7 accepted papers
We address the problem of learning a risk-sensitive policy based on the CVaR risk measure using distributional reinforcement learning. In particular, we show that the standard action-selection strategy when applying the distributional Bellman optimality operator can result in convergence to neither…
We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure feasibility with respect to constraints and risk-based objectives such as conditional value-at-risk…
Optimal transport is a framework for comparing measures whereby a cost is incurred for transporting one measure to another. Recent works have aimed to improve optimal transport plans through the introduction of various forms of structure. We introduce novel order constraints into the optimal transpo…
We present a system for document retrieval that combines direct classification with standard content-based retrieval approaches to significantly improve the relevance of the retrieved documents. Our system exploits the availability of an imperfect but sizable amount of labeled data from past queries…
The robust Markov decision processes (MDP) framework aims to address the problem of parameter uncertainty due to model mismatch, approximation errors or even adversarial behaviors. It is especially relevant when deploying the learned policies in real-world applications. Scaling up the robust MDP fra…
Advances in recovering low-rank matrices from noisy observations have led to tractable algorithms for clustering from general pairwise labels with provable performance guarantees. Based on convex relaxation, it has been shown that the ground truth clusters can be recovered with high probability unde…
We present a framework for biclustering and clustering where the observations are general labels. Our approach is based on the maximum likelihood estimator and its convex relaxation, and generalizes recent works in graph clustering to the biclustering setting. In addition to standard biclustering se…