465 citations · 1.2k across the 32 of their papers we have counts for
7 papers · 2 filters
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
Chris Cundy, Aditya Grover, Stefano Ermon
A structural equation model (SEM) is an effective framework to reason over causal relationships represented via a directed acyclic graph (DAG). Recent advances have enabled effecti…
Frame Averaging for Invariant and Equivariant Network Design
Omri Puny, Matan Atzmon, Heli Ben-Hamu +4
Many machine learning tasks involve learning functions that are known to be invariant or equivariant to certain symmetries of the input data. However, it is often challenging to de…
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran +6
We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer ar…
Rotation Invariant Graph Neural Networks using Spin Convolutions
Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das +4
Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomic systems. Simulation techniques…
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data
Kourosh Hakhamaneshi, Pieter Abbeel, Vladimir Stojanovic +1
The goal of Multi-task Bayesian Optimization (MBO) is to minimize the number of queries required to accurately optimize a target black-box function, given access to offline evaluat…
Pretrained Transformers as Universal Computation Engines
Kevin Lu, Aditya Grover, Pieter Abbeel +1
We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning -- in particular, without finetuning of the…