255 citations · 2.4k across the 127 of their papers we have counts for
7 papers · 1 filter
Learning with a Strong Adversary
Ruitong Huang, Bing Xu, Dale Schuurmans +1
The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, \emph{learning with a strong adver…
Online Learning with Gaussian Payoffs and Side Observations
Yifan Wu, András György, Csaba Szepesvári
We consider a sequential learning problem with Gaussian payoffs and side information: after selecting an action , the learner receives information about the payoff of every acti…
Combinatorial Cascading Bandits
Branislav Kveton, Zheng Wen, Azin Ashkan +1
We propose combinatorial cascading bandits, a class of partial monitoring problems where at each step a learning agent chooses a tuple of ground items subject to constraints and re…
Fast Cross-Validation for Incremental Learning
Pooria Joulani, András György, Csaba Szepesvári
Cross-validation (CV) is one of the main tools for performance estimation and parameter tuning in machine learning. The general recipe for computing CV estimate is to run a learnin…
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
Daniel Hsu, Aryeh Kontorovich, Csaba Szepesvári
This article provides the first procedure for computing a fully data-dependent interval that traps the mixing time of a finite reversible ergodic Markov chain at a…
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
Prashanth L. A., Cheng Jie, Michael Fu +2
Cumulative prospect theory (CPT) is known to model human decisions well, with substantial empirical evidence supporting this claim. CPT works by distorting probabilities and is mor…