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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 - [i84]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) - [i83]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) - [i82]Subhojyoti Mukherjee, Anusha Lalitha, Kousha Kalantari, Aniket Deshmukh, Ge Liu, Yifei Ma, Branislav Kveton:
Optimal Design for Human Feedback. CoRR abs/2404.13895 (2024) - [i81]Matej Cief, Branislav Kveton, Michal Kompan:
Cross-Validated Off-Policy Evaluation. CoRR abs/2405.15332 (2024) - [i80]Aniruddha Bhargava, Lalit Jain, Branislav Kveton, Ge Liu, Subhojyoti Mukherjee:
Off-Policy Evaluation from Logged Human Feedback. CoRR abs/2406.10030 (2024) - [i79]Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song:
Online Posterior Sampling with a Diffusion Prior. CoRR abs/2410.03919 (2024) - [i78]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) - 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) - [i25]Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt:
Diffusion Independent Semi-Bandit Influence Maximization. CoRR abs/1703.00557 (2017) - [i24]Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvári, Tomás Tunys, Zheng Wen, Masrour Zoghi:
Online Learning to Rank in Stochastic Click Models. CoRR abs/1703.02527 (2017) - [i23]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen:
Bernoulli Rank-1 Bandits for Click Feedback. CoRR abs/1703.06513 (2017) - [i22]Branislav Kveton, S. Muthukrishnan, Hoa T. Vu:
Finding Subcube Heavy Hitters in Data Streams. CoRR abs/1708.05159 (2017) - [i21]Tong Yu, Branislav Kveton, Zheng Wen, Hung Bui, Ole J. Mengshoel:
SpectralFPL: Online Spectral Learning for Single Topic Models. CoRR abs/1709.07172 (2017) - [i20]Branislav Kveton, Csaba Szepesvári, Anup Rao, Zheng Wen, Yasin Abbasi-Yadkori, S. Muthukrishnan:
Stochastic Low-Rank Bandits. CoRR abs/1712.04644 (2017) - 2016
- [j4]Branislav Kveton, Shlomo Berkovsky:
Minimal Interaction Content Discovery in Recommender Systems. ACM Trans. Interact. Intell. Syst. 6(2): 15:1-15:25 (2016) - [c46]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen:
DCM Bandits: Learning to Rank with Multiple Clicks. ICML 2016: 1215-1224 - [c45]Suvash Sedhain, Hung Bui, Jaya Kawale, Nikos Vlassis, Branislav Kveton, Aditya Krishna Menon, Trung Bui, Scott Sanner:
Practical Linear Models for Large-Scale One-Class Collaborative Filtering. IJCAI 2016: 3854-3860 - [c44]Branislav Kveton, Hung Bui, Mohammad Ghavamzadeh, Georgios Theocharous, S. Muthukrishnan, Siqi Sun:
Graphical Model Sketch. ECML/PKDD (1) 2016: 81-97 - [c43]Shi Zong, Hao Ni, Kenny Sung, Nan Rosemary Ke, Zheng Wen, Branislav Kveton:
Cascading Bandits for Large-Scale Recommendation Problems. UAI 2016 - [i19]Branislav Kveton, Hung Bui, Mohammad Ghavamzadeh, Georgios Theocharous, S. Muthukrishnan, Siqi Sun:
Graphical Model Sketch. CoRR abs/1602.03105 (2016) - [i18]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen:
DCM Bandits: Learning to Rank with Multiple Clicks. CoRR abs/1602.03146 (2016) - [i17]Shi Zong, Hao Ni, Kenny Sung, Nan Rosemary Ke, Zheng Wen, Branislav Kveton:
Cascading Bandits for Large-Scale Recommendation Problems. CoRR abs/1603.05359 (2016) - [i16]Zheng Wen, Branislav Kveton, Michal Valko:
Influence Maximization with Semi-Bandit Feedback. CoRR abs/1605.06593 (2016) - [i15]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen:
Stochastic Rank-1 Bandits. CoRR abs/1608.03023 (2016) - 2015
- [j3]Salman Salamatian, Amy Zhang, Flávio du Pin Calmon, Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Pedro Oliveira, Nina Taft:
Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy. IEEE J. Sel. Top. Signal Process. 9(7): 1240-1255 (2015) - [j2]Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Amy Zhang:
PriView: Personalized Media Consumption Meets Privacy against Inference Attacks. IEEE Softw. 32(4): 53-59 (2015) - [c42]Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits. AISTATS 2015 - [c41]Branislav Kveton, Csaba Szepesvári, Zheng Wen, Azin Ashkan:
Cascading Bandits: Learning to Rank in the Cascade Model. ICML 2015: 767-776 - [c40]Zheng Wen, Branislav Kveton, Azin Ashkan:
Efficient Learning in Large-Scale Combinatorial Semi-Bandits. ICML 2015: 1113-1122 - [c39]Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen:
Optimal Greedy Diversity for Recommendation. IJCAI 2015: 1742-1748 - [c38]Branislav Kveton, Shlomo Berkovsky:
Minimal Interaction Search in Recommender Systems. IUI 2015: 236-246 - [c37]Jaya Kawale, Hung Hai Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla:
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation. NIPS 2015: 1297-1305 - [c36]Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Combinatorial Cascading Bandits. NIPS 2015: 1450-1458 - [i14]Branislav Kveton, Csaba Szepesvári, Zheng Wen, Azin Ashkan:
Cascading Bandits. CoRR abs/1502.02763 (2015) - [i13]Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Combinatorial Cascading Bandits. CoRR abs/1507.04208 (2015) - 2014
- [c35]Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan:
Large-Scale Optimistic Adaptive Submodularity. AAAI 2014: 1816-1823 - [c34]Michal Valko, Rémi Munos, Branislav Kveton, Tomás Kocák:
Spectral Bandits for Smooth Graph Functions. ICML 2014: 46-54 - [c33]Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen:
Diversified Utility Maximization for Recommendations. RecSys Posters 2014 - [c32]Branislav Kveton, Zheng Wen, Azin Ashkan, Hoda Eydgahi, Brian Eriksson:
Matroid Bandits: Fast Combinatorial Optimization with Learning. UAI 2014: 420-429 - [c31]Salman Salamatian, Nadia Fawaz, Branislav Kveton, Nina Taft:
SPPM: Sparse Privacy Preserving Mappings. UAI 2014: 712-721 - [i12]Branislav Kveton, Zheng Wen, Azin Ashkan, Hoda Eydgahi, Brian Eriksson:
Matroid Bandits: Fast Combinatorial Optimization with Learning. CoRR abs/1403.5045 (2014) - [i11]Branislav Kveton, Zheng Wen, Azin Ashkan, Michal Valko:
Learning to Act Greedily: Polymatroid Semi-Bandits. CoRR abs/1405.7752 (2014) - [i10]Zheng Wen, Azin Ashkan, Hoda Eydgahi, Branislav Kveton:
Efficient Learning in Large-Scale Combinatorial Semi-Bandits. CoRR abs/1406.7443 (2014) - [i9]Salman Salamatian, Amy Zhang, Flávio du Pin Calmon, Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Pedro Oliveira, Nina Taft:
Managing your Private and Public Data: Bringing down Inference Attacks against your Privacy. CoRR abs/1408.3698 (2014) - [i8]Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits. CoRR abs/1410.0949 (2014) - [i7]Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen:
DUM: Diversity-Weighted Utility Maximization for Recommendations. CoRR abs/1411.3650 (2014) - 2013
- [c30]Branislav Kveton, Georgios Theocharous:
Structured Kernel-Based Reinforcement Learning. AAAI 2013: 569-575 - [c29]Branislav Kveton, Michal Valko:
Learning from a single labeled face and a stream of unlabeled data. FG 2013: 1-8 - [c28]Salman Salamatian, Amy Zhang, Flávio du Pin Calmon, Sandilya Bhamidipati, Nadia Fawaz, Branislav Kveton, Pedro Oliveira, Nina Taft:
How to hide the elephant- or the donkey- in the room: Practical privacy against statistical inference for large data. GlobalSIP 2013: 269-272 - [c27]Zheng Wen, Branislav Kveton, Brian Eriksson, Sandilya Bhamidipati:
Sequential Bayesian Search. ICML (2) 2013: 226-234 - [c26]Diana Joumblatt, Jaideep Chandrashekar, Branislav Kveton, Nina Taft, Renata Teixeira:
Predicting user dissatisfaction with Internet application performance at end-hosts. INFOCOM 2013: 235-239 - [c25]Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan:
Adaptive Submodular Maximization in Bandit Setting. NIPS 2013: 2697-2705 - 2012
- [c24]Branislav Kveton, Georgios Theocharous:
Kernel-Based Reinforcement Learning on Representative States. AAAI 2012: 977-983 - [c23]Tianxi Li, Branislav Kveton, Yu Wu, Ashwin Kashyap:
Incorporating Metadata into Dynamic Topic Analysis. BMA 2012: 34-43 - [c22]Stéphane Caron, Branislav Kveton, Marc Lelarge, Smriti Bhagat:
Leveraging Side Observations in Stochastic Bandits. UAI 2012: 142-151 - [i6]Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting:
Online Semi-Supervised Learning on Quantized Graphs. CoRR abs/1203.3522 (2012) - [i5]Qian Zhu, Branislav Kveton, Lily B. Mummert, Padmanabhan Pillai:
Automatic Tuning of Interactive Perception Applications. CoRR abs/1203.3537 (2012) - [i4]Branislav Kveton, Milos Hauskrecht:
Partitioned Linear Programming Approximations for MDPs. CoRR abs/1206.3266 (2012) - [i3]Carlos Guestrin, Milos Hauskrecht, Branislav Kveton:
Solving Factored MDPs with Continuous and Discrete Variables. CoRR abs/1207.4150 (2012) - [i2]Stéphane Caron, Branislav Kveton, Marc Lelarge, Smriti Bhagat:
Leveraging Side Observations in Stochastic Bandits. CoRR abs/1210.4839 (2012) - 2011
- [c21]Avneesh Singh Saluja, Frank Mokaya, Mariano Phielipp, Branislav Kveton:
Automatic Identity Inference for Smart TVs. Lifelong Learning 2011 - [c20]Michal Valko, Branislav Kveton, Hamed Valizadegan, Gregory F. Cooper, Milos Hauskrecht:
Conditional Anomaly Detection with Soft Harmonic Functions. ICDM 2011: 735-743 - [i1]Carlos Guestrin, Milos Hauskrecht, Branislav Kveton:
Solving Factored MDPs with Hybrid State and Action Variables. CoRR abs/1110.0028 (2011) - 2010
- [c19]Branislav Kveton, Matthai Philipose, Michal Valko, Ling Huang:
Online semi-supervised perception: Real-time learning without explicit feedback. CVPR Workshops 2010: 15-21 - [c18]Mariano Phielipp, Magdiel Galan, Richard Lee, Branislav Kveton, Jeffrey Hightower:
Fast, Accurate, and Practical Identity Inference Using TV Remote Controls. IAAI 2010: 1827-1832 - [c17]Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting:
Online Semi-Supervised Learning on Quantized Graphs. UAI 2010: 606-614 - [c16]Qian Zhu, Branislav Kveton, Lily B. Mummert, Padmanabhan Pillai:
Automatic Tuning of Interactive Perception Applications. UAI 2010: 743-751 - [c15]Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang:
Semi-Supervised Learning with Max-Margin Graph Cuts. AISTATS 2010: 421-428
2000 – 2009
- 2009
- [c14]Keng-hao Chang, Jeffrey Hightower, Branislav Kveton:
Inferring Identity Using Accelerometers in Television Remote Controls. Pervasive 2009: 151-167 - 2008
- [c13]Branislav Kveton, Jia Yuan Yu, Georgios Theocharous, Shie Mannor:
Online Learning with Expert Advice and Finite-Horizon Constraints. AAAI 2008: 331-336 - [c12]Branislav Kveton, Jia Yuan Yu, Georgios Theocharous, Shie Mannor:
A Lazy Approach to Online Learning with Constraints. ISAIM 2008 - [c11]Branislav Kveton, Milos Hauskrecht:
Partitioned Linear Programming Approximations for MDPs. UAI 2008: 341-348 - 2007
- [c10]Branislav Kveton, Prashant Gandhi, Georgios Theocharous, Shie Mannor, Barbara Rosario, Nilesh Shah:
Adaptive Timeout Policies for Fast Fine-Grained Power Management. AAAI 2007: 1795-1800 - [c9]Milos Hauskrecht, Michal Valko, Branislav Kveton, Shyam Visweswaran, Gregory F. Cooper:
Evidence-based Anomaly Detection in Clinical Domains. AMIA 2007 - 2006
- [j1]Branislav Kveton, Milos Hauskrecht, Carlos Guestrin:
Solving Factored MDPs with Hybrid State and Action Variables. J. Artif. Intell. Res. 27: 153-201 (2006) - [c8]Denver Dash, Branislav Kveton, John Mark Agosta, Eve M. Schooler, Jaideep Chandrashekar, Abraham Bachrach, Alex Newman:
When Gossip is Good: Distributed Probabilistic Inference for Detection of Slow Network Intrusions. AAAI 2006: 1115-1122 - [c7]Branislav Kveton, Milos Hauskrecht:
Learning Basis Functions in Hybrid Domains. AAAI 2006: 1161-1166 - [c6]Branislav Kveton, Milos Hauskrecht:
Solving Factored MDPs with Exponential-Family Transition Models. ICAPS 2006: 114-120 - 2005
- [c5]Branislav Kveton, Denver Dash:
Automatic Excursion Detection in Manufacturing: Preliminary Results. FLAIRS 2005: 375-380 - [c4]Branislav Kveton, Milos Hauskrecht:
An MCMC Approach to Solving Hybrid Factored MDPs. IJCAI 2005: 1346-1351 - 2004
- [c3]Branislav Kveton, Milos Hauskrecht:
Heuristic Refinements of Approximate Linear Programming for Factored Continuous-State Markov Decision Processes. ICAPS 2004: 306-314 - [c2]Carlos Guestrin, Milos Hauskrecht, Branislav Kveton:
Solving Factored MDPs with Continuous and Discrete Variables. UAI 2004: 235-242 - 2003
- [c1]Milos Hauskrecht, Branislav Kveton:
Linear Program Approximations for Factored Continuous-State Markov Decision Processes. NIPS 2003: 895-902
Coauthor Index
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