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Gradient Estimation with Discrete Stein Operators (NeurIPS 2022)

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# Gradient Estimation with Discrete Stein Operators

Code for reproducing the results in "Gradient Estimation with Discrete Stein Operators" \
https://arxiv.org/abs/2202.09497 \
**NeurIPS 2022 Outstanding Paper Award**

### Variance of Gradient Estimates for Training VAEs
#### Bernoulli likelihoods
<img src="results/var_dyn.png" width="900">

#### Gaussian likelihoods
<img src="results/var_cont.png" width="900">

## Dependencies
```
tensorflow >= 2.5.0
tensorflow-datasets >= 4.2.0
tensorflow-probability >= 0.12.2
scipy >= 1.6.3
absl >= 0.12.0
pandas >= 1.2.4
numpy >= 1.19.5
tqdm >= 4.60.0
```

## Usage
Running VAE experiments:
```
python experiment_launcher_singlelayer.py --dataset={dataset} --genmo_lr={lr} --infnet_lr={lr} --encoder_type=nonlinear --grad_type={grad_type} --K={K} --D=200 --seed={seed}
```

- `dataset`: 
  - dynamically binarized: `mnist`, `fashion_mnist`, `omniglot`.
  - non-binarized: `continuous_mnist`, `continuous_fashion`, `continuous_omniglot`.
- `lr`: 
  - dynamically binarized: `1e-3` for `mnist` and `omniglot`, `3e-4` for `fashion_mnist`.
  - non-binarized: `1e-4`.
- `grad_type`: 
  - REINFORCE leave-one-out: `reinforce_loo`
  - DisARM (Dong et al., 2020): `disarm`
  - Double CV (Titsias & Shi, 2021): `double_cv`
  - RELAX (Grathwohl et al., 2017): `relax` (not affected by `K`, always using 3 evaluations of `f`)
  - ARMS (Dimitriev & Zhou, 2020): `arms`
  - RODEO (ours): 
    - `K=2`: `discrete_stein_avg`
    - `K>2`: `discrete_stein_output_avg`
- `K`: number of samples used, equivalent to number of evaluations of `f` in gradient estimators except RELAX.
- `seed`: 1-5.

Running hierarchical VAE experiments:
```
python experiment_launcher_multilayer.py --dataset={dataset} --genmo_lr={lr} --infnet_lr={lr} --grad_type={grad_type} --K=2 --seed={seed}
```

## Citation

To cite this work, please use
```
@inproceedings{NEURIPS2022_a5a5b0ff,
 author = {Shi, Jiaxin and Zhou, Yuhao and Hwang, Jessica and Titsias, Michalis and Mackey, Lester},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {25829--25841},
 publisher = {Curran Associates, Inc.},
 title = {Gradient Estimation with Discrete Stein Operators},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/a5a5b0ff87c59172a13342d428b1e033-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}
```

## Acknowledgement

The code is modified from Double CV (https://github.com/thjashin/double-cv), originally based on DisARM (https://github.com/google-research/google-research/tree/master/disarm/binary) and ARMS (https://github.com/alekdimi/arms)

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