12 citations · 26 across the 5 of their papers we have counts for
5 papers
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…
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Yexiang Xue, Zhiyuan Li, Stefano Ermon +2
Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of infe…
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…
Playing games against nature: optimal policies for renewable resource allocation
Stefano Ermon, Jon Conrad, Carla P. Gomes +1
In this paper we introduce a class of Markov decision processes that arise as a natural model for many renewable resource allocation problems. Upon extending results from the inven…