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2020 – today
- 2024
[c38]Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren:
Faster Convergence with MultiWay Preferences. AISTATS 2024: 433-441- [c37]Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin M. Stangl:
On the Vulnerability of Fairness Constrained Learning to Malicious Noise. AISTATS 2024: 4096-4104 - [c36]Rohan Deb, Aadirupa Saha, Arindam Banerjee:
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. AISTATS 2024: 4546-4554 - [c35]Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj
, Aadirupa Saha, Yuanyuan Yang:
Dueling Optimization with a Monotone Adversary. ALT 2024: 221-243 - [c34]Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu:
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation. ICLR 2024 - [c33]Aadirupa Saha, Branislav Kveton:
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling. ICLR 2024 - [i35]Aadirupa Saha, Pierre Gaillard:
Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization. CoRR abs/2402.18917 (2024) - [i34]Aadirupa Saha, Hilal Asi:
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy. CoRR abs/2403.15045 (2024) - [i33]Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu:
Strategic Linear Contextual Bandits. CoRR abs/2406.00551 (2024) - 2023
- [c32]Thomas Kleine Buening, Aadirupa Saha:
ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits. AISTATS 2023: 3854-3878 - [c31]Aadirupa Saha, Aldo Pacchiano, Jonathan Lee:
Dueling RL: Reinforcement Learning with Trajectory Preferences. AISTATS 2023: 6263-6289 - [c30]Pierre Gaillard, Aadirupa Saha, Soham Dan:
One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits. AISTATS 2023: 7755-7773 - [c29]Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro:
Federated Online and Bandit Convex Optimization. ICML 2023: 27439-27460 - [c28]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 - [i32]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 abs/2302.03805 (2023) - [i31]Aadirupa Saha, Branislav Kveton:
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling. CoRR abs/2303.09033 (2023) - [i30]Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin Stangl:
On the Vulnerability of Fairness Constrained Learning to Malicious Noise. CoRR abs/2307.11892 (2023) - [i29]Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj, Aadirupa Saha, Yuanyuan Yang:
Dueling Optimization with a Monotone Adversary. CoRR abs/2311.11185 (2023) - [i28]Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu:
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation. CoRR abs/2311.15647 (2023) - [i27]Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nati Sebro:
Federated Online and Bandit Convex Optimization. CoRR abs/2311.17586 (2023) - [i26]Aadirupa Saha, Vitaly Feldman, Tomer Koren, Yishay Mansour:
Faster Convergence with Multiway Preferences. CoRR abs/2312.11788 (2023) - [i25]Rohan Deb, Aadirupa Saha:
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. CoRR abs/2312.17229 (2023) - 2022
- [c27]Aadirupa Saha, Suprovat Ghoshal:
Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits. AISTATS 2022: 456-482 - [c26]Aadirupa Saha, Akshay Krishnamurthy:
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. ALT 2022: 968-994 - [c25]Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier:
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. ICML 2022: 1764-1786 - [c24]Aadirupa Saha, Pierre Gaillard:
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences. ICML 2022: 19011-19026 - [c23]Aadirupa Saha, Shubham Gupta:
Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits. ICML 2022: 19027-19049 - [i24]Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier:
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. CoRR abs/2202.04593 (2022) - [i23]Aadirupa Saha, Pierre Gaillard:
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences. CoRR abs/2202.06694 (2022) - [i22]Suprovat Ghoshal, Aadirupa Saha:
Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits. CoRR abs/2202.11795 (2022) - [i21]Aadirupa Saha, Tomer Koren, Yishay Mansour:
Dueling Convex Optimization with General Preferences. CoRR abs/2210.02562 (2022) - [i20]Thomas Kleine Buening, Aadirupa Saha:
ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits. CoRR abs/2210.14322 (2022) - [i19]Pierre Gaillard, Aadirupa Saha, Soham Dan:
One Arrow, Two Kills: An Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits. CoRR abs/2210.14998 (2022) - 2021
- [c22]Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor:
Confidence-Budget Matching for Sequential Budgeted Learning. ICML 2021: 2937-2947 - [c21]Aadirupa Saha, Tomer Koren, Yishay Mansour:
Adversarial Dueling Bandits. ICML 2021: 9235-9244 - [c20]Aadirupa Saha, Tomer Koren, Yishay Mansour:
Dueling Convex Optimization. ICML 2021: 9245-9254 - [c19]Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain:
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. ICML 2021: 9255-9264 - [c18]Aadirupa Saha, Pierre Gaillard:
Dueling Bandits with Adversarial Sleeping. NeurIPS 2021: 27761-27771 - [c17]Aadirupa Saha:
Optimal Algorithms for Stochastic Contextual Preference Bandits. NeurIPS 2021: 30050-30062 - [c16]Robert Tyler Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann:
Strategically efficient exploration in competitive multi-agent reinforcement learning. UAI 2021: 1587-1596 - [i18]Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor:
Confidence-Budget Matching for Sequential Budgeted Learning. CoRR abs/2102.03400 (2021) - [i17]Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain:
Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization. CoRR abs/2102.07387 (2021) - [i16]Shubham Gupta, Aadirupa Saha, Sumeet Katariya:
Pure Exploration with Structured Preference Feedback. CoRR abs/2104.05294 (2021) - [i15]Aadirupa Saha, Pierre Gaillard:
Dueling Bandits with Adversarial Sleeping. CoRR abs/2107.02274 (2021) - [i14]Robert Tyler Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann:
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning. CoRR abs/2107.14698 (2021) - [i13]Shubham Gupta, Aadirupa Saha:
Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits. CoRR abs/2111.03917 (2021) - [i12]Aldo Pacchiano, Aadirupa Saha, Jonathan Lee:
Dueling RL: Reinforcement Learning with Trajectory Preferences. CoRR abs/2111.04850 (2021) - [i11]Aadirupa Saha, Akshay Krishnamurthy:
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. CoRR abs/2111.12306 (2021) - [i10]Prateek Chanda, Aadirupa Saha:
A Sketch Based Game Theoretic Approach to Detect Anomalous Dense Sub-Communities in Large Data Streams. CoRR abs/2111.15525 (2021) - 2020
- [c15]Aadirupa Saha:
Polytime Decomposition of Generalized Submodular Base Polytopes with Efficient Sampling. ACML 2020: 625-640 - [c14]Aadirupa Saha, Aditya Gopalan:
Best-item Learning in Random Utility Models with Subset Choices. AISTATS 2020: 4281-4291 - [c13]Aadirupa Saha, Pierre Gaillard, Michal Valko:
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards. ICML 2020: 8357-8366 - [c12]Aadirupa Saha, Aditya Gopalan:
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model. ICML 2020: 8367-8376 - [i9]Aadirupa Saha, Aditya Gopalan:
Best-item Learning in Random Utility Models with Subset Choices. CoRR abs/2002.07994 (2020) - [i8]Aadirupa Saha, Pierre Gaillard, Michal Valko:
Improved Sleeping Bandits with Stochastic Actions Sets and Adversarial Rewards. CoRR abs/2004.06248 (2020) - [i7]Aadirupa Saha, Tomer Koren, Yishay Mansour:
Adversarial Dueling Bandits. CoRR abs/2010.14563 (2020)
2010 – 2019
- 2019
- [c11]Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya:
How Many Pairwise Preferences Do We Need to Rank a Graph Consistently? AAAI 2019: 4830-4837 - [c10]Aadirupa Saha, Aditya Gopalan:
Active Ranking with Subset-wise Preferences. AISTATS 2019: 3312-3321 - [c9]Aadirupa Saha, Aditya Gopalan:
PAC Battling Bandits in the Plackett-Luce Model. ALT 2019: 700-737 - [c8]Aadirupa Saha, Aditya Gopalan:
Combinatorial Bandits with Relative Feedback. NeurIPS 2019: 983-993 - [c7]Aadirupa Saha, Shreyas Sheshadri, Chiranjib Bhattacharyya:
Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits. UAI 2019: 595-605 - [i6]Aadirupa Saha, Aditya Gopalan:
Regret Minimisation in Multinomial Logit Bandits. CoRR abs/1903.00543 (2019) - [i5]Aadirupa Saha, Aditya Gopalan:
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model. CoRR abs/1903.00558 (2019) - 2018
- [c6]Siddharth Barman, Aditya Gopalan, Aadirupa Saha:
Online Learning for Structured Loss Spaces. AAAI 2018: 2696-2703 - [c5]Aadirupa Saha, Aditya Gopalan:
Battle of Bandits. UAI 2018: 805-814 - [i4]Aditya Gopalan, Aadirupa Saha:
PAC-Battling Bandits with Plackett-Luce: Tradeoff between Sample Complexity and Subset Size. CoRR abs/1808.04008 (2018) - [i3]Aadirupa Saha, Aditya Gopalan:
Active Ranking with Subset-wise Preferences. CoRR abs/1810.10321 (2018) - [i2]Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya:
How Many Pairwise Preferences Do We Need to Rank A Graph Consistently? CoRR abs/1811.02161 (2018) - 2017
- [i1]Siddharth Barman, Aditya Gopalan, Aadirupa Saha:
Online Learning for Structured Loss Spaces. CoRR abs/1706.04125 (2017) - 2015
- [c4]Harikrishna Narasimhan, Harish G. Ramaswamy, Aadirupa Saha, Shivani Agarwal:
Consistent Multiclass Algorithms for Complex Performance Measures. ICML 2015: 2398-2407 - 2014
- [c3]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: 93-98 - 2013
- [c2]Amrita Ghosal, Aadirupa Saha, Sipra Das Bit:
Energy Saving Replay Attack Prevention in Clustered Wireless Sensor Networks. PAISI 2013: 82-96 - 2011
- [c1]Subir Halder, Amrita Ghosal, Aadirupa Saha, DasBit Sipra:
Energy-Balancing and Lifetime Enhancement of Wireless Sensor Network with Archimedes Spiral. UIC 2011: 420-434
Coauthor Index
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