5 papers
Offline Materials Optimization with CliqueFlowmer
Jakub Grudzien Kuba, Benjamin Kurt Miller, Sergey Levine +1
Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials…
Cliqueformer: Model-Based Optimization with Structured Transformers
Jakub Grudzien Kuba, Pieter Abbeel, Sergey Levine
Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based…
Language Self-Play For Data-Free Training
Jakub Grudzien Kuba, Mengting Gu, Qi Ma +3
Large language models (LLMs) have advanced rapidly in recent years, driven by scale, abundant high-quality training data, and reinforcement learning. Yet this progress faces a fund…
Mirror Learning: A Unifying Framework of Policy Optimisation
Jakub Grudzien Kuba, Christian Schroeder de Witt, Jakob Foerster
Modern deep reinforcement learning (RL) algorithms are motivated by either the generalised policy iteration (GPI) or trust-region learning (TRL) frameworks. However, algorithms tha…
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel +1
While machine learning models are typically trained to solve prediction problems, we might often want to use them for optimization problems. For example, given a dataset of protein…