Timezone: »
Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific click models. The click model is a model of how the user interacts with a list of documents. Though these results are significant, their impact on practice is limited, because all proposed algorithms are designed for specific click models and lack convergence guarantees in other models. In this work, we propose BatchRank, the first online learning to rank algorithm for a broad class of click models. The class encompasses two most fundamental click models, the cascade and position-based models. We derive a gap-dependent upper bound on the T-step regret of BatchRank and evaluate it on a range of web search queries. We observe that BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an existing algorithm for the cascade model.
Author Information
Masrour Zoghi (Independent Researcher)
Tomas Tunys (Czech Technical University)
Mohammad Ghavamzadeh (Adobe Research & INRIA)
Branislav Kveton (Adobe Research)
Csaba Szepesvari (University of Alberta)
Zheng Wen (Adobe Research)
Related Events (a corresponding poster, oral, or spotlight)
-
2017 Poster: Online Learning to Rank in Stochastic Click Models »
Mon. Aug 7th 08:30 AM -- 12:00 PM Room Gallery #30
More from the Same Authors
-
2023 Poster: Stochastic Gradient Succeeds for Bandits »
Jincheng Mei · Zixin Zhong · Bo Dai · Alekh Agarwal · Csaba Szepesvari · Dale Schuurmans -
2023 Poster: Revisiting Simple Regret: Fast Rates for Returning a Good Arm »
Yao Zhao · Connor J Stephens · Csaba Szepesvari · Kwang-Sung Jun -
2023 Poster: The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation »
Philip Amortila · Nan Jiang · Csaba Szepesvari -
2023 Poster: Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice »
Toshinori Kitamura · Tadashi Kozuno · Yunhao Tang · Nino Vieillard · Michal Valko · Wenhao Yang · Jincheng Mei · Pierre Menard · Mohammad Gheshlaghi Azar · Remi Munos · Olivier Pietquin · Matthieu Geist · Csaba Szepesvari · Wataru Kumagai · Yutaka Matsuo -
2021 Workshop: Workshop on Reinforcement Learning Theory »
Shipra Agrawal · Simon Du · Niao He · Csaba Szepesvari · Lin Yang -
2021 : RL Foundation Panel »
Matthew Botvinick · Thomas Dietterich · Leslie Kaelbling · John Langford · Warrren B Powell · Csaba Szepesvari · Lihong Li · Yuxi Li -
2021 Workshop: Reinforcement Learning for Real Life »
Yuxi Li · Minmin Chen · Omer Gottesman · Lihong Li · Zongqing Lu · Rupam Mahmood · Niranjani Prasad · Zhiwei (Tony) Qin · Csaba Szepesvari · Matthew Taylor -
2021 Poster: Meta-Thompson Sampling »
Branislav Kveton · Mikhail Konobeev · Manzil Zaheer · Chih-wei Hsu · Martin Mladenov · Craig Boutilier · Csaba Szepesvari -
2021 Spotlight: Meta-Thompson Sampling »
Branislav Kveton · Mikhail Konobeev · Manzil Zaheer · Chih-wei Hsu · Martin Mladenov · Craig Boutilier · Csaba Szepesvari -
2021 Poster: Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient »
Botao Hao · Yaqi Duan · Tor Lattimore · Csaba Szepesvari · Mengdi Wang -
2021 Poster: Improved Regret Bound and Experience Replay in Regularized Policy Iteration »
Nevena Lazic · Dong Yin · Yasin Abbasi-Yadkori · Csaba Szepesvari -
2021 Poster: Leveraging Non-uniformity in First-order Non-convex Optimization »
Jincheng Mei · Yue Gao · Bo Dai · Csaba Szepesvari · Dale Schuurmans -
2021 Poster: A Distribution-dependent Analysis of Meta Learning »
Mikhail Konobeev · Ilja Kuzborskij · Csaba Szepesvari -
2021 Oral: Improved Regret Bound and Experience Replay in Regularized Policy Iteration »
Nevena Lazic · Dong Yin · Yasin Abbasi-Yadkori · Csaba Szepesvari -
2021 Spotlight: Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient »
Botao Hao · Yaqi Duan · Tor Lattimore · Csaba Szepesvari · Mengdi Wang -
2021 Spotlight: Leveraging Non-uniformity in First-order Non-convex Optimization »
Jincheng Mei · Yue Gao · Bo Dai · Csaba Szepesvari · Dale Schuurmans -
2021 Spotlight: A Distribution-dependent Analysis of Meta Learning »
Mikhail Konobeev · Ilja Kuzborskij · Csaba Szepesvari -
2021 Poster: Bootstrapping Fitted Q-Evaluation for Off-Policy Inference »
Botao Hao · Xiang Ji · Yaqi Duan · Hao Lu · Csaba Szepesvari · Mengdi Wang -
2021 Spotlight: Bootstrapping Fitted Q-Evaluation for Off-Policy Inference »
Botao Hao · Xiang Ji · Yaqi Duan · Hao Lu · Csaba Szepesvari · Mengdi Wang -
2021 Town Hall: Town Hall »
John Langford · Marina Meila · Tong Zhang · Le Song · Stefanie Jegelka · Csaba Szepesvari -
2021 Poster: On the Optimality of Batch Policy Optimization Algorithms »
Chenjun Xiao · Yifan Wu · Jincheng Mei · Bo Dai · Tor Lattimore · Lihong Li · Csaba Szepesvari · Dale Schuurmans -
2021 Spotlight: On the Optimality of Batch Policy Optimization Algorithms »
Chenjun Xiao · Yifan Wu · Jincheng Mei · Bo Dai · Tor Lattimore · Lihong Li · Csaba Szepesvari · Dale Schuurmans -
2020 : Efficient Planning in Large MDPs with Weak Linear Function Approximation - Csaba Szepesvari »
Csaba Szepesvari -
2020 : Speaker Panel »
Csaba Szepesvari · Martha White · Sham Kakade · Gergely Neu · Shipra Agrawal · Akshay Krishnamurthy -
2020 Poster: On the Global Convergence Rates of Softmax Policy Gradient Methods »
Jincheng Mei · Chenjun Xiao · Csaba Szepesvari · Dale Schuurmans -
2020 Poster: Model-Based Reinforcement Learning with Value-Targeted Regression »
Alex Ayoub · Zeyu Jia · Csaba Szepesvari · Mengdi Wang · Lin Yang -
2020 Poster: Learning with Good Feature Representations in Bandits and in RL with a Generative Model »
Tor Lattimore · Csaba Szepesvari · Gellért Weisz -
2020 Poster: A simpler approach to accelerated optimization: iterative averaging meets optimism »
Pooria Joulani · Anant Raj · Andras Gyorgy · Csaba Szepesvari -
2019 Workshop: Reinforcement Learning for Real Life »
Yuxi Li · Alborz Geramifard · Lihong Li · Csaba Szepesvari · Tao Wang -
2019 Poster: POLITEX: Regret Bounds for Policy Iteration using Expert Prediction »
Yasin Abbasi-Yadkori · Peter Bartlett · Kush Bhatia · Nevena Lazic · Csaba Szepesvari · Gellért Weisz -
2019 Oral: POLITEX: Regret Bounds for Policy Iteration using Expert Prediction »
Yasin Abbasi-Yadkori · Peter Bartlett · Kush Bhatia · Nevena Lazic · Csaba Szepesvari · Gellért Weisz -
2019 Poster: Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits »
Branislav Kveton · Csaba Szepesvari · Sharan Vaswani · Zheng Wen · Tor Lattimore · Mohammad Ghavamzadeh -
2019 Poster: Online Learning to Rank with Features »
Shuai Li · Tor Lattimore · Csaba Szepesvari -
2019 Oral: Online Learning to Rank with Features »
Shuai Li · Tor Lattimore · Csaba Szepesvari -
2019 Oral: Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits »
Branislav Kveton · Csaba Szepesvari · Sharan Vaswani · Zheng Wen · Tor Lattimore · Mohammad Ghavamzadeh -
2019 Poster: CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration »
Gellért Weisz · Andras Gyorgy · Csaba Szepesvari -
2019 Oral: CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration »
Gellért Weisz · Andras Gyorgy · Csaba Szepesvari -
2018 Poster: Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers »
Yao Ma · Alex Olshevsky · Csaba Szepesvari · Venkatesh Saligrama -
2018 Oral: Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers »
Yao Ma · Alex Olshevsky · Csaba Szepesvari · Venkatesh Saligrama -
2018 Poster: Bandits with Delayed, Aggregated Anonymous Feedback »
Ciara Pike-Burke · Shipra Agrawal · Csaba Szepesvari · Steffen Grünewälder -
2018 Oral: Bandits with Delayed, Aggregated Anonymous Feedback »
Ciara Pike-Burke · Shipra Agrawal · Csaba Szepesvari · Steffen Grünewälder -
2018 Poster: LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration »
Gellért Weisz · Andras Gyorgy · Csaba Szepesvari -
2018 Oral: LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration »
Gellért Weisz · Andras Gyorgy · Csaba Szepesvari -
2017 Poster: Active Learning for Accurate Estimation of Linear Models »
Carlos Riquelme Ruiz · Mohammad Ghavamzadeh · Alessandro Lazaric -
2017 Poster: Model-Independent Online Learning for Influence Maximization »
Sharan Vaswani · Branislav Kveton · Zheng Wen · Mohammad Ghavamzadeh · Laks V.S Lakshmanan · Mark Schmidt -
2017 Poster: Bottleneck Conditional Density Estimation »
Rui Shu · Hung Bui · Mohammad Ghavamzadeh -
2017 Talk: Active Learning for Accurate Estimation of Linear Models »
Carlos Riquelme Ruiz · Mohammad Ghavamzadeh · Alessandro Lazaric -
2017 Talk: Bottleneck Conditional Density Estimation »
Rui Shu · Hung Bui · Mohammad Ghavamzadeh -
2017 Talk: Model-Independent Online Learning for Influence Maximization »
Sharan Vaswani · Branislav Kveton · Zheng Wen · Mohammad Ghavamzadeh · Laks V.S Lakshmanan · Mark Schmidt