default search action
Branislav Kveton
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j6]Behnam Rahdari, Peter Brusilovsky, Branislav Kveton:
Towards Simulation-Based Evaluation of Recommender Systems with Carousel Interfaces. Trans. Recomm. Syst. 2(1): 9:1-9:25 (2024) - [c105]Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu:
Pessimistic Off-Policy Multi-Objective Optimization. AISTATS 2024: 2980-2988 - [c104]Matej Cief, Branislav Kveton, Michal Kompan:
Pessimistic Off-Policy Optimization for Learning to Rank. ECAI 2024: 1896-1903 - [c103]Aadirupa Saha, Branislav Kveton:
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling. ICLR 2024 - [c102]Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach, Branislav Kveton, Volkan Cevher:
MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent. ICML 2024 - [c101]Ziqian Lin, Hao Ding, Trong Nghia Hoang, Branislav Kveton, Anoop Deoras, Hao Wang:
Pre-trained Recommender Systems: A Causal Debiasing Perspective. WSDM 2024: 424-433 - [c100]Behnam Rahdari, Hao Ding, Ziwei Fan, Yifei Ma, Zhuotong Chen, Anoop Deoras, Branislav Kveton:
Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs. WSDM 2024: 1078-1081 - [i85]Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach, Branislav Kveton, Volkan Cevher:
MADA: Meta-Adaptive Optimizers through hyper-gradient Descent. CoRR abs/2401.08893 (2024) - [i84]Subhojyoti Mukherjee, Ge Liu, Aniket Deshmukh, Anusha Lalitha, Yifei Ma, Branislav Kveton:
Experimental Design for Active Transductive Inference in Large Language Models. CoRR abs/2404.08846 (2024) - [i83]Subhojyoti Mukherjee, Anusha Lalitha, Kousha Kalantari, Aniket Deshmukh, Ge Liu, Yifei Ma, Branislav Kveton:
Optimal Design for Human Feedback. CoRR abs/2404.13895 (2024) - [i82]Matej Cief, Branislav Kveton, Michal Kompan:
Cross-Validated Off-Policy Evaluation. CoRR abs/2405.15332 (2024) - [i81]Aniruddha Bhargava, Lalit Jain, Branislav Kveton, Ge Liu, Subhojyoti Mukherjee:
Off-Policy Evaluation from Logged Human Feedback. CoRR abs/2406.10030 (2024) - [i80]Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song:
Online Posterior Sampling with a Diffusion Prior. CoRR abs/2410.03919 (2024) - [i79]Junda Wu, Xintong Li, Ruoyu Wang, Yu Xia, Yuxin Xiong, Jianing Wang, Tong Yu, Xiang Chen, Branislav Kveton, Lina Yao, Jingbo Shang, Julian J. McAuley:
OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models. CoRR abs/2410.23703 (2024) - [i78]Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, Ruiyi Zhang, Jiuxiang Gu, Tyler Derr, Hongjie Chen, Junda Wu, Xiang Chen, Zichao Wang, Subrata Mitra, Nedim Lipka, Nesreen Ahmed, Yu Wang:
Personalization of Large Language Models: A Survey. CoRR abs/2411.00027 (2024) - 2023
- [c99]Mohammad Javad Azizi, Branislav Kveton, Mohammad Ghavamzadeh, Sumeet Katariya:
Meta-Learning for Simple Regret Minimization. AAAI 2023: 6709-6717 - [c98]Imad Aouali, Branislav Kveton, Sumeet Katariya:
Mixed-Effect Thompson Sampling. AISTATS 2023: 2087-2115 - [c97]Branislav Kveton, Yi Liu, Johan Matteo Kruijssen, Yisu Nie:
Non-Compliant Bandits. CIKM 2023: 1138-1147 - [c96]Joey Hong, Branislav Kveton, Manzil Zaheer, Sumeet Katariya, Mohammad Ghavamzadeh:
Multi-Task Off-Policy Learning from Bandit Feedback. ICML 2023: 13157-13173 - [c95]Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton, Patrick Blöbaum:
Thompson Sampling with Diffusion Generative Prior. ICML 2023: 13434-13468 - [c94]Runzhe Wan, Haoyu Wei, Branislav Kveton, Rui Song:
Multiplier Bootstrap-based Exploration. ICML 2023: 35444-35490 - [c93]Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi:
Logarithmic Bayes Regret Bounds. NeurIPS 2023 - [c92]Hao Ding, Branislav Kveton, Yifei Ma, Youngsuk Park, Venkataramana Kini, Yupeng Gu, Ravi Divvela, Fei Wang, Anoop Deoras, Hao Wang:
Trending Now: Modeling Trend Recommendations. RecSys 2023: 294-305 - [c91]Tesi Xiao, Branislav Kveton, Sumeet Katariya, Tanmay Gangwani, Anshuka Rangi:
Towards Sequential Counterfactual Learning to Rank. SIGIR-AP 2023: 122-128 - [c90]Anusha Lalitha, Kousha Kalantari, Yifei Ma, Anoop Deoras, Branislav Kveton:
Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances. UAI 2023: 1164-1173 - [i77]Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton, Patrick Blöbaum:
Thompson Sampling with Diffusion Generative Prior. CoRR abs/2301.05182 (2023) - [i76]Sanath Kumar Krishnamurthy, Tanmay Gangwani, Sumeet Katariya, Branislav Kveton, Anshuka Rangi:
Selective Uncertainty Propagation in Offline RL. CoRR abs/2302.00284 (2023) - [i75]Runzhe Wan, Haoyu Wei, Branislav Kveton, Rui Song:
Multiplier Bootstrap-based Exploration. CoRR abs/2302.01543 (2023) - [i74]Aadirupa Saha, Branislav Kveton:
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling. CoRR abs/2303.09033 (2023) - [i73]Anusha Lalitha, Kousha Kalantari, Yifei Ma, Anoop Deoras, Branislav Kveton:
Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances. CoRR abs/2306.07549 (2023) - [i72]Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi:
Logarithmic Bayes Regret Bounds. CoRR abs/2306.09136 (2023) - [i71]Subhojyoti Mukherjee, Ruihao Zhu, Branislav Kveton:
Efficient and Interpretable Bandit Algorithms. CoRR abs/2310.14751 (2023) - [i70]Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu:
Pessimistic Off-Policy Multi-Objective Optimization. CoRR abs/2310.18617 (2023) - [i69]Ziqian Lin, Hao Ding, Nghia Hoang, Branislav Kveton, Anoop Deoras, Hao Wang:
Pre-trained Recommender Systems: A Causal Debiasing Perspective. CoRR abs/2310.19251 (2023) - [i68]Behnam Rahdari, Hao Ding, Ziwei Fan, Yifei Ma, Zhuotong Chen, Anoop Deoras, Branislav Kveton:
Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs. CoRR abs/2312.14345 (2023) - 2022
- [j5]Branislav Kveton, Muhammad Jehangir Amjad, Christophe Diot, Dimitris Konomis, Augustin Soule, Xiaolong Yang:
Optimal probing with statistical guarantees for network monitoring at scale. Comput. Commun. 192: 119-131 (2022) - [c89]Ruihao Zhu, Branislav Kveton:
Safe Optimal Design with Applications in Off-Policy Learning. AISTATS 2022: 2436-2447 - [c88]Rong Zhu, Branislav Kveton:
Random Effect Bandits. AISTATS 2022: 3091-3107 - [c87]Branislav Kveton, Ofer Meshi, Masrour Zoghi, Zhen Qin:
On the Value of Prior in Online Learning to Rank. AISTATS 2022: 6880-6892 - [c86]Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh, Craig Boutilier:
Thompson Sampling with a Mixture Prior. AISTATS 2022: 7565-7586 - [c85]Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh:
Hierarchical Bayesian Bandits. AISTATS 2022: 7724-7741 - [c84]Behnam Rahdari, Peter Brusilovsky, Branislav Kveton:
Towards Increasing the Coverage of Interactive Recommendations. FLAIRS 2022 - [c83]Behnam Rahdari, Branislav Kveton, Peter Brusilovsky:
The Magic of Carousels: Single vs. Multi-List Recommender Systems. HT 2022: 166-174 - [c82]Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh:
Deep Hierarchy in Bandits. ICML 2022: 8833-8851 - [c81]Runzhe Wan, Branislav Kveton, Rui Song:
Safe Exploration for Efficient Policy Evaluation and Comparison. ICML 2022: 22491-22511 - [c80]Mohammad Javad Azizi, Branislav Kveton, Mohammad Ghavamzadeh:
Fixed-Budget Best-Arm Identification in Structured Bandits. IJCAI 2022: 2798-2804 - [c79]Nan Wang, Hongning Wang, Maryam Karimzadehgan, Branislav Kveton, Craig Boutilier:
IMO^3: Interactive Multi-Objective Off-Policy Optimization. IJCAI 2022: 3523-3529 - [c78]Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton:
Uplifting Bandits. NeurIPS 2022 - [i67]Nan Wang, Hongning Wang, Maryam Karimzadehgan, Branislav Kveton, Craig Boutilier:
IMO3: Interactive Multi-Objective Off-Policy Optimization. CoRR abs/2201.09798 (2022) - [i66]Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh:
Deep Hierarchy in Bandits. CoRR abs/2202.01454 (2022) - [i65]Mohammad Javad Azizi, Branislav Kveton, Mohammad Ghavamzadeh, Sumeet Katariya:
Meta-Learning for Simple Regret Minimization. CoRR abs/2202.12888 (2022) - [i64]Runzhe Wan, Branislav Kveton, Rui Song:
Safe Exploration for Efficient Policy Evaluation and Comparison. CoRR abs/2202.13234 (2022) - [i63]Imad Aouali, Branislav Kveton, Sumeet Katariya:
Generalizing Hierarchical Bayesian Bandits. CoRR abs/2205.15124 (2022) - [i62]Matej Cief, Branislav Kveton, Michal Kompan:
Pessimistic Off-Policy Optimization for Learning to Rank. CoRR abs/2206.02593 (2022) - [i61]Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton:
Uplifting Bandits. CoRR abs/2206.04091 (2022) - [i60]Behnam Rahdari, Branislav Kveton, Peter Brusilovsky:
From Ranked Lists to Carousels: A Carousel Click Model. CoRR abs/2209.13426 (2022) - [i59]Rong Zhu, Branislav Kveton:
Robust Contextual Linear Bandits. CoRR abs/2210.14483 (2022) - [i58]Alexia Atsidakou, Sumeet Katariya, Sujay Sanghavi, Branislav Kveton:
Bayesian Fixed-Budget Best-Arm Identification. CoRR abs/2211.08572 (2022) - [i57]Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh:
Multi-Task Off-Policy Learning from Bandit Feedback. CoRR abs/2212.04720 (2022) - 2021
- [c77]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed:
Non-Stationary Off-Policy Optimization. AISTATS 2021: 2494-2502 - [c76]Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári:
Meta-Thompson Sampling. ICML 2021: 5884-5893 - [c75]Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvári:
No Regrets for Learning the Prior in Bandits. NeurIPS 2021: 28029-28041 - [c74]Nan Wang, Branislav Kveton, Maryam Karimzadehgan:
CORe: Capitalizing On Rewards in Bandit Exploration. UAI 2021: 1968-1978 - [i56]Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári:
Meta-Thompson Sampling. CoRR abs/2102.06129 (2021) - [i55]Nan Wang, Branislav Kveton, Maryam Karimzadehgan:
CORe: Capitalizing On Rewards in Bandit Exploration. CoRR abs/2103.04387 (2021) - [i54]Mohammad Javad Azizi, Branislav Kveton, Mohammad Ghavamzadeh:
Fixed-Budget Best-Arm Identification in Contextual Bandits: A Static-Adaptive Algorithm. CoRR abs/2106.04763 (2021) - [i53]Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh, Craig Boutilier:
Thompson Sampling with a Mixture Prior. CoRR abs/2106.05608 (2021) - [i52]Rong Zhu, Branislav Kveton:
Random Effect Bandits. CoRR abs/2106.12200 (2021) - [i51]Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvári:
No Regrets for Learning the Prior in Bandits. CoRR abs/2107.06196 (2021) - [i50]Muhammad Jehangir Amjad, Christophe Diot, Dimitris Konomis, Branislav Kveton, Augustin Soule, Xiaolong Yang:
Optimal Probing with Statistical Guarantees for Network Monitoring at Scale. CoRR abs/2109.07743 (2021) - [i49]Ruihao Zhu, Branislav Kveton:
Safe Optimal Design with Applications in Policy Learning. CoRR abs/2111.04835 (2021) - [i48]Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh:
Hierarchical Bayesian Bandits. CoRR abs/2111.06929 (2021) - 2020
- [c73]Sharan Vaswani, Abbas Mehrabian, Audrey Durand, Branislav Kveton:
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems. AISTATS 2020: 1988-1998 - [c72]Branislav Kveton, Manzil Zaheer, Csaba Szepesvári, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier:
Randomized Exploration in Generalized Linear Bandits. AISTATS 2020: 2066-2076 - [c71]Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel:
Graphical Models Meet Bandits: A Variational Thompson Sampling Approach. ICML 2020: 10902-10912 - [c70]Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer:
Differentiable Meta-Learning of Bandit Policies. NeurIPS 2020 - [c69]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier:
Latent Bandits Revisited. NeurIPS 2020 - [i47]Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer:
Differentiable Bandit Exploration. CoRR abs/2002.06772 (2020) - [i46]Thanh Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori, Anup Rao, Branislav Kveton:
Sample Efficient Graph-Based Optimization with Noisy Observations. CoRR abs/2006.02672 (2020) - [i45]Branislav Kveton, Martin Mladenov, Chih-Wei Hsu, Manzil Zaheer, Csaba Szepesvári, Craig Boutilier:
Differentiable Meta-Learning in Contextual Bandits. CoRR abs/2006.05094 (2020) - [i44]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed:
Piecewise-Stationary Off-Policy Optimization. CoRR abs/2006.08236 (2020) - [i43]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier:
Latent Bandits Revisited. CoRR abs/2006.08714 (2020) - [i42]Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel:
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems. CoRR abs/2007.04915 (2020) - [i41]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Mohammad Ghavamzadeh, Craig Boutilier:
Non-Stationary Latent Bandits. CoRR abs/2012.00386 (2020)
2010 – 2019
- 2019
- [c68]Yang Cao, Zheng Wen, Branislav Kveton, Yao Xie:
Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit. AISTATS 2019: 418-427 - [c67]Sumeet Katariya, Branislav Kveton, Zheng Wen, Vamsi K. Potluru:
Conservative Exploration using Interleaving. AISTATS 2019: 954-963 - [c66]Thanh Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori, Anup Rao, Branislav Kveton:
Sample Efficient Graph-Based Optimization with Noisy Observations. AISTATS 2019: 3333-3341 - [c65]Branislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh:
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. ICML 2019: 3601-3610 - [c64]Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier:
Perturbed-History Exploration in Stochastic Multi-Armed Bandits. IJCAI 2019: 2786-2793 - [c63]Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi:
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback. UAI 2019: 196-206 - [c62]Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier:
Perturbed-History Exploration in Stochastic Linear Bandits. UAI 2019: 530-540 - [c61]Gaurush Hiranandani, Harvineet Singh, Prakhar Gupta, Iftikhar Ahamath Burhanuddin, Zheng Wen, Branislav Kveton:
Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank. UAI 2019: 722-732 - [i40]Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier:
Perturbed-History Exploration in Stochastic Multi-Armed Bandits. CoRR abs/1902.10089 (2019) - [i39]Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier:
Perturbed-History Exploration in Stochastic Linear Bandits. CoRR abs/1903.09132 (2019) - [i38]Chih-Wei Hsu, Branislav Kveton, Ofer Meshi, Martin Mladenov, Csaba Szepesvári:
Empirical Bayes Regret Minimization. CoRR abs/1904.02664 (2019) - [i37]Branislav Kveton, Saied Mahdian, S. Muthukrishnan, Zheng Wen, Yikun Xian:
Waterfall Bandits: Learning to Sell Ads Online. CoRR abs/1904.09404 (2019) - [i36]Branislav Kveton, Manzil Zaheer, Csaba Szepesvári, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier:
Randomized Exploration in Generalized Linear Bandits. CoRR abs/1906.08947 (2019) - [i35]Sharan Vaswani, Abbas Mehrabian, Audrey Durand, Branislav Kveton:
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems. CoRR abs/1910.04928 (2019) - 2018
- [c60]Charles Chen, Sungchul Kim, Hung Bui, Ryan A. Rossi, Eunyee Koh, Branislav Kveton, Razvan C. Bunescu:
Predictive Analysis by Leveraging Temporal User Behavior and User Embeddings. CIKM 2018: 2175-2182 - [c59]Shuai Li, Yasin Abbasi-Yadkori, Branislav Kveton, S. Muthukrishnan, Vishwa Vinay, Zheng Wen:
Offline Evaluation of Ranking Policies with Click Models. KDD 2018: 1685-1694 - [c58]Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári:
TopRank: A practical algorithm for online stochastic ranking. NeurIPS 2018: 3949-3958 - [c57]Tong Yu, Branislav Kveton, Zheng Wen, Hung Bui, Ole J. Mengshoel:
SpectralLeader: Online Spectral Learning for Single Topic Models. ECML/PKDD (2) 2018: 379-395 - [c56]Xiuyuan Lu, Zheng Wen, Branislav Kveton:
Efficient online recommendation via low-rank ensemble sampling. RecSys 2018: 460-464 - [c55]Branislav Kveton, S. Muthukrishnan, Hoa T. Vu, Yikun Xian:
Finding Subcube Heavy Hitters in Analytics Data Streams. WWW 2018: 1705-1714 - [i34]Yang Cao, Zheng Wen, Branislav Kveton, Yao Xie:
Nearly Optimal Adaptive Procedure for Piecewise-Stationary Bandit: a Change-Point Detection Approach. CoRR abs/1802.03692 (2018) - [i33]Shuai Li, Yasin Abbasi-Yadkori, Branislav Kveton, S. Muthukrishnan, Vishwa Vinay, Zheng Wen:
Offline Evaluation of Ranking Policies with Click Models. CoRR abs/1804.10488 (2018) - [i32]Sharan Vaswani, Branislav Kveton, Zheng Wen, Anup Rao, Mark Schmidt, Yasin Abbasi-Yadkori:
New Insights into Bootstrapping for Bandits. CoRR abs/1805.09793 (2018) - [i31]Sumeet Katariya, Branislav Kveton, Zheng Wen, Vamsi K. Potluru:
Conservative Exploration using Interleaving. CoRR abs/1806.00892 (2018) - [i30]Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári:
TopRank: A practical algorithm for online stochastic ranking. CoRR abs/1806.02248 (2018) - [i29]Branislav Kveton, Chang Li, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi:
BubbleRank: Safe Online Learning to Rerank. CoRR abs/1806.05819 (2018) - [i28]Prakhar Gupta, Gaurush Hiranandani, Harvineet Singh, Branislav Kveton, Zheng Wen, Iftikhar Ahamath Burhanuddin:
Online Diverse Learning to Rank from Partial-Click Feedback. CoRR abs/1811.00911 (2018) - [i27]Branislav Kveton, Csaba Szepesvári, Zheng Wen, Mohammad Ghavamzadeh, Tor Lattimore:
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. CoRR abs/1811.05154 (2018) - 2017
- [c54]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen:
Stochastic Rank-1 Bandits. AISTATS 2017: 392-401 - [c53]Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt:
Model-Independent Online Learning for Influence Maximization. ICML 2017: 3530-3539 - [c52]Masrour Zoghi, Tomás Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvári, Zheng Wen:
Online Learning to Rank in Stochastic Click Models. ICML 2017: 4199-4208 - [c51]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen:
Bernoulli Rank-1 Bandits for Click Feedback. IJCAI 2017: 2001-2007 - [c50]Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani:
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback. NIPS 2017: 3022-3032 - [c49]Tong Yu, Branislav Kveton, Ole J. Mengshoel:
Thompson Sampling for Optimizing Stochastic Local Search. ECML/PKDD (1) 2017: 493-510 - [c48]Shi Zong, Branislav Kveton, Shlomo Berkovsky, Azin Ashkan, Zheng Wen:
Get to the Bottom: Causal Analysis for User Modeling. UMAP 2017: 256-264 - [c47]Shi Zong, Branislav Kveton, Shlomo Berkovsky, Azin Ashkan, Nikos Vlassis, Zheng Wen:
Does Weather Matter?: Causal Analysis of TV Logs. WWW (Companion Volume) 2017: 883-884 - [i26]Shi Zong, Branislav Kveton, Shlomo Berkovsky, Azin Ashkan, Nikos Vlassis, Zheng Wen:
Does Weather Matter? Causal Analysis of TV Logs. CoRR abs/1701.08716 (2017)