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Implementing Controlled Monte Carlo Diffusions (ICLR 2024)

CMCD 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Transport meets Variational Inference: Controlled Monte Carlo Diffusions

Official code repository for our ICLR 2024 paper [[OpenReview]](https://openreview.net/forum?id=PP1rudnxiW) [[arXiv]](https://arxiv.org/abs/2307.01050).

We provide code to run the experiments in the paper, on a wide variety of target distributions that have been implemented in `model_handler.py`. The code is written in Jax, and we use `wandb` for logging and visualisation.

To run different methods and targets, following the template below -

```python main.py --config.model log_ionosphere --config.boundmode MCD_ULA```

- ULA uses MCD_ULA
- MCD uses MCD_ULA_sn
- UHA uses UHA
- LDVI uses MCD_U_a-lp-sn
- CMCD uses MCD_CAIS_sn
- 2nd order CMCD uses MCD_CAIS_UHA_sn
- CMCD + VarGrad loss uses MCD_CAIS_var_sn

Below, we provide the commands replicating the exact hparam settings used in the paper, and the wandb links to the experiments.

#### 40-GMM Experiments

By default, in order to make comparisons to DDS/PIS, we use the same network architecture with time embeddings from the DDS repo. In order to run our method using the DDS architecture, you can set `--config.nn_arch dds` in the command line.

```bash
python main.py --config.model many_gmm --config.boundmode MCD_CAIS_sn --config.N 2000 --config.nbridges 256 --noconfig.pretrain_mfvi --config.init_sigma 60 --config.grad_clipping --config.init_eps 1 --config.eps_schedule cos_sq --config.lr 0.001 --noconfig.train_eps --noconfig.train_vi --config.wandb.name "kl 40gmm pis net eps=1, cos_sq" --config.nn_arch dds
```

```bash
python main.py --config.model many_gmm --config.boundmode MCD_CAIS_var_sn --config.N 2000 --config.nbridges 256 --noconfig.pretrain_mfvi --config.init_sigma 15 --config.grad_clipping --config.init_eps 0.65 --config.emb_dim 130 --config.lr 0.005 --noconfig.train_eps --noconfig.train_vi --config.wandb.name "logvar 40gmm"
```

```bash
python main.py --config.model many_gmm --config.boundmode MCD_CAIS_sn --config.N 2000 --config.nbridges 256 --noconfig.pretrain_mfvi --config.init_sigma 15 --config.grad_clipping --config.init_eps 0.1 --config.emb_dim 130 --config.lr 0.005 --noconfig.train_eps --noconfig.train_vi --config.wandb.name "kl 40gmm"
```

```bash
python main.py --config.model many_gmm --config.boundmode MCD_CAIS_sn --config.N 2000 --config.nbridges 256 --noconfig.pretrain_mfvi --config.init_sigma 60 --config.grad_clipping --config.init_eps 1 --config.eps_schedule cos_sq --config.lr 0.001 --noconfig.train_eps --noconfig.train_vi --config.wandb.name "kl 40gmm pis net eps=1, cos_sq" --config.nn_arch dds
```

[[Old KL Wandb experiment eps=0.65]](https://wandb.ai/shreyaspadhy/cais/runs/5z3rdxgh?workspace=user-shreyaspadhy)

[[Old KL Wandb experiment eps=0.1]](https://wandb.ai/shreyaspadhy/cais/runs/2rigzwcd?workspace=user-shreyaspadhy)

[[Old logvar Wandb experiment eps=0.65]](https://wandb.ai/shreyaspadhy/cais/runs/9o0ccmpv?workspace=user-shreyaspadhy)

[[Old logvar Wandb experiment eps=0.1]](https://wandb.ai/shreyaspadhy/cais/runs/236aqlcp?workspace=user-shreyaspadhy)

#### Funnel Experiments

```bash
python main.py --config.boundmode MCD_CAIS_sn --config.model funnel --config.N 300 --config.alpha 0.05 --config.emb_dim 48 --config.init_eps 0.1 -config.init_sigma 1 --config.iters 11000 --noconfig.pretrain_mfvi --config.train_vi --noconfig.train_eps --config.wandb.name "funnel replicate w/ cos_sq" --config.lr 0.01 --config.n_samples 2000 --config.eps_schedule cos_sq
```
[[Old wandb experiment with paper numbers]](https://wandb.ai/shreyaspadhy/cais/runs/kh9n0y3n/workspace?workspace=user-shreyaspadhy)
[[Replicated Wandb experiment at main]](https://wandb.ai/shreyaspadhy/final_cmcd/runs/wka879ae?workspace=user-shreyaspadhy)

The paper numbers differ in the following ways: (1) Uses Geffner's manual ADAM implementation.

#### LGCP Experiments

```
python main.py --config.boundmode MCD_CAIS_sn --config.model lgcp --config.N 20 --config.alpha 0.05 --config.emb_dim 20 --config.init_eps 0.00001 -config.init_sigma 1 --config.iters 37500 --config.pretrain_mfvi --config.train_vi --config.train_eps --config.wandb.name "lgcp replicate" --config.lr 0.0001 --config.n_samples 500 --config.mfvi_iters 20000
```
[[Old wandb experiment with paper numbers]](https://wandb.ai/shreyaspadhy/cais/runs/jemnkjp5/workspace?workspace=user-shreyaspadhy)
[[Wandb experiment at main]](https://wandb.ai/shreyaspadhy/final_cmcd/runs/325oa9q7?workspace=user-shreyaspadhy)

Differences from the paper experiments: (1) The new run is about 10min slower due to extra logging, (2) 20000 steps of MFVI is enough, vs 150k from the paper.

#### 2-GMM Experiments

```
python main.py --config.boundmode MCD_CAIS_sn --config.model gmm --config.N 300 --config.alpha 0.05 --config.emb_dim 20 --config.init_eps 0.01 -config.init_sigma 1 --config.iters 11000 --noconfig.pretrain_mfvi --config.train_vi --noconfig.train_eps --config.wandb.name "gmm replicate" --config.lr 0.001 --config.n_samples 500
```
[[Old wandb experiment with rebuttal paper numbers]](https://wandb.ai/shreyaspadhy/cais/runs/h9nwksr4/workspace?workspace=user-shreyaspadhy)
[[Wandb experiment]](https://wandb.ai/shreyaspadhy/final_cmcd/runs/1otzopu0?workspace=user-shreyaspadhy)

[[Original paper numbers]](https://wandb.ai/shreyaspadhy/cais/sweeps/n2exqhfq?workspace=user-shreyaspadhy)

Differences: (1) The new run has better $\ln Z$ estimates overall.

If you use any of our code or ideas, please cite our work using the following BibTeX entry:

```bibtex
@inproceedings{
vargas2024transport,
title={Transport meets Variational Inference: Controlled Monte Carlo Diffusions},
author={Francisco Vargas and Shreyas Padhy and Denis Blessing and Nikolas N{\"u}sken},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=PP1rudnxiW}
}
```

CMCD

CMCD 최신버젼 다운로드

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CMCD

CMCD 최신버젼 다운로드

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