12 citations · 31 across the 7 of their papers we have counts for
5 papers · 1 filter
Extreme Q-Learning: MaxEnt RL without Entropy
Divyansh Garg, Joey Hejna, Matthieu Geist +1
Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possibl…
Modular Conformal Calibration
Charles Marx, Shengjia Zhao, Willie Neiswanger +1
Uncertainty estimates must be calibrated (i.e., accurate) and sharp (i.e., informative) in order to be useful. This has motivated a variety of methods for recalibration, which use…
A Unified Framework for Multi-distribution Density Ratio Estimation
Lantao Yu, Yujia Jin, Stefano Ermon
Binary density ratio estimation (DRE), the problem of estimating the ratio given their empirical samples, provides the foundation for many state-of-the-art machine learni…
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…
Estimating Uncertainty Online Against an Adversary
Volodymyr Kuleshov, Stefano Ermon
Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems. Existing uncertainty estimation techniques may fail when their m…