Energy-Balancing and Lifetime Enhancement of Wireless Sensor Network with Archimedes Spiral. - Subir Halder; Amrita Ghosal; Aadirupa Saha; DasBit Sipra (UIC, 2011)

Energy Saving Replay Attack Prevention in Clustered Wireless Sensor Networks. - Amrita Ghosal; Aadirupa Saha; Sipra Das Bit (PAISI, 2013)

Learning Score Systems for Patient Mortality Prediction in Intensive Care Units via Orthogonal Matching Pursuit. - Aadirupa Saha; Chandrahas Dewangan; Harikrishna Narasimhan; Sriram Sampath; Shivani Agarwal 001 (ICMLA, 2014)

Consistent Multiclass Algorithms for Complex Performance Measures. - Harikrishna Narasimhan; Harish G. Ramaswamy; Aadirupa Saha; Shivani Agarwal 001 (ICML, 2015)

Online Learning for Structured Loss Spaces. - Siddharth Barman; Aditya Gopalan; Aadirupa Saha (AAAI, 2018)

Battle of Bandits. - Aadirupa Saha; Aditya Gopalan (UAI, 2018)

PAC-Battling Bandits with Plackett-Luce: Tradeoff between Sample Complexity and Subset Size. - Aditya Gopalan; Aadirupa Saha (CoRR, 2018)

How Many Pairwise Preferences Do We Need to Rank a Graph Consistently? - Aadirupa Saha; Rakesh Shivanna; Chiranjib Bhattacharyya (AAAI, 2019)

Active Ranking with Subset-wise Preferences. - Aadirupa Saha; Aditya Gopalan (AISTATS, 2019)

PAC Battling Bandits in the Plackett-Luce Model. - Aadirupa Saha; Aditya Gopalan (ALT, 2019)

Combinatorial Bandits with Relative Feedback. - Aadirupa Saha; Aditya Gopalan (NeurIPS, 2019)

Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits. - Aadirupa Saha; Shreyas Sheshadri; Chiranjib Bhattacharyya (UAI, 2019)

Regret Minimisation in Multinomial Logit Bandits. - Aadirupa Saha; Aditya Gopalan (CoRR, 2019)

Polytime Decomposition of Generalized Submodular Base Polytopes with Efficient Sampling. - Aadirupa Saha (ACML, 2020)

Best-item Learning in Random Utility Models with Subset Choices. - Aadirupa Saha; Aditya Gopalan (AISTATS, 2020)

From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model. - Aadirupa Saha; Aditya Gopalan (ICML, 2020)

Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. - Aadirupa Saha; Pierre Gaillard; Michal Valko (ICML, 2020)

Improved Sleeping Bandits with Stochastic Actions Sets and Adversarial Rewards. - Aadirupa Saha; Pierre Gaillard; Michal Valko (CoRR, 2020)

Confidence-Budget Matching for Sequential Budgeted Learning. - Yonathan Efroni; Nadav Merlis; Aadirupa Saha; Shie Mannor (ICML, 2021)

Adversarial Dueling Bandits. - Aadirupa Saha; Tomer Koren; Yishay Mansour (ICML, 2021)

Dueling Convex Optimization. - Aadirupa Saha; Tomer Koren; Yishay Mansour (ICML, 2021)

Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. - Aadirupa Saha; Nagarajan Natarajan; Praneeth Netrapalli; Prateek Jain 002 (ICML, 2021)

Optimal Algorithms for Stochastic Contextual Preference Bandits. - Aadirupa Saha (NeurIPS, 2021)

Dueling Bandits with Adversarial Sleeping. - Aadirupa Saha; Pierre Gaillard (NeurIPS, 2021)

Strategically efficient exploration in competitive multi-agent reinforcement learning. - Robert Tyler Loftin; Aadirupa Saha; Sam Devlin; Katja Hofmann (UAI, 2021)

Pure Exploration with Structured Preference Feedback. - Shubham Gupta; Aadirupa Saha; Sumeet Katariya (CoRR, 2021)

A Sketch Based Game Theoretic Approach to Detect Anomalous Dense Sub-Communities in Large Data Streams. - Prateek Chanda; Aadirupa Saha (CoRR, 2021)

Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits. - Aadirupa Saha; Suprovat Ghoshal (AISTATS, 2022)

Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. - Aadirupa Saha; Akshay Krishnamurthy (ALT, 2022)

Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. - Viktor Bengs; Aadirupa Saha; Eyke Hüllermeier (ICML, 2022)

Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences. - Aadirupa Saha; Pierre Gaillard (ICML, 2022)

Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits. - Aadirupa Saha; Shubham Gupta (ICML, 2022)

Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences. - Aadirupa Saha; Pierre Gaillard (CoRR, 2022)

Dueling Convex Optimization with General Preferences. - Aadirupa Saha; Tomer Koren; Yishay Mansour (CoRR, 2022)

One Arrow, Two Kills: An Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits. - Pierre Gaillard; Aadirupa Saha; Soham Dan (CoRR, 2022)

ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits. - Thomas Kleine Buening; Aadirupa Saha (AISTATS, 2023)

One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits. - Pierre Gaillard; Aadirupa Saha; Soham Dan (AISTATS, 2023)

Dueling RL: Reinforcement Learning with Trajectory Preferences. - Aadirupa Saha; Aldo Pacchiano; Jonathan Lee 002 (AISTATS, 2023)

Federated Online and Bandit Convex Optimization. - Kumar Kshitij Patel; Lingxiao Wang; Aadirupa Saha; Nathan Srebro (ICML, 2023)

Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback. - Han Shao; Lee Cohen 001; Avrim Blum; Yishay Mansour; Aadirupa Saha; Matthew R. Walter (NeurIPS, 2023)

Federated Online and Bandit Convex Optimization. - Kumar Kshitij Patel; Lingxiao Wang; Aadirupa Saha; Nati Sebro (CoRR, 2023)

Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. - Rohan Deb; Aadirupa Saha (CoRR, 2023)

On the Vulnerability of Fairness Constrained Learning to Malicious Noise. - Avrim Blum; Princewill Okoroafor; Aadirupa Saha; Kevin M. Stangl (AISTATS, 2024)

Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. - Rohan Deb; Aadirupa Saha; Arindam Banerjee 001 (AISTATS, 2024)

Faster Convergence with MultiWay Preferences. - Aadirupa Saha; Vitaly Feldman; Yishay Mansour; Tomer Koren (AISTATS, 2024)

Dueling Optimization with a Monotone Adversary. - Avrim Blum; Meghal Gupta; Gene Li; Naren Sarayu Manoj; Aadirupa Saha; Yuanyuan Yang (ALT, 2024)

Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation. - Thomas Kleine Buening; Aadirupa Saha; Christos Dimitrakakis; Haifeng Xu (ICLR, 2024)

Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling. - Aadirupa Saha; Branislav Kveton (ICLR, 2024)

Strategic Linear Contextual Bandits. - Thomas Kleine Buening; Aadirupa Saha; Christos Dimitrakakis; Haifeng Xu (NeurIPS, 2024)

A Graph Theoretic Approach for Preference Learning with Feature Information. - Aadirupa Saha; Arun Rajkumar (UAI, 2024)

Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization. - Aadirupa Saha; Pierre Gaillard (CoRR, 2024)

DP-Dueling: Learning from Preference Feedback without Compromising User Privacy. - Aadirupa Saha; Hilal Asi (CoRR, 2024)

Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration. - Avinandan Bose; Zhihan Xiong; Aadirupa Saha; Simon Shaolei Du; Maryam Fazel (CoRR, 2024)

Finally Rank-Breaking Conquers MNL Bandits: Optimal and Efficient Algorithms for MNL Assortment. - Aadirupa Saha; Pierre Gaillard (ICLR, 2025)

Tracking the Best Expert Privately. - Aadirupa Saha; Vinod Raman; Hilal Asi (CoRR, 2025)