Mengxiao Zhang
23 papers · 2020–2026 · 8 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (29) π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Dynamic Duo
(18)
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Century Club Β· Bronze
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Keyword Collector
(102)
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Unstoppable Β· Bronze
Top co-authors
Research topics
Keywords
regret bound
(12)
online learning
(7)
adversarial bandit
(4)
contextual bandit
(4)
linear bandit
(3)
feedback graph
(3)
multi-armed bandit
(3)
online convex optimization
(3)
nash equilibrium
(3)
multi-agent system
(2)
high probability bound
(2)
online newton step
(2)
last-iterate convergence
(2)
self-concordant barrier
(2)
zero-sum game
(2)
delayed feedback
(2)
no-regret learning
(2)
online mirror descent
(2)
value function
(1)
online advertising
(1)
Papers
Decentralized Online Convex Optimization with Unknown Feedback Delays
AAAI 2026
Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach
COLT 2026
Alternating Regret for Online Convex Optimization
COLT 2025
Contextual Linear Bandits with Delay as Payoff
ICML 2025
Exploiting Curvature in Online Convex Optimization with Delayed Feedback
ICML 2025
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
COLT 2024
Contextual Multinomial Logit Bandits with General Value Functions
NeurIPS 2024
Efficient Contextual Bandits with Uninformed Feedback Graphs
ICML 2024
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
NeurIPS 2024
Online Learning in Contextual Second-Price Pay-Per-Click Auctions
AISTATS 2024
Provably Efficient Interactive-Grounded Learning with Personalized Reward
NeurIPS 2024
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
ALT 2023
No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution
AISTATS 2023
Practical Contextual Bandits with Feedback Graphs
NeurIPS 2023
Adaptive Bandit Convex Optimization with Heterogeneous Curvature
COLT 2022
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits
COLT 2022
No-Regret Learning in Time-Varying Zero-Sum Games
ICML 2022
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
ICML 2021
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
COLT 2021
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
ICLR 2021
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
COLT 2020
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
NeurIPS 2020
Selling Data at an Auction under Privacy Constraints
UAI 2020