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This is a collection of interesting papers that I have read so far or want to read. Note that the list is not up-to-date. Topics: reinforcement learning, deep learning, mathematics, statistics, bandit algorithms, optimization.

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# Reading List

This is a collection of interesting papers (and books) that I have read so far or want to read. Note that the list is not up-to-date.

## Table of Contents
1. [General Deep Learning](#general-deep-learning)
2. [Conformal Prediction](#conformal-prediction)
3. [Differential Geometry in Deep Learning](#differential-geometry-in-deep-learning)
4. [Dimensionality Reduction](#dimensionality-reduction)
5. [Thompson Sampling](#thompson-sampling)
6. [Deep Reinforcement Learning](#deep-reinforcement-learning)
7. [Reinforcement Learning](#reinforcement-learning)
8. [Bandit Algorithms](#bandit-algorithms)
9. [Optimization](#optimization)
10. [Statistics](#statistics)
11. [Probability Modeling & Inference](#probability-modeling--inference)
12. [Uncertainty Estimation](#uncertainty-estimation)
13. [Statistical Learning](#statistical-learning)
14. [Lecture Notes, Books and Courses](#lecture-notes-books-and-courses)
15. [Blogs](#blogs)
16. [Schools](#schools)

---

<a name="general-deep-learning"></a>
## 1. General Deep Learning
- **2023**: [Your diffusion model secretly knows the dimension of the data manifold](https://arxiv.org/pdf/2212.12611.pdf)
- **2022**: [Regularising Inverse Problems with Generative Machine Learning Models](https://www.ndfcampbell.org/assets/papers/reg_inv_problems.pdf)
- **2021**: [SCORE-BASED GENERATIVE MODELING THROUGH STOCHASTIC DIFFERENTIAL EQUATIONS](https://arxiv.org/pdf/2011.13456.pdf)
- **2021**: [Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck](https://openreview.net/pdf?id=90M-91IZ0JC)
- **2021**: [Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks](https://arxiv.org/pdf/2101.06475.pdf)
- **2021**: [Loss landscapes and optimization in over-parameterized non-linear systems and neural networks](https://arxiv.org/pdf/2003.00307.pdf)
- **2021**: [Why flatness correlates with generalization for Deep NN](https://arxiv.org/pdf/2103.06219.pdf)
- **2021**: [The Modern Mathematics of Deep Learning](https://arxiv.org/pdf/2105.04026.pdf)
- **2021**: [The Principles of Deep Learning Theory](https://arxiv.org/pdf/2106.10165.pdf)
- **2020**: [Neural tangent kernel](https://arxiv.org/pdf/1806.07572.pdf)
- **2018**: [Lipschitz regularity of deep neural networks: analysis and efficient estimation](https://papers.nips.cc/paper/2018/file/d54e99a6c03704e95e6965532dec148b-Paper.pdf)
- **2015**: [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf)
- **1998**: [Efficient BackProp](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)

---

<a name="conformal-prediction"></a>
## 2. Conformal Prediction
- **2022**: [Conformal Prediction: a Unified Review of Theory and New Challenges](https://arxiv.org/pdf/2005.07972.pdf)
- **2022**: [Conformal Off-Policy Prediction in Contextual Bandits](https://arxiv.org/pdf/2206.04405.pdf)
- **2020**: [Conformal Prediction Under Covariate Shift](https://arxiv.org/pdf/1904.06019.pdf)
- **2019**: [Conformalized Quantile Regression](https://proceedings.neurips.cc/paper/2019/file/5103c3584b063c431bd1268e9b5e76fb-Paper.pdf)
- **2005**: [Algorithmic Learning in a Random World](https://link.springer.com/book/10.1007/b106715)

---

<a name="differential-geometry-in-deep-learning"></a>
## 3. Differential Geometry in Deep Learning
- **2020**: [Neural Ordinary Differential Equations on Manifolds](https://arxiv.org/pdf/2006.06663.pdf)
- **2019**: [Diffeomorphic Learning](https://arxiv.org/pdf/1806.01240.pdf)
- **2019**: [Deep ReLU network approximation of functions on a manifold](https://arxiv.org/pdf/1908.00695.pdf)
- **2019**: [Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds](https://proceedings.neurips.cc/paper/2019/file/fd95ec8df5dbeea25aa8e6c808bad583-Paper.pdf)
- **2016**: [Deep nets for local manifold learning](https://arxiv.org/pdf/1607.07110.pdf)

---

<a name="dimensionality-reduction"></a>
## 4. Dimensionality Reduction
- **2024**: [On Sparsity and Sub-Gaussianity in the Johnson-Lindenstrauss Lemma](https://arxiv.org/pdf/2409.06275)
- **2020**: [Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey](https://arxiv.org/pdf/2009.10301.pdf)
- **2015**: [Parametric nonlinear dimensionality reduction using kernel t-SNE](https://core.ac.uk/download/pdf/20074835.pdf)
- **2009**: [Learning a Parametric Embedding by Preserving Local Structure](http://proceedings.mlr.press/v5/maaten09a/maaten09a.pdf)

---

<a name="thompson-sampling"></a>
## 5. Thompson Sampling
- **2020**: [A Tutorial on Thompson Sampling](https://arxiv.org/pdf/1707.02038.pdf)
- **2020**: [Neural Thompson Sampling](https://arxiv.org/pdf/2010.00827.pdf)
- **2018**: [Deep Contextual Multi-armed Bandits](https://arxiv.org/pdf/1807.09809.pdf)

---

<a name="deep-reinforcement-learning"></a>
## 6. Deep Reinforcement Learning

### Famous Applications
- **2022**: [CICERO](https://www.science.org/doi/10.1126/science.ade9097)

### Algorithms
- [List of algorithms](https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html)
- **2018**: [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://arxiv.org/pdf/1801.01290.pdf)
- **2017**: [Reinforcement Learning with Deep Energy-Based Policies](https://arxiv.org/pdf/1702.08165.pdf)

### DQN Convergence
- **2020**: [A Theoretic Analysis of DQN](https://arxiv.org/pdf/1901.00137.pdf)
- **2020**: [A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation](http://proceedings.mlr.press/v119/xu20c/xu20c.pdf)
- **2019**: [Towards Characterizing Divergence in Deep Q-Learning](https://arxiv.org/pdf/1903.08894.pdf)

### Soft Q Learning Theory
- **2018**: [Equivalence Between Policy Gradients and Soft Q-Learning](https://arxiv.org/pdf/1704.06440.pdf)

### Exploration
- **2022**: [Exploring through Random Curiosity with General Value Functions](https://arxiv.org/pdf/2211.10282.pdf)
- **2020**: [Planning go explore via self-supervised world models](https://arxiv.org/pdf/2005.05960.pdf)
- **2020**: [Hypermodels for Exploration](https://arxiv.org/pdf/2006.07464.pdf)
- **2018**: [DORA The Explorer: Directed Outreaching Reinforcement Action-Selection](https://arxiv.org/abs/1804.04012)
- **2017**: [#Exploration: A Study of Count-Based Exploration for Deep Reinforcement](https://arxiv.org/pdf/1611.04717.pdf)
- **2016**: [Unifying Count-Based Exploration and Intrinsic Motivation](https://arxiv.org/pdf/1606.01868.pdf)

### Stability
- **2021**: [Enforcing Robust Control Guarantees with Neural Network Policies](https://arxiv.org/pdf/2011.08105.pdf)
- **2018**: [Control-Theoretic Analysis of Smoothness for Stability-Certified Reinforcement Learning](https://lavaei.ieor.berkeley.edu/RL_1_2018.pdf)

### Dreamer Algorithm
- **2021**: [Mastering Atari with Discrete World-Models](https://arxiv.org/pdf/2010.02193.pdf)
- **2020**: [Dream to Control](https://arxiv.org/pdf/1912.01603.pdf)
- **2020**: [Planning to Explore via Supervised World-Models](https://arxiv.org/pdf/2005.05960.pdf)
- **2019**: [Learning Latent Dynamics from Pixels](https://arxiv.org/pdf/1811.04551.pdf)

### Goal Conditioned RL
- **2021**: [Adversarial Intrinsic Motivation for Reinforcement Learning](https://openreview.net/pdf?id=GYr3qnFKgU)

### Distributional RL
- **2021**: [GMAC: A Distributional Perspective on Actor-Critic Framework](https://arxiv.org/pdf/2105.11366.pdf)
- **2020**: [Sample-Based Distributional Policy Gradient](https://arxiv.org/pdf/2001.02652.pdf)
- **2019**: [Statistics and Samples in Distributional Reinforcement Learning](https://arxiv.org/pdf/1902.08102.pdf)
- **2018**: [Distributed Distributional Deterministic Policy Gradients](https://arxiv.org/pdf/1804.08617.pdf)

---

<a name="reinforcement-learning"></a>
## 7. Reinforcement Learning

### General Papers
- **2024**: [Stop Regressing: Training Value Functions via Classification for Scalable Deep RL](https://arxiv.org/pdf/2403.03950v1)
- **2023**: [BACKSTEPPING TEMPORAL DIFFERENCE LEARNING](https://arxiv.org/pdf/2302.09875)
- **2023**: [Empirical Design in Reinforcement Learning](https://arxiv.org/pdf/2304.01315.pdf)
- **2023**: [An Analysis of Quantile Temporal-Difference Learning](https://arxiv.org/pdf/2301.04462.pdf)
- **2022**: [Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error](https://proceedings.mlr.press/v162/fujimoto22a/fujimoto22a.pdf)
- **2022**: [Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach](https://proceedings.mlr.press/v162/wu22i/wu22i.pdf)
- **2021**: [Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction](https://arxiv.org/pdf/2006.03041.pdf)
- **2021**: [Adaptive Sampling for Best Policy Identification in MDPs](http://proceedings.mlr.press/v139/marjani21a/marjani21a.pdf)
- **2021**: [Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint](https://arxiv.org/pdf/2101.07123)
- **2020**: [Fast active learning for pure exploration in reinforcement learning](https://arxiv.org/abs/2007.13442)
- **2020**: [Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning](https://arxiv.org/pdf/2003.06898.pdf)
- **2019**: [Revisiting the Softmax Bellman Operator: New Benefits and New Perspective](https://arxiv.org/pdf/1812.00456.pdf)
- **2019**: [Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP](https://arxiv.org/pdf/1901.09311.pdf)
- **2019**: [Provably Efficient Reinforcement Learning with Linear Function Approximation](https://arxiv.org/pdf/1907.05388.pdf)
- **2018**: [Deep Reinforcement Learning that Matters](https://ojs.aaai.org/index.php/AAAI/article/download/11694/11553)
- **2018**: [Is Q-learning Provably Efficient?](https://arxiv.org/pdf/1807.03765.pdf)
- **2018**: [Adaptive Sampling for Policy Identification](https://arxiv.org/pdf/2009.13405.pdf)
- **2020**: [On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces](https://proceedings.neurips.cc/paper/2020/file/9fa04f87c9138de23e92582b4ce549ec-Paper.pdf)
- **2016**: [Learning the Variance of the Reward-To-Go](https://jmlr.org/papers/volume17/14-335/14-335.pdf)
- **2012**: [Policy Gradients with Variance Related Risk Criteria](https://arxiv.org/pdf/1206.6404.pdf)
- **2009**: [An Analysis of Reinforcement Learning with Function Approximation](http://icml2008.cs.helsinki.fi/papers/652.pdf)
- **2008**: [An Analysis of Model-Based Interval Estimation for Markov Decision Processes](https://www.sciencedirect.com/science/article/pii/S0022000008000767)
- **2006**: [PAC Model-Free Reinforcement Learning](https://cseweb.ucsd.edu/~ewiewior/06efficient.pdf)
- **2004**: [Bias and Variance in Value Function Estimation](https://icml.cc/Conferences/2004/proceedings/papers/248.pdf)
- **2001**: [Convergence of Optimistic and Incremental Q-Learning](https://proceedings.neurips.cc/paper/2001/file/6f2688a5fce7d48c8d19762b88c32c3b-Paper.pdf)
- **2001**: [TD Algorithm for the Variance of Return and Mean-Variance Reinforcement Learning](https://www.jstage.jst.go.jp/article/tjsai/16/3/16_3_353/_pdf)
- **2000**: [Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms](https://link.springer.com/content/pdf/10.1023/A:1007678930559.pdf)
- **1993**: [Convergence of Stochastic Iterative Dynamic Programming Algorithms](https://proceedings.neurips.cc/paper/1993/file/5807a685d1a9ab3b599035bc566ce2b9-Paper.pdf)
- **1992**: [Reinforcement Learning Applied to Linear Quadratic Regulation](https://papers.nips.cc/paper/1992/file/19bc916108fc6938f52cb96f7e087941-Paper.pdf)
- **1982**: [The Variance of Discounted Markov Decision Processes](https://www.jstor.org/stable/3213832)

### Zero-Shot RL
- **2023**: [Does Zero-Shot Reinforcement Learning Exist?](https://arxiv.org/pdf/2209.14935.pdf)
- **2021**: [Learning One Representation to Optimize All Rewards](https://arxiv.org/pdf/2103.07945.pdf)
- **2017**: [Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning](http://proceedings.mlr.press/v70/oh17a/oh17a.pdf)

### MDPs
- **2021**: [Hoeffding’s Inequality for General Markov Chains and Its Applications to Statistical Learning](https://jmlr.org/papers/v22/19-479.html)

### Constrained / Safe RL
- **2024**: [Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking](https://arxiv.org/pdf/2403.03185)
- **2022**: [Safety-constrained Reinforcement Learning with a Distributional Safety Critic](https://link.springer.com/article/10.1007/s10994-022-06187-8)
- **2022**: [Constrained Variational Policy Optimization for Safe Reinforcement Learning](https://arxiv.org/pdf/2201.11927.pdf)
- **2022**: [TRC: Trust Region Conditional Value at Risk for Safe Reinforcement Learning](https://ieeexplore.ieee.org/document/9677982)
- **2022**: [Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk](https://arxiv.org/pdf/2206.04436.pdf)
- **2022**: [SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics](https://arxiv.org/pdf/2204.09424.pdf)
- **2019**: [Benchmarking Safe Exploration in Deep Reinforcement Learning](https://cdn.openai.com/safexp-short.pdf)
- **2017**: [Constrained Policy Optimization](https://arxiv.org/pdf/1705.10528.pdf)
- **2017**: [Risk-Constrained Reinforcement Learning with Percentile Risk Criteria](https://arxiv.org/pdf/1512.01629.pdf)
- **2015**: [Variance-Constrained Actor-Critic Algorithms for Discounted and Average Reward MDPs](https://arxiv.org/pdf/1403.6530.pdf)
- **2015**: [A Comprehensive Survey on Safe Reinforcement Learning](https://www.jmlr.org/papers/volume16/garcia15a/garcia15a.pdf)
- **2015**: [Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach](https://arxiv.org/pdf/1506.02188.pdf)

### Off-Policy Evaluation
- **2022**: [A Review of Off-Policy Evaluation in Reinforcement Learning](https://arxiv.org/pdf/2212.06355.pdf)
- **2022**: [Conformal Off-Policy Prediction in Contextual Bandits](https://arxiv.org/pdf/2206.04405.pdf)
- **2020**: [CoinDICE: Off-Policy Confidence Interval Estimation](https://arxiv.org/pdf/2010.11652.pdf)
- **2018**: [Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation](https://arxiv.org/pdf/1810.12429.pdf)
- **2015**: [High Confidence Policy Improvement](https://people.cs.umass.edu/~pthomas/papers/Thomas2015b.pdf)
- **2015**: [High Confidence Off-Policy Evaluation](https://people.cs.umass.edu/~pthomas/papers/Thomas2015.pdf)
- **2000**: [Eligibility Traces for Off-Policy Policy Evaluation](https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1079&context=cs_faculty_pubs)

---

<a name="bandit-algorithms"></a>
## 8. Bandit Algorithms
- **2023**: [Quantile Bandits for Best Arms Identification](https://arxiv.org/pdf/2010.11568.pdf)
- **2020**: [Neural Contextual Bandits with Deep Representation and Shallow Exploration](https://arxiv.org/pdf/2012.01780.pdf)
- **2020**: [Neural Contextual Bandits with UCB-based Exploration](https://arxiv.org/pdf/1911.04462.pdf)
- **2020** [Contributions to the Optimal Solution of Several Bandit Problems](https://emiliekaufmann.github.io/HDR_EmilieKaufmann.pdf)
- **2016**: [Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks](https://proceedings.neurips.cc/paper/2016/file/abd815286ba1007abfbb8415b83ae2cf-Paper.pdf)
- **2016**: [Optimal Best Arm Identification with Fixed Confidence](https://arxiv.org/pdf/1602.04589.pdf)
- **2016**: [Explore First, Exploit Next: The True Shape of Regret in Bandit Problems](https://arxiv.org/abs/1602.07182)
- **2011**: [Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems](https://arxiv.org/pdf/1102.2670.pdf)
- **2011** [Pure exploration in finitely-armed and continuous-armed bandits](http://sbubeck.com/BMS--TCS09.pdf)
- **2010** [Best Arm Identification in Multi-Armed Bandits](http://sbubeck.com/COLT10_ABM.pdf)
- **2002**: [Finite-time Analysis of the Multiarmed Bandit Problem](https://homes.di.unimi.it/~cesabian/Pubblicazioni/ml-02.pdf)
- **2002**: [Using Confidence Bounds for Exploitation-Exploration Trade-offs](https://www.jmlr.org/papers/volume3/auer02a/auer02a.pdf)
- **2002**: [THE NONSTOCHASTIC MULTIARMED BANDIT PROBLEM∗](http://rob.schapire.net/papers/AuerCeFrSc01.pdf)

---

<a name="optimization"></a>
## 9. Optimization

### Min-Max Optimization
- **2021**: [A Mean-Field Analysis of Two-Player Zero-Sum Games](https://arxiv.org/pdf/2002.06277.pdf)
- **2021**: [The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets](http://proceedings.mlr.press/v139/hsieh21a/hsieh21a-supp.pdf)
- **2020**: [On the Convergence of Single-Call Stochastic Extra-Gradient Methods](https://arxiv.org/pdf/1908.08465.pdf)
- **2020**: [Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances](https://arxiv.org/pdf/2006.08141.pdf)
- **2020**: [On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems](http://proceedings.mlr.press/v119/lin20a/lin20a.pdf)
- **2020**: [Robust Reinforcement Learning via Adversarial Training with Langevin Dynamics](https://arxiv.org/pdf/2002.06063.pdf)
- **2018**: [Finding Mixed Nash Equilibria of Generative Adversarial Networks](https://arxiv.org/pdf/1811.02002.pdf)
- **2009**: [Subgradient Methods for Saddle-Point Problems](https://link.springer.com/article/10.1007/s10957-009-9522-7)

---

<a name="statistics"></a>
## 10. Statistics
- **2024**: [Information Lower Bounds for Robust Mean Estimation](https://arxiv.org/pdf/2403.01892)
- **2022**: [A Short Note on an Inequality between KL and TV](https://arxiv.org/pdf/2202.07198.pdf)
- **2020**: [A Tutorial on Quantile Estimation via Monte Carlo](https://web.njit.edu/~marvin/papers/qtut-r2.pdf)
- **2019**: [Safe Testing](https://arxiv.org/abs/1906.07801)
- **2012**: [Concentration Inequalities for Order Statistics](https://arxiv.org/pdf/1207.7209.pdf)
- **1996**: [Importance Sampling for Monte Carlo Estimation of Quantiles](https://web.stanford.edu/~glynn/papers/1996/G96.pdf)
- **1987**: [Better Bootstrap Confidence Intervals](https://www.jstor.org/stable/2289144)
- **1982**: [Some Methods for Testing the Homogeneity of Rainfall Records](https://www.sciencedirect.com/science/article/pii/002216948290066X)

---

<a name="probability-modeling--inference"></a>
## 11. Probability Modeling & Inference
- **2024**: [The Bayesian Learning Rule](https://arxiv.org/pdf/2107.04562)
- **2023**: [Semi-Implicit Variational Inference via Score Matching](https://openreview.net/pdf?id=sd90a2ytrt)
- **2021**: [Normalizing Flows for Probabilistic Modeling and Inference](https://jmlr.org/papers/volume22/19-1028/19-1028.pdf)
- **2020**: [Improved Techniques for Training Score-Based Generative Models](https://arxiv.org/pdf/2006.09011.pdf)
- **2019**: [Variational Approximations using Fisher Divergence](https://arxiv.org/pdf/1905.05284.pdf)
- **2018**: [Semi-Implicit Variational Inference](https://arxiv.org/pdf/1805.11183.pdf)
- **2018**: [Variational Inference: A Review for Statisticians](https://arxiv.org/abs/1601.00670)
- **2017**: [Variational Hamiltonian Monte Carlo via Score Matching](https://arxiv.org/pdf/1602.02219.pdf)
- **2013**: [Stochastic Variational Inference](https://jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf)
- **2013**: [Auto-Encoding Variational Bayes](https://arxiv.org/pdf/1312.6114.pdf)
- **2005**: [Estimation of Non-Normalized Statistical Models by Score Matching](https://jmlr.org/papers/volume6/hyvarinen05a/hyvarinen05a.pdf)

---

<a name="uncertainty-estimation"></a>
## 12. Uncertainty Estimation
- **2025**: [Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization](https://jmlr.org/papers/volume26/23-1359/23-1359.pdf)
- **2023**: [Epistemic Neural Networks](https://arxiv.org/pdf/2107.08924)
- **2022**: [Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping](https://arxiv.org/pdf/2206.03633)

---

## 13. Statistical Learning
- **2025** [User-friendly Introduction to PAC-Bayes Bounds](https://arxiv.org/pdf/2110.11216)
- **2009** [Agnostic Online Learning](https://www.cs.huji.ac.il/~shais/papers/BendavidPalShalevtech09.pdf)

---

<a name="lecture-notes-books-and-courses"></a>
## 14. Lecture Notes, Books and Courses
- **2024**: [Hypothesis Testing with E-values (Book)](https://www.stat.cmu.edu/~aramdas/ebook-v1.pdf)
- **2021**: [Regularization in RL, Google](https://rl-vs.github.io/rlvs2021/class-material/regularized_mdp/Regularization_RL_RLVS.pdf)
- [CS 6789: Foundations of Reinforcement Learning](https://wensun.github.io/CS6789.html)
- [RL Book Theory](https://rltheorybook.github.io/)
- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book.html)
- [Bandit Algorithms](https://tor-lattimore.com/downloads/book/book.pdf)
- **2021**: [Lecture Notes for Statistics 311/Electrical Engineering 377](https://web.stanford.edu/class/stats311/lecture-notes.pdf)
- **2015**: [Rademacher Complexities and VC Dimension](http://www.cs.cmu.edu/~hanxiaol/slides/rademacher_vc_hanxiaol.pdf)
- **2013**: [An Introduction to Stochastic Approximation](http://rcombes.supelec.free.fr/pdf/lecture_stoch_approx.pdf)
- **2006**: [System Identification and the Limits of Learning from Data](https://marco-campi.unibs.it/pdf-pszip/sys-id-and-limits-learning.pdf)
- [Deep Learning, Goodfellow et al., 2016](https://www.deeplearningbook.org)
- [The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman, 2009](https://web.stanford.edu/~hastie/ElemStatLearn)
- [Machine Learning: A Probabilistic Perspective, Murphy, 2012](https://www.cs.ubc.ca/~murphyk/MLbook/)
- [Probability Theory: The Logic of Science, E. T. Jaynes, 2003](https://www.amazon.com/Probability-Theory-Science-T-Jaynes/dp/0521592712)
- [CS285 at UC Berkeley, Deep Reinforcement Learning](http://rail.eecs.berkeley.edu/deeprlcourse/)
- [CS234 at Stanford University, Reinforcement Learning](http://web.stanford.edu/class/cs234/index.html)
- [15.097 at MIT, Prediction: Machine Learning and Statistics](http://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/)
- **2008**: [Graphical Models, Exponential Families, and Variational Inference, Wainwright and Jordan](https://people.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf)

---

<a name="blogs"></a>
## 15. Blogs
- [Deep Reinforcement Learning Doesn't Work Yet](https://www.alexirpan.com/2018/02/14/rl-hard.html)
- [Distill's Publication on Feature Visualization](https://distill.pub/2017/feature-visualization/)
- [Lil'Log: Blog on Machine Learning](https://lilianweng.github.io/lil-log/)

---

<a name="schools"></a>
## 16. Schools
- [Math Unipd - Vargiolu](https://www.math.unipd.it/~vargiolu/home/link.html)
- [School of Mathematics, UniPr](http://www.smi-math.unipr.it/perugia-2021/15/)
- [Machine Learning Schools](https://github.com/sshkhr/awesome-mlss)
- [Prairie Summer School](https://project.inria.fr/paiss/)

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