Aadirupa Saha
38 papers · 2015–2025 · 8 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π§ Keyword Pioneer π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (15) π Conference Polyglot (8)
πΊοΈ
Taxonomy Completionist
(15)
π§
Keyword Pioneer
π
Grand Slam
π¬
Deep Specialist
(18)
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(127)
β‘
Prolific Year
(5)
π
Conference Pioneer
π
Century Club
(38)
π₯
Unstoppable
(7)
β
The Questioner
Conferences
ICML (13)
AISTATS (9)
NIPS (5)
ALT (3)
ICLR (3)
UAI (3)
AAAI (1)
ACML (1)
Top co-authors
Keywords
preference learning
(12)
regret bound
(11)
online learning
(10)
dueling bandit
(8)
regret minimization
(7)
multi-armed bandit
(7)
sample complexity
(5)
pac learning
(5)
preference feedback
(3)
stochastic optimization
(3)
online convex optimization
(3)
convex optimization
(3)
contextual bandit
(3)
active learning
(3)
subset selection
(3)
bandit convex optimization
(2)
zeroth-order optimization
(2)
online algorithm
(2)
plackett-luce model
(2)
dynamic regret
(2)
Papers
Finally Rank-Breaking Conquers MNL Bandits: Optimal and Efficient Algorithms for MNL Assortment
ICLR 2025
Dueling Convex Optimization with General Preferences
ICML 2025
Tracking The Best Expert Privately
ICML 2025
A Graph Theoretic Approach for Preference Learning with Feature Information
UAI 2024
Strategic Linear Contextual Bandits
NIPS 2024
Faster Convergence with MultiWay Preferences
AISTATS 2024
On the Vulnerability of Fairness Constrained Learning to Malicious Noise
AISTATS 2024
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources
AISTATS 2024
Dueling Optimization with a Monotone Adversary
ALT 2024
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation
ICLR 2024
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling
ICLR 2024
Dueling RL: Reinforcement Learning with Trajectory Preferences
AISTATS 2023
One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits
AISTATS 2023
Federated Online and Bandit Convex Optimization
ICML 2023
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
NIPS 2023
ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits
AISTATS 2023
Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits
AISTATS 2022
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models
ICML 2022
Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits
ICML 2022
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability
ALT 2022
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences
ICML 2022
Dueling Bandits with Adversarial Sleeping
NIPS 2021
Strategically efficient exploration in competitive multi-agent reinforcement learning
UAI 2021
Confidence-Budget Matching for Sequential Budgeted Learning
ICML 2021
Adversarial Dueling Bandits
ICML 2021
Dueling Convex Optimization
ICML 2021
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization
ICML 2021
Optimal Algorithms for Stochastic Contextual Preference Bandits
NIPS 2021
Best-item Learning in Random Utility Models with Subset Choices
AISTATS 2020
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
ICML 2020
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
ICML 2020
Polytime Decomposition of Generalized Submodular Base Polytopes with Efficient Sampling
ACML 2020
How Many Pairwise Preferences Do We Need to Rank a Graph Consistently?
AAAI 2019
PAC Battling Bandits in the Plackett-Luce Model
ALT 2019
Active Ranking with Subset-wise Preferences
AISTATS 2019
Combinatorial Bandits with Relative Feedback
NIPS 2019
Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits
UAI 2019
Consistent Multiclass Algorithms for Complex Performance Measures
ICML 2015