Aadirupa Saha - RankMe

83 papers A* 23A 13B 3C 3Journal 40Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	LLM-as-Judge on a Budget. CoRR Aadirupa Saha, Aniket Wagde, Branislav Kveton
2026	J	`jnl`	Learning to Allocate Resources with Censored Feedback. CoRR Giovanni Montanari, Côme Fiegel, Corentin Pla, Aadirupa Saha, Vianney Perchet
2026	J	`jnl`	One Good Source is All You Need: Near-Optimal Regret for Bandits under Heterogeneous Noise. CoRR Aadirupa Saha, Amith Bhat, Haipeng Luo
2025	A*	`conf`	Dueling Convex Optimization with General Preferences. ICML Aadirupa Saha, Tomer Koren, Yishay Mansour
2025	A*	`conf`	Finally Rank-Breaking Conquers MNL Bandits: Optimal and Efficient Algorithms for MNL Assortment. ICLR Aadirupa Saha, Pierre Gaillard
2025	A*	`conf`	Tracking The Best Expert Privately. ICML Hilal Asi, Vinod Raman, Aadirupa Saha
2025	J	`jnl`	Tracking the Best Expert Privately. CoRR Aadirupa Saha, Vinod Raman, Hilal Asi
2024	A	`conf`	A Graph Theoretic Approach for Preference Learning with Feature Information. UAI Aadirupa Saha, Arun Rajkumar
2024	A*	`conf`	Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation. ICLR Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu
2024	J	`jnl`	DP-Dueling: Learning from Preference Feedback without Compromising User Privacy. CoRR Aadirupa Saha, Hilal Asi
2024	B	`conf`	Dueling Optimization with a Monotone Adversary. ALT Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj, Aadirupa Saha, Yuanyuan Yang
2024	A	`conf`	Faster Convergence with MultiWay Preferences. AISTATS Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren
2024	J	`jnl`	Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration. CoRR Avinandan Bose, Zhihan Xiong, Aadirupa Saha, Simon Shaolei Du, Maryam Fazel
2024	A	`conf`	On the Vulnerability of Fairness Constrained Learning to Malicious Noise. AISTATS Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin M. Stangl
2024	A*	`conf`	Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling. ICLR Aadirupa Saha, Branislav Kveton
2024	J	`jnl`	Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization. CoRR Aadirupa Saha, Pierre Gaillard
2024	A*	`conf`	Strategic Linear Contextual Bandits. NeurIPS Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu
2024	J	`jnl`	Strategic Linear Contextual Bandits. CoRR Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu
2024	A	`conf`	Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. AISTATS Rohan Deb, Aadirupa Saha, Arindam Banerjee
2023	A	`conf`	ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits. AISTATS Thomas Kleine Buening, Aadirupa Saha
2023	J	`jnl`	Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation. CoRR Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu
2023	J	`jnl`	Dueling Optimization with a Monotone Adversary. CoRR Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj, Aadirupa Saha, Yuanyuan Yang
2023	A	`conf`	Dueling RL: Reinforcement Learning with Trajectory Preferences. AISTATS Aadirupa Saha, Aldo Pacchiano, Jonathan Lee
2023	A*	`conf`	Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback. NeurIPS Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Matthew R. Walter
2023	J	`jnl`	Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback. CoRR Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Matthew R. Walter
2023	J	`jnl`	Faster Convergence with Multiway Preferences. CoRR Aadirupa Saha, Vitaly Feldman, Tomer Koren, Yishay Mansour
2023	A*	`conf`	Federated Online and Bandit Convex Optimization. ICML Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro
2023	J	`jnl`	Federated Online and Bandit Convex Optimization. CoRR Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nati Sebro
2023	J	`jnl`	On the Vulnerability of Fairness Constrained Learning to Malicious Noise. CoRR Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin Stangl
2023	A	`conf`	One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits. AISTATS Pierre Gaillard, Aadirupa Saha, Soham Dan
2023	J	`jnl`	Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling. CoRR Aadirupa Saha, Branislav Kveton
2023	J	`jnl`	Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. CoRR Rohan Deb, Aadirupa Saha
2022	J	`jnl`	ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits. CoRR Thomas Kleine Buening, Aadirupa Saha
2022	J	`jnl`	Dueling Convex Optimization with General Preferences. CoRR Aadirupa Saha, Tomer Koren, Yishay Mansour
2022	B	`conf`	Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. ALT Aadirupa Saha, Akshay Krishnamurthy
2022	A	`conf`	Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits. AISTATS Aadirupa Saha, Suprovat Ghoshal
2022	J	`jnl`	Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits. CoRR Suprovat Ghoshal, Aadirupa Saha
2022	J	`jnl`	One Arrow, Two Kills: An Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits. CoRR Pierre Gaillard, Aadirupa Saha, Soham Dan
2022	A*	`conf`	Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits. ICML Aadirupa Saha, Shubham Gupta
2022	A*	`conf`	Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. ICML Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier
2022	J	`jnl`	Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. CoRR Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier
2022	A*	`conf`	Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences. ICML Aadirupa Saha, Pierre Gaillard
2022	J	`jnl`	Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences. CoRR Aadirupa Saha, Pierre Gaillard
2021	J	`jnl`	A Sketch Based Game Theoretic Approach to Detect Anomalous Dense Sub-Communities in Large Data Streams. CoRR Prateek Chanda, Aadirupa Saha
2021	A*	`conf`	Adversarial Dueling Bandits. ICML Aadirupa Saha, Tomer Koren, Yishay Mansour
2021	A*	`conf`	Confidence-Budget Matching for Sequential Budgeted Learning. ICML Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor
2021	J	`jnl`	Confidence-Budget Matching for Sequential Budgeted Learning. CoRR Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor
2021	A*	`conf`	Dueling Bandits with Adversarial Sleeping. NeurIPS Aadirupa Saha, Pierre Gaillard
2021	J	`jnl`	Dueling Bandits with Adversarial Sleeping. CoRR Aadirupa Saha, Pierre Gaillard
2021	A*	`conf`	Dueling Convex Optimization. ICML Aadirupa Saha, Tomer Koren, Yishay Mansour
2021	J	`jnl`	Dueling RL: Reinforcement Learning with Trajectory Preferences. CoRR Aldo Pacchiano, Aadirupa Saha, Jonathan Lee
2021	J	`jnl`	Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. CoRR Aadirupa Saha, Akshay Krishnamurthy
2021	A*	`conf`	Optimal Algorithms for Stochastic Contextual Preference Bandits. NeurIPS Aadirupa Saha
2021	J	`jnl`	Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization. CoRR Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
2021	J	`jnl`	Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits. CoRR Shubham Gupta, Aadirupa Saha
2021	A*	`conf`	Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. ICML Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
2021	J	`jnl`	Pure Exploration with Structured Preference Feedback. CoRR Shubham Gupta, Aadirupa Saha, Sumeet Katariya
2021	J	`jnl`	Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning. CoRR Robert Tyler Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann
2021	A	`conf`	Strategically efficient exploration in competitive multi-agent reinforcement learning. UAI Robert Tyler Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann
2020	J	`jnl`	Adversarial Dueling Bandits. CoRR Aadirupa Saha, Tomer Koren, Yishay Mansour
2020	A	`conf`	Best-item Learning in Random Utility Models with Subset Choices. AISTATS Aadirupa Saha, Aditya Gopalan
2020	J	`jnl`	Best-item Learning in Random Utility Models with Subset Choices. CoRR Aadirupa Saha, Aditya Gopalan
2020	A*	`conf`	From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model. ICML Aadirupa Saha, Aditya Gopalan
2020	A*	`conf`	Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. ICML Aadirupa Saha, Pierre Gaillard, Michal Valko
2020	J	`jnl`	Improved Sleeping Bandits with Stochastic Actions Sets and Adversarial Rewards. CoRR Aadirupa Saha, Pierre Gaillard, Michal Valko
2020	C	`conf`	Polytime Decomposition of Generalized Submodular Base Polytopes with Efficient Sampling. ACML Aadirupa Saha
2019	A	`conf`	Active Ranking with Subset-wise Preferences. AISTATS Aadirupa Saha, Aditya Gopalan
2019	A	`conf`	Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits. UAI Aadirupa Saha, Shreyas Sheshadri, Chiranjib Bhattacharyya
2019	A*	`conf`	Combinatorial Bandits with Relative Feedback. NeurIPS Aadirupa Saha, Aditya Gopalan
2019	J	`jnl`	From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model. CoRR Aadirupa Saha, Aditya Gopalan
2019	A*	`conf`	How Many Pairwise Preferences Do We Need to Rank a Graph Consistently? AAAI Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya
2019	B	`conf`	PAC Battling Bandits in the Plackett-Luce Model. ALT Aadirupa Saha, Aditya Gopalan
2019	J	`jnl`	Regret Minimisation in Multinomial Logit Bandits. CoRR Aadirupa Saha, Aditya Gopalan
2018	J	`jnl`	Active Ranking with Subset-wise Preferences. CoRR Aadirupa Saha, Aditya Gopalan
2018	A	`conf`	Battle of Bandits. UAI Aadirupa Saha, Aditya Gopalan
2018	J	`jnl`	How Many Pairwise Preferences Do We Need to Rank A Graph Consistently? CoRR Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya
2018	A*	`conf`	Online Learning for Structured Loss Spaces. AAAI Siddharth Barman, Aditya Gopalan, Aadirupa Saha
2018	J	`jnl`	PAC-Battling Bandits with Plackett-Luce: Tradeoff between Sample Complexity and Subset Size. CoRR Aditya Gopalan, Aadirupa Saha
2017	J	`jnl`	Online Learning for Structured Loss Spaces. CoRR Siddharth Barman, Aditya Gopalan, Aadirupa Saha
2015	A*	`conf`	Consistent Multiclass Algorithms for Complex Performance Measures. ICML Harikrishna Narasimhan, Harish G. Ramaswamy, Aadirupa Saha, Shivani Agarwal
2014	C	`conf`	Learning Score Systems for Patient Mortality Prediction in Intensive Care Units via Orthogonal Matching Pursuit. ICMLA Aadirupa Saha, Chandrahas Dewangan, Harikrishna Narasimhan, Sriram Sampath, Shivani Agarwal
2013	—	`conf`	Energy Saving Replay Attack Prevention in Clustered Wireless Sensor Networks. PAISI Amrita Ghosal, Aadirupa Saha, Sipra Das Bit
2011	C	`conf`	Energy-Balancing and Lifetime Enhancement of Wireless Sensor Network with Archimedes Spiral. UIC Subir Halder, Amrita Ghosal, Aadirupa Saha, DasBit Sipra