Publications

Double Gumbel Q-Learning.

David Yu-Tung Hui

Aaron Courville

Pierre-Luc Bacon

openreview.net

On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes

Jia Lin Hau

Erick Delage

Mohammad Ghavamzadeh

Marek Petrik

openreview.net

DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets

Lazar Atanackovic

Alexander Tong

Jason Hartford

Leo J Lee

Bo Wang

Yoshua Bengio

openreview.net

Equivariant Adaptation of Large Pretrained Models

Arnab Kumar Mondal

Siba Smarak Panigrahi

Sékou-Oumar Kaba

Sai Rajeswar

Siamak Ravanbakhsh

Equivariant networks are specifically designed to ensure consistent behavior with respect to a set of input transformations, leading to high… (voir plus)er sample efficiency and more accurate and robust predictions. However, redesigning each component of prevalent deep neural network architectures to achieve chosen equivariance is a difficult problem and can result in a computationally expensive network during both training and inference. A recently proposed alternative towards equivariance that removes the architectural constraints is to use a simple canonicalization network that transforms the input to a canonical form before feeding it to an unconstrained prediction network. We show here that this approach can effectively be used to make a large pretrained network equivariant. However, we observe that the produced canonical orientations can be misaligned with those of the training distribution, hindering performance. Using dataset-dependent priors to inform the canonicalization function, we are able to make large pretrained models equivariant while maintaining their performance. This significantly improves the robustness of these models to deterministic transformations of the data, such as rotations. We believe this equivariant adaptation of large pretrained models can help their domain-specific applications with known symmetry priors.

openreview.net

For SALE: State-Action Representation Learning for Deep Reinforcement Learning

Scott Fujimoto

Wei-Di Chang

Edward J. Smith

Shixiang Shane Gu

Doina Precup

David Meger

In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked… (voir plus) for environments with low-level states, such as physical control problems. This paper introduces SALE, a novel approach for learning embeddings that model the nuanced interaction between state and action, enabling effective representation learning from low-level states. We extensively study the design space of these embeddings and highlight important design considerations. We integrate SALE and an adaptation of checkpoints for RL into TD3 to form the TD7 algorithm, which significantly outperforms existing continuous control algorithms. On OpenAI gym benchmark tasks, TD7 has an average performance gain of 276.7% and 50.7% over TD3 at 300k and 5M time steps, respectively, and works in both the online and offline settings.

openreview.net

GAUCHE: A Library for Gaussian Processes in Chemistry

Ryan-Rhys Griffiths

Leo Klarner

Henry Moss

Aditya Ravuri

Sang T. Truong

Bojana Rankovic

Samuel Don Stanton

Yuanqi Du

Arian Rokkum Jamasb

Gary Tom

Julius Schwartz

Austin Tripp

Aryan Deshwal

Gregory Kell

Anthony Bourached

Alex James Chan

Jacob Moss

Chengzhi Guo

Simon Frieder

Alpha Lee … (voir 8 de plus)

Philippe Schwaller

Jian Tang

Johannes P. Dürholt

Saudamini Chaurasia

Ji Won Park

Felix Strieth-Kalthoff

Bingqing Cheng

Alán Aspuru-Guzik

We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine… (voir plus) learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations however is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche

openreview.net

Group Robust Classification Without Any Group Information

Christos Tsirigotis

Joao Monteiro

Pau Rodriguez

David Vazquez

Aaron Courville

openreview.net

Guiding The Last Layer in Federated Learning with Pre-Trained Models

Gwen Legate

Nicolas Bernier

Lucas Caccia

Edouard Oyallon

Eugene Belilovsky

openreview.net

Importance-aware Co-teaching for Offline Model-based Optimization

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger

Xue (Steve) Liu

Offline model-based optimization aims to find a design that maximizes a property of interest using only an offline dataset, with application… (voir plus)s in robot, protein, and molecule design, among others. A prevalent approach is gradient ascent, where a proxy model is trained on the offline dataset and then used to optimize the design. This method suffers from an out-of-distribution issue, where the proxy is not accurate for unseen designs. To mitigate this issue, we explore using a pseudo-labeler to generate valuable data for fine-tuning the proxy. Specifically, we propose

2023-09-21

NeurIPS.cc/2023/Conference (poster)

openreview.net