66 citations · 104 across the 9 of their papers we have counts for
9 papers
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking
Chris Cundy, Stefano Ermon
In many domains, autoregressive models can attain high likelihood on the task of predicting the next observation. However, this maximum-likelihood (MLE) objective does not necessar…
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
Gengchen Mai, Weiming Huang, Jin Sun +11
Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by…
LMPriors: Pre-Trained Language Models as Task-Specific Priors
Kristy Choi, Chris Cundy, Sanjari Srivastava +1
Particularly in low-data regimes, an outstanding challenge in machine learning is developing principled techniques for augmenting our models with suitable priors. This is to encour…
Beyond Bayes-optimality: meta-learning what you know you don't know
Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch +11
Meta-training agents with memory has been shown to culminate in Bayes-optimal agents, which casts Bayes-optimality as the implicit solution to a numerical optimization problem rath…
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…
IQ-Learn: Inverse soft-Q Learning for Imitation
Divyansh Garg, Shuvam Chakraborty, Chris Cundy +3
In many sequential decision-making problems (e.g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the…
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients
Chris Cundy, Rishi Desai, Stefano Ermon
As reinforcement learning techniques are increasingly applied to real-world decision problems, attention has turned to how these algorithms use potentially sensitive information. W…
Exploring Hierarchy-Aware Inverse Reinforcement Learning
Chris Cundy, Daniel Filan
We introduce a new generative model for human planning under the Bayesian Inverse Reinforcement Learning (BIRL) framework which takes into account the fact that humans often plan u…
Parallelizing Linear Recurrent Neural Nets Over Sequence Length
Eric Martin, Chris Cundy
Recurrent neural networks (RNNs) are widely used to model sequential data but their non-linear dependencies between sequence elements prevent parallelizing training over sequence l…