Csaba Szepesvari

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Google ∙ University of Alberta

Research Scientist at DeepMind, Professor at University of Alberta, Principal Investigator at Alberta Machine Intelligence Institute (Amii)

Differentiable Bandit Exploration

We learn bandit policies that maximize the average reward over bandit in...
02/17/2020 ∙ by Craig Boutilier, et al. ∙ 22 ∙ share

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Online Algorithm for Unsupervised Sensor Selection

In many security and healthcare systems, the detection and diagnosis sys...
01/15/2019 ∙ by Arun Verma, et al. ∙ 18 ∙ share

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Provably Efficient Adaptive Approximate Policy Iteration

Model-free reinforcement learning algorithms combined with value functio...
02/08/2020 ∙ by Botao Hao, et al. ∙ 15 ∙ share

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Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers

We consider worker skill estimation for the single-coin Dawid-Skene crow...
04/25/2019 ∙ by Yao Ma, et al. ∙ 12 ∙ share

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Learning with Good Feature Representations in Bandits and in RL with a Generative Model

The construction in the recent paper by Du et al. [2019] implies that se...
11/18/2019 ∙ by Tor Lattimore, et al. ∙ 11 ∙ share

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Detecting Overfitting via Adversarial Examples

The repeated reuse of test sets in popular benchmark problems raises dou...
03/06/2019 ∙ by Roman Werpachowski, et al. ∙ 10 ∙ share

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Perturbed-History Exploration in Stochastic Linear Bandits

We propose a new online algorithm for minimizing the cumulative regret i...
03/21/2019 ∙ by Branislav Kveton, et al. ∙ 10 ∙ share

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Autonomous exploration for navigating in non-stationary CMPs

We consider a setting in which the objective is to learn to navigate in ...
10/18/2019 ∙ by Pratik Gajane, et al. ∙ 10 ∙ share

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Online Learning to Rank with Features

We introduce a new model for online ranking in which the click probabili...
10/05/2018 ∙ by Shuai Li, et al. ∙ 8 ∙ share

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Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits

We propose a multi-armed bandit algorithm that explores based on randomi...
11/13/2018 ∙ by Branislav Kveton, et al. ∙ 6 ∙ share

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Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures

This paper addresses the problem of evaluating learning systems in safet...
12/04/2018 ∙ by Jonathan Uesato, et al. ∙ 6 ∙ share

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An Information-Theoretic Approach to Minimax Regret in Partial Monitoring

We prove a new minimax theorem connecting the worst-case Bayesian regret...
02/01/2019 ∙ by Tor Lattimore, et al. ∙ 6 ∙ share

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Empirical Bayes Regret Minimization

The prevalent approach to bandit algorithm design is to have a low-regre...
04/04/2019 ∙ by Chih-Wei Hsu, et al. ∙ 6 ∙ share

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Perturbed-History Exploration in Stochastic Multi-Armed Bandits

We propose an online algorithm for cumulative regret minimization in a s...
02/26/2019 ∙ by Branislav Kveton, et al. ∙ 4 ∙ share

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PAC-Bayes with Backprop

We explore a method to train probabilistic neural networks by minimizing...
08/19/2019 ∙ by Omar Rivasplata, et al. ∙ 4 ∙ share

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On the Global Convergence Rates of Softmax Policy Gradient Methods

We make three contributions toward better understanding policy gradient ...
05/13/2020 ∙ by Jincheng Mei, et al. ∙ 4 ∙ share

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TopRank: A practical algorithm for online stochastic ranking

Online learning to rank is a sequential decision-making problem where in...
06/06/2018 ∙ by Tor Lattimore, et al. ∙ 2 ∙ share

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BubbleRank: Safe Online Learning to Rerank

We study the problem of online learning to re-rank, where users provide ...
06/15/2018 ∙ by Branislav Kveton, et al. ∙ 2 ∙ share

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PAC-Bayes bounds for stable algorithms with instance-dependent priors

PAC-Bayes bounds have been proposed to get risk estimates based on a tra...
06/18/2018 ∙ by Omar Rivasplata, et al. ∙ 2 ∙ share

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Randomized Exploration in Generalized Linear Bandits

We study two randomized algorithms for generalized linear bandits, GLM-T...
06/21/2019 ∙ by Branislav Kveton, et al. ∙ 2 ∙ share

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Exploration by Optimisation in Partial Monitoring

We provide a simple and efficient algorithm for adversarial k-action d-o...
07/12/2019 ∙ by Tor Lattimore, et al. ∙ 1 ∙ share

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Exploration-Enhanced POLITEX

We study algorithms for average-cost reinforcement learning problems wit...
08/27/2019 ∙ by Yasin Abbasi-Yadkori, et al. ∙ 1 ∙ share

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Bandits with Delayed Anonymous Feedback

We study the bandits with delayed anonymous feedback problem, a variant ...
09/20/2017 ∙ by Ciara Pike-Burke, et al. ∙ 0 ∙ share

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Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging

We consider d-dimensional linear stochastic approximation algorithms (LS...
09/12/2017 ∙ by Chandrashekar Lakshminarayanan, et al. ∙ 0 ∙ share

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A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds

Recently, much work has been done on extending the scope of online learn...
09/08/2017 ∙ by Pooria Joulani, et al. ∙ 0 ∙ share

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Structured Best Arm Identification with Fixed Confidence

We study the problem of identifying the best action among a set of possi...
06/16/2017 ∙ by Ruitong Huang, et al. ∙ 0 ∙ share

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Mixing time estimation in reversible Markov chains from a single sample path

The spectral gap γ of a finite, ergodic, and reversible Markov chain is ...
08/24/2017 ∙ by Daniel Hsu, et al. ∙ 0 ∙ share

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An a Priori Exponential Tail Bound for k-Folds Cross-Validation

We consider a priori generalization bounds developed in terms of cross-v...
06/19/2017 ∙ by Karim Abou-Moustafa, et al. ∙ 0 ∙ share

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Bernoulli Rank-1 Bandits for Click Feedback

The probability that a user will click a search result depends both on i...
03/19/2017 ∙ by Sumeet Katariya, et al. ∙ 0 ∙ share

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Online Learning to Rank in Stochastic Click Models

Online learning to rank is a core problem in information retrieval and m...
03/07/2017 ∙ by Masrour Zoghi, et al. ∙ 0 ∙ share

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The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits

Stochastic linear bandits are a natural and simple generalisation of fin...
10/14/2016 ∙ by Tor Lattimore, et al. ∙ 0 ∙ share

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(Bandit) Convex Optimization with Biased Noisy Gradient Oracles

Algorithms for bandit convex optimization and online learning often rely...
09/22/2016 ∙ by Xiaowei Hu, et al. ∙ 0 ∙ share

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Multiclass Classification Calibration Functions

In this paper we refine the process of computing calibration functions f...
09/20/2016 ∙ by Bernardo Ávila Pires, et al. ∙ 0 ∙ share

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Chaining Bounds for Empirical Risk Minimization

This paper extends the standard chaining technique to prove excess risk ...
09/07/2016 ∙ by Gábor Balázs, et al. ∙ 0 ∙ share

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Stochastic Rank-1 Bandits

We propose stochastic rank-1 bandits, a class of online learning problem...
08/10/2016 ∙ by Sumeet Katariya, et al. ∙ 0 ∙ share

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On Minimax Optimal Offline Policy Evaluation

This paper studies the off-policy evaluation problem, where one aims to ...
09/12/2014 ∙ by Lihong Li, et al. ∙ 0 ∙ share

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Adaptive Monte Carlo via Bandit Allocation

We consider the problem of sequentially choosing between a set of unbias...
05/13/2014 ∙ by James Neufeld, et al. ∙ 0 ∙ share

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Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models

In this paper we study a model-based approach to calculating approximate...
02/19/2016 ∙ by Bernardo Ávila Pires, et al. ∙ 0 ∙ share

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Conservative Bandits

We study a novel multi-armed bandit problem that models the challenge fa...
02/13/2016 ∙ by Yifan Wu, et al. ∙ 0 ∙ share

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DCM Bandits: Learning to Rank with Multiple Clicks

A search engine recommends to the user a list of web pages. The user exa...
02/09/2016 ∙ by Sumeet Katariya, et al. ∙ 0 ∙ share

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Online Learning with Gaussian Payoffs and Side Observations

We consider a sequential learning problem with Gaussian payoffs and side...
10/27/2015 ∙ by Yifan Wu, et al. ∙ 0 ∙ share

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Fast Cross-Validation for Incremental Learning

Cross-validation (CV) is one of the main tools for performance estimatio...
06/30/2015 ∙ by Pooria Joulani, et al. ∙ 0 ∙ share

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Cascading Bandits: Learning to Rank in the Cascade Model

A search engine usually outputs a list of K web pages. The user examines...
02/10/2015 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits

A stochastic combinatorial semi-bandit is an online learning problem whe...
10/03/2014 ∙ by Branislav Kveton, et al. ∙ 0 ∙ share

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Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping

We consider the problem of efficiently learning optimal control policies...
06/13/2012 ∙ by Richard S. Sutton, et al. ∙ 0 ∙ share

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Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstractions

In this paper, we consider planning in stochastic shortest path (SSP) pr...
06/13/2012 ∙ by Alejandro Isaza, et al. ∙ 0 ∙ share

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Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems

The analysis of online least squares estimation is at the heart of many ...
02/14/2011 ∙ by Yasin Abbasi-Yadkori, et al. ∙ 0 ∙ share

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Online Learning under Delayed Feedback

Online learning with delayed feedback has received increasing attention ...
06/04/2013 ∙ by Pooria Joulani, et al. ∙ 0 ∙ share

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Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions

We study the problem of learning Markov decision processes with finite s...
03/12/2013 ∙ by Yasin Abbasi-Yadkori, et al. ∙ 0 ∙ share

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Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs

We present simple and computationally efficient nonparametric estimators...
03/09/2010 ∙ by David Pal, et al. ∙ 0 ∙ share

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Csaba Szepesvari

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