| 2025 | ICLR | Finally Rank-Breaking Conquers MNL Bandits: Optimal and Efficient Algorithms for MNL Assortment. | Aadirupa Saha, Pierre Gaillard |
| 2025 | ICML | Tracking The Best Expert Privately. | Hilal Asi, Vinod Raman, Aadirupa Saha |
| 2025 | ICML | Dueling Convex Optimization with General Preferences. | Aadirupa Saha, Tomer Koren, Yishay Mansour |
| 2024 | AISTATS | On the Vulnerability of Fairness Constrained Learning to Malicious Noise. | Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin M. Stangl |
| 2024 | AISTATS | Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. | Rohan Deb, Aadirupa Saha, Arindam Banerjee |
| 2024 | AISTATS | Faster Convergence with MultiWay Preferences. | Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren |
| 2024 | ALT | Dueling Optimization with a Monotone Adversary. | Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj, Aadirupa Saha, Yuanyuan Yang |
| 2024 | ICLR | Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation. | Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu |
| 2024 | ICLR | Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling. | Aadirupa Saha, Branislav Kveton |
| 2024 | UAI | A Graph Theoretic Approach for Preference Learning with Feature Information. | Aadirupa Saha, Arun Rajkumar |
| 2023 | AISTATS | ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits. | Thomas Kleine Buening, Aadirupa Saha |
| 2023 | AISTATS | One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits. | Pierre Gaillard, Aadirupa Saha, Soham Dan |
| 2023 | AISTATS | Dueling RL: Reinforcement Learning with Trajectory Preferences. | Aadirupa Saha, Aldo Pacchiano, Jonathan Lee |
| 2023 | ICML | Federated Online and Bandit Convex Optimization. | Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro |
| 2022 | AISTATS | Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits. | Aadirupa Saha, Suprovat Ghoshal |
| 2022 | ALT | Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. | Aadirupa Saha, Akshay Krishnamurthy |
| 2022 | ICML | Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. | Viktor Bengs, Aadirupa Saha, Eyke Hllermeier |
| 2022 | ICML | Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences. | Aadirupa Saha, Pierre Gaillard |
| 2022 | ICML | Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits. | Aadirupa Saha, Shubham Gupta |
| 2021 | ICML | Confidence-Budget Matching for Sequential Budgeted Learning. | Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor |
| 2021 | ICML | Adversarial Dueling Bandits. | Aadirupa Saha, Tomer Koren, Yishay Mansour |
| 2021 | ICML | Dueling Convex Optimization. | Aadirupa Saha, Tomer Koren, Yishay Mansour |
| 2021 | ICML | Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. | Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain |
| 2021 | UAI | Strategically efficient exploration in competitive multi-agent reinforcement learning. | Robert Tyler Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann |
| 2020 | ACML | Polytime Decomposition of Generalized Submodular Base Polytopes with Efficient Sampling. | Aadirupa Saha |
| 2020 | AISTATS | Best-item Learning in Random Utility Models with Subset Choices. | Aadirupa Saha, Aditya Gopalan |
| 2020 | ICML | From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model. | Aadirupa Saha, Aditya Gopalan |
| 2020 | ICML | Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. | Aadirupa Saha, Pierre Gaillard, Michal Valko |
| 2019 | AAAI | How Many Pairwise Preferences Do We Need to Rank a Graph Consistently? | Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya |
| 2019 | AISTATS | Active Ranking with Subset-wise Preferences. | Aadirupa Saha, Aditya Gopalan |
| 2019 | ALT | PAC Battling Bandits in the Plackett-Luce Model. | Aadirupa Saha, Aditya Gopalan |
| 2019 | UAI | Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits. | Aadirupa Saha, Shreyas Sheshadri, Chiranjib Bhattacharyya |
| 2018 | AAAI | Online Learning for Structured Loss Spaces. | Siddharth Barman, Aditya Gopalan, Aadirupa Saha |
| 2018 | UAI | Battle of Bandits. | Aadirupa Saha, Aditya Gopalan |
| 2015 | ICML | Consistent Multiclass Algorithms for Complex Performance Measures. | Harikrishna Narasimhan, Harish G. Ramaswamy, Aadirupa Saha, Shivani Agarwal |
| 2014 | ICMLA | 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 |
| 2011 | UIC | Energy-Balancing and Lifetime Enhancement of Wireless Sensor Network with Archimedes Spiral. | Subir Halder, Amrita Ghosal, Aadirupa Saha, DasBit Sipra |