Haipeng Luo
IBM Early Career Chair and Associate Professor of Computer Science
Researches online learning, bandits and reinforcement learning theory.
Haipeng Luo's lab at USC works on learning theory & optimization and reinforcement learning. 47 papers since 2023, 43 at top venues, mostly NeurIPS and COLT. 5 PhD students. 8 alumni, most often to Microsoft. Applications close .
At a glance
- papers, 2023–2026
- 12/ year
- at T1 venues
- 91%
- PhD students
- 5
- MS students
- 0
- alumni listed
- 8
Papers by yearfrom the lab homepage, 2023–2026
- 15 papers2026 · T1 13 · collecting
- 12 papers2025 · T1 11
- 11 papers2024 · T1 11
- 9 papers2023 · T1 8
T1 venuesOtherVenues: NeurIPS, COLT, ICML, AISTATS, ALT
Research focuspaper titles that use each topic's words
- Learning theory & optimization21 papers
- Reinforcement learning15 papers
- online8 papers
- bandits7 papers
- online learning5 papers
Paper list
From the lab homepage.
202615 papers
- NeurIPS (T1)Accelerating Min-Max Optimization via Power-Law Stepsizes (opens in a new tab)
- NeurIPS (T1)Adaptive Calibration in Non-Stationary Environments (opens in a new tab)
- COLT (T1)Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret (opens in a new tab)
- IEEE/ACM Transactions on NetworkingAutomatic Link Selection in Multi-Channel Multiple Access with Link Failures (opens in a new tab)
- COLT (T1)Calibeating Made Simple (opens in a new tab)
- COLT (T1)Efficient Swap Multicalibration of Elicitable Properties (opens in a new tab)
- NeurIPS (T1)Group Distributionally Robust Optimization with Flexible Sample Queries (opens in a new tab)
- EC (T1)Is Online Linear Optimization Sufficient for Strategic Robustness? (opens in a new tab)
- NeurIPS (T1)Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions (opens in a new tab)
- ICML (T1)One Good Source is All You Need: Near-Optimal Regret for Bandits under Heterogeneous Noise (opens in a new tab)
- COLT (T1)Online Learning for Uninformed Markov Games: Empirical Nash-Value Regret and Non-Stationarity Adaptation (opens in a new tab)
- STOC (T1)Proximal Regret and Proximal Correlated Equilibria: A New Tractable Solution Concept for Online Learning and Games (opens in a new tab)
202512 papers
- NeurIPS (T1)Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback (opens in a new tab)
- COLT (T1)Alternating Regret for Online Convex Optimization (opens in a new tab)
- NeurIPS (T1)Comparator-Adaptive Φ-Regret: Improved Bounds, Simpler Algorithms, and Applications to Games (opens in a new tab)
- ICML (T1)Contextual Linear Bandits with Delay as Payoff (opens in a new tab)
- COLT (T1)Corrupted Learning Dynamics in Games (opens in a new tab)
- NeurIPS (T1)From Average-Iterate to Last-Iterate Convergence in Games: A Reduction and Its Applications (opens in a new tab)
- NeurIPS (T1)Improved Bounds for Swap Multicalibration and Swap Omniprediction (opens in a new tab)
- NeurIPS (T1)Improved Regret and Contextual Linear Extension for Pandora’s Box and Prophet Inequality (opens in a new tab)
- COLT (T1)Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback (opens in a new tab)
- ICLR (T1)Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games (opens in a new tab)
- NeurIPS DynaFront WorkshopOn Separation Between Best-Iterate, Random-Iterate, and Last-Iterate Convergence of Learning in Games (opens in a new tab)
- NeurIPS (T1)Simultaneous Swap Regret Minimization via KL-Calibration (opens in a new tab)
202411 papers
- ICML (T1)ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints (opens in a new tab)
- NeurIPS (T1)Contextual Multinomial Logit Bandits with General Value Functions (opens in a new tab)
- ICML (T1)Efficient Contextual Bandits with Uninformed Feedback Graphs (opens in a new tab)
- NeurIPS (T1)Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms (opens in a new tab)
- AISTATS (T1)Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games (opens in a new tab)
- ICML (T1)Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback (opens in a new tab)
- NeurIPS (T1)No-Regret Learning for Fair Multi-Agent Social Welfare Optimization (opens in a new tab)
- AISTATS (T1)Online Learning in Contextual Second-Price Pay-Per-Click Auctions (opens in a new tab)
- NeurIPS (T1)Optimal Multiclass U-Calibration Error and Beyond (opens in a new tab)
- NeurIPS (T1)Provably Efficient Interaction-Grounded Learning with Personalized Reward (opens in a new tab)
- NeurIPS (T1)Tractable Local Equilibria in Non-Concave Games (opens in a new tab)
20239 papers
- NeurIPS (T1)Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms (opens in a new tab)
- ALTImproved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs (opens in a new tab)
- AISTATS (T1)No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution (opens in a new tab)
- NeurIPS (T1)No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions (opens in a new tab)
- UAI (T1)Posterior Sampling-based Online Learning for the Stochastic Shortest Path Model (opens in a new tab)
- NeurIPS (T1)Practical Contextual Bandits with Feedback Graphs (opens in a new tab)
- ICML (T1)Refined Regret for Adversarial MDPs with Linear Function Approximation (opens in a new tab)
- NeurIPS (T1)Regret Matching+: (In)Stability and Fast Convergence in Games (opens in a new tab)
- NeurIPS (T1)Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games (opens in a new tab)
Where alumni went8 listed, 20% to industry
Industry 1Academia 4
Most often to
- Microsoft
Also to
- Cornell University
- UCLA
- University of Iowa
Apply to USCFall 2027
Computer Science PhD (Viterbi)
- Round
- Single round (Dec 15)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Confirmed on the program page.
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