487553.3524250)
- **semantic question answering on big data**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2928294.2928302)
- **a chinese knowledge base question answering system**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3459637.3481970)
- **knowledge graph embedding based question answering**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3289600.3290956)
- **duplicate detection in programming question answering communities**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3169795)
- **a factoid question answering system for vietnamese**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3184558.3191535)
- **humor detection in product question answering systems**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397271.3401077)
- **exploring diversification in non-factoid question answering**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3234944.3234973)
- **designing a question-answering system for comic contents**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3011549.3011554)
- **performance prediction for non-factoid question answering**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3341981.3344249)
- **non-factoid question answering in the legal domain**
  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3331184.3331431)
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  - [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3291291.3291311)
- **more accurate question answering on freebase**
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  - [[Paper]](https://gaoxiangluo.github.io/files/A_Survey_of_Causality_in_Visual_Question_Answering.pdf)
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  - [[Paper]](https://arxiv.org/pdf/2109.01558.pdf)
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  - [[Paper]](https://arxiv.org/pdf/2010.16412.pdf)
- **l g ] 9 n ov 2 01 8 density estimation for shift-invariant multidimensional distributions**
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  - [[Paper]](https://arxiv.org/pdf/2204.13653.pdf)
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  - [[Paper]](https://arxiv.org/pdf/2202.00254.pdf)
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  - [[Paper]](https://openreview.net/pdf?id=jrA5GAccy_)
- **how good are deep models in understanding the generated images?**
  - [[Paper]](https://arxiv.org/pdf/2208.10760.pdf)

### Clinical Distribution Shift

- **data distribution shifts and monitoring - chip huyen**
  - [[Paper]](https://huyenchip.com/2022/02/07/data-distribution-shifts-and-monitoring.html)
- **shifting the distribution | evidence for population health**
  - [[Paper]](https://academic.oup.com/book/9251/chapter/155945428)
- **principles for tackling distribution shift - youtube**
  - [[Paper]](https://www.youtube.com/watch?v=QKBh6TmvBaw)
- **zachary c. lipton: deep learning under distribution shift**
  - [[Paper]](https://www.youtube.com/watch?v=WhpZKIra-FQ)
- **preventing dataset shift from breaking machine-learning ...**
  - [[Paper]](https://hal.archives-ouvertes.fr/hal-03293375/file/main.pdf)
- **adapting event extractors to medical data - acl anthology**
  - [[Paper]](https://aclanthology.org/2021.eacl-main.258.pdf)
- **distinguishing clinical sentiment: the importance of domain ...**
  - [[Paper]](https://aclanthology.org/W19-1915.pdf)
- **the performance differences of a medical code prediction ...**
  - [[Paper]](https://aclanthology.org/2022.clinicalnlp-1.10.pdf)
- **investigating the challenges of temporal relation extraction ...**
  - [[Paper]](https://aclanthology.org/W18-5607.pdf)
- **rethinking group-robust algorithms in a label-wise setting**
  - [[Paper]](https://aclanthology.org/2022.findings-acl.192.pdf)
- **analyzing dynamic adversarial training data in the limit**
  - [[Paper]](https://aclanthology.org/2022.findings-acl.18.pdf)
- **how to leverage the multimodal ehr data for better medical ...**
  - [[Paper]](https://aclanthology.org/2021.emnlp-main.329.pdf)
- **gcn with external knowledge for clinical event detection**
  - [[Paper]](https://aclanthology.org/2021.ccl-1.106.pdf)
- **examining dataset shift during prospective validation**
  - [[Paper]](https://proceedings.mlr.press/v149/otles21a/otles21a.pdf)
- **evaluating domain generalization for survival analysis in ...**
  - [[Paper]](https://proceedings.mlr.press/v174/pfisterer22a/pfisterer22a.pdf)
- **understanding clinical collaborations through federated ...**
  - [[Paper]](https://proceedings.mlr.press/v149/caldas21a/caldas21a.pdf)
- **domain adaptation under target and conditional shift**
  - [[Paper]](http://proceedings.mlr.press/v28/zhang13d.pdf)
- **robust causal inference under covariate shift via worst-case ...**
  - [[Paper]](http://proceedings.mlr.press/v125/jeong20a/jeong20a.pdf)
- **what went wrong and when ... - review for neurips paper**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/08fa43588c2571ade19bc0fa5936e028-Review.html)
- **what went wrong and when? instance-wise feature ...**
  - [[Paper]](https://papers.nips.cc/paper/2020/hash/08fa43588c2571ade19bc0fa5936e028-Abstract.html)
- **evaluating model performance under worst-case ...**
  - [[Paper]](https://papers.nips.cc/paper/2021/file/908075ea2c025c335f4865f7db427062-Paper.pdf)
- **from predictions to decisions: using l kahead regularization**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/2adcfc3929e7c03fac3100d3ad51da26-Paper.pdf)
- **domain generalization via model-agnostic learning of ...**
  - [[Paper]](https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf)
- **improving robustness against common corruptions by ...**
  - [[Paper]](https://papers.nips.cc/paper/2020/file/85690f81aadc1749175c187784afc9ee-Paper.pdf)
- **likelihood ratios for out-of-distribution detection - openreview**
  - [[Paper]](https://papers.nips.cc/paper/9611-likelihood-ratios-for-out-of-distribution-detection.pdf)
- **the "moving targets" training algorithm**
  - [[Paper]](http://papers.nips.cc/paper/233-the-moving-targets-training-algorithm.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2021/0431.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2017/0517.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2020/0455.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2018/0096.pdf)
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  - [[Paper]](https://www.ijcai.org/proceedings/2020/0299.pdf)
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