AAAI 2025 accepted papers on Bandits and online learning
31 of the 3,486 papers accepted at AAAI 2025 (0.9%) are on Bandits and online learning, compared with 1.2% at AAAI 2024. They fall into 4 topics.
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Topics and papers
Multi-armed bandits · 13 papers
- Quantum Best Arm Identification with Quantum Oracles (2 citations)
- Robust Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents (2 citations)
- Heterogeneous Multi-Agent Bandits with Parsimonious Hints (1 citation)
- Multiple Trade-offs: An Improved Approach for Lexicographic Linear Bandits (1 citation)
- Nearly Tight Bounds for Exploration in Streaming Multi-Armed Bandits with Known Optimality Gap (1 citation)
- Replication-proof Bandit Mechanism Design with Bayesian Agents (1 citation)
- The Bandit Whisperer: Communication Learning for Restless Bandits (1 citation)
- Batch Ensemble for Variance Dependent Regret in Stochastic Bandits
- Delay as Payoff in MAB
- Efficient Graph Bandit Learning with Side-Observations and Switching Constraints
- Linear Streaming Bandit: Regret Minimization and Fixed-Budget Epsilon-Best Arm Identification
- On the Asymptotic Optimality of Confidence Interval Based Algorithms for Fixed Confidence MABs
and 1 more in the interactive map.
Online convex optimization and games · 11 papers
- Doubly-Bounded Queue for Constrained Online Learning: Keeping Pace with Dynamics of Both Loss and Constraint (3 citations)
- Online Learning of Coalition Structures by Selfish Agents (2 citations)
- Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control (1 citation)
- Revisiting Projection-Free Online Learning with Time-Varying Constraints (1 citation)
- Adaptive Market Making with Inventory Constraints via Online Learning
- Augmenting Online Algorithms for Knapsack Problem with Total Weight Information
- Improved Bounds for Online Facility Location with Predictions
- Improved Regret Bounds for Online Fair Division with Bandit Learning
- Non-stochastic Budgeted Online Pricing with Semi-Bandit Feedback
- Online Nonsubmodular Optimization with Delayed Feedback in the Bandit Setting
- Safe Online Convex Optimization with Heavy-Tailed Observation Noises