Mengxiao Zhang
22 papers · 2020–2026 · 8 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+9 more ↓ Show less ↑
🏃 Academic Marathon (5) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🐝 Cross-Pollinator (11)
🌍
Conference Polyglot
(7)
🏃
Academic Marathon
(5)
🌈
Renaissance Researcher
(5)
🤝
Dynamic Duo
(18)
🏆
Grand Slam
🔥
Unstoppable
(6)
💎
Century Club
(21)
🗃️
Keyword Collector
(74)
⚡
Prolific Year
(6)
Conferences
COLT (6)
ICML (5)
NIPS (5)
AISTATS (2)
AAAI (1)
ALT (1)
ICLR (1)
UAI (1)
Top co-authors
Research topics
Keywords
regret bound
(11)
online learning
(7)
adversarial bandit
(3)
multi-armed bandit
(3)
contextual bandit
(3)
online mirror descent
(2)
nash equilibrium
(2)
linear bandit
(2)
self-concordant barrier
(2)
feedback graph
(2)
no-regret learning
(2)
zero-sum game
(2)
multi-agent system
(2)
online convex optimization
(1)
value function
(1)
distributed learning
(1)
markov decision process
(1)
game theory
(1)
adversarial learning
(1)
mechanism design
(1)
Papers
Decentralized Online Convex Optimization with Unknown Feedback Delays
AAAI 2026
Alternating Regret for Online Convex Optimization
COLT 2025
Exploiting Curvature in Online Convex Optimization with Delayed Feedback
ICML 2025
Contextual Linear Bandits with Delay as Payoff
ICML 2025
Provably Efficient Interactive-Grounded Learning with Personalized Reward
NIPS 2024
Online Learning in Contextual Second-Price Pay-Per-Click Auctions
AISTATS 2024
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
COLT 2024
Efficient Contextual Bandits with Uninformed Feedback Graphs
ICML 2024
Contextual Multinomial Logit Bandits with General Value Functions
NIPS 2024
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
NIPS 2024
Practical Contextual Bandits with Feedback Graphs
NIPS 2023
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
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits
COLT 2022
Adaptive Bandit Convex Optimization with Heterogeneous Curvature
COLT 2022
No-Regret Learning in Time-Varying Zero-Sum Games
ICML 2022
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
ICLR 2021
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
COLT 2021
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
ICML 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
NIPS 2020
Selling Data at an Auction under Privacy Constraints
UAI 2020