96 citations · 131 across the 8 of their papers we have counts for
8 papers
A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound
Gal Dalal, Balazs Szorenyi, Gugan Thoppe
Policy evaluation in reinforcement learning is often conducted using two-timescale stochastic approximation, which results in various gradient temporal difference methods such as G…
Optimal Learning of Mallows Block Model
Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi +1
The Mallows model, introduced in the seminal paper of Mallows 1957, is one of the most fundamental ranking distribution over the symmetric group . To analyze more complex rank…
Learning to Crawl
Utkarsh Upadhyay, Robert Busa-Fekete, Wojciech Kotlowski +2
Web crawling is the problem of keeping a cache of webpages fresh, i.e., having the most recent copy available when a page is requested. This problem is usually coupled with the nat…
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Alina Beygelzimer, Dávid Pál, Balázs Szörényi +3
We study the problem of efficient online multiclass linear classification with bandit feedback, where all examples belong to one of classes and lie in the -dimensional Eucli…
The information-theoretic value of unlabeled data in semi-supervised learning
Alexander Golovnev, Dávid Pál, Balázs Szörényi
We quantify the separation between the numbers of labeled examples required to learn in two settings: Settings with and without the knowledge of the distribution of the unlabeled d…
Multi-objective Bandits: Optimizing the Generalized Gini Index
Robert Busa-Fekete, Balazs Szorenyi, Paul Weng +1
We study the multi-armed bandit (MAB) problem where the agent receives a vectorial feedback that encodes many possibly competing objectives to be optimized. The goal of the agent i…
Finite Sample Analyses for TD(0) with Function Approximation
Gal Dalal, Balázs Szörényi, Gugan Thoppe +1
TD(0) is one of the most commonly used algorithms in reinforcement learning. Despite this, there is no existing finite sample analysis for TD(0) with function approximation, even f…
Distributed Clustering of Linear Bandits in Peer to Peer Networks
Nathan Korda, Balazs Szorenyi, Shuai Li
We provide two distributed confidence ball algorithms for solving linear bandit problems in peer to peer networks with limited communication capabilities. For the first, we assume…