Banghua Zhu
16 papers · 2021–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge π§ Keyword Pioneer π Renaissance Researcher (6) πΊοΈ Taxonomy Completionist (26)
π
Conference Polyglot
(6)
π
Cross-Pollinator
(11)
π
Century Club
(16)
β‘
Prolific Year
(6)
Conferences
ICML (6)
ICLR (4)
NIPS (3)
AISTATS (1)
ALT (1)
OSDI (1)
Top co-authors
Research topics
Keywords
imitation learning
(2)
policy learning
(2)
sample complexity
(2)
reinforcement learning
(1)
offline reinforcement learning
(1)
model selection
(1)
reward modeling
(1)
reinforcement learning from human feedback
(1)
game theory
(1)
object detection
(1)
robust statistics
(1)
inverse reinforcement learning
(1)
distributed learning
(1)
autonomous driving
(1)
online learning
(1)
semi-supervised learning
(1)
image classification
(1)
regret minimization
(1)
algorithm optimization
(1)
federated learning
(1)
Papers
Noisy Computing of the Threshold Function
ALT 2025
From Crowdsourced Data to High-quality Benchmarks: Arena-Hard and Benchbuilder Pipeline
ICML 2025
Taming Overconfidence in LLMs: Reward Calibration in RLHF
ICLR 2025
How to Evaluate Reward Models for RLHF
ICLR 2025
Fairness in Serving Large Language Models
OSDI 2024
The Effective Horizon Explains Deep RL Performance in Stochastic Environments
ICLR 2024
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains
ICLR 2024
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
ICML 2024
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF
ICML 2024
Doubly-Robust Self-Training
NIPS 2023
Online Learning in Stackelberg Games with an Omniscient Follower
ICML 2023
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
ICML 2023
Jump-Start Reinforcement Learning
ICML 2023
Towards Optimal Caching and Model Selection for Large Model Inference
NIPS 2023
Byzantine-Robust Federated Learning with Optimal Statistical Rates
AISTATS 2023
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
NIPS 2021