Aadirupa Saha

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 70

Top Topics

Unified Framework

Grows Quadratically

Online Learning

Convex Optimization

Top Venues

Publications

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