Scholar
Chicheng Zhang
Google Scholar ID: 29B3BAgAAAAJ
University of Arizona
Interactive machine learning
Learning theory
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Citations & Impact
All-time
Citations
1,864
H-index
19
i10-index
21
Publications
20
Co-authors
37
list available
Contact
GitHub
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Publications
7 items
Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Bandits
2026
Cited
0
Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measure
2025
Cited
0
Physics-Informed Parametric Bandits for Beam Alignment in mmWave Communications
2025
Cited
0
Towards Fundamental Limits for Active Multi-distribution Learning
2025
Cited
0
Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM
2025
Cited
0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leiblier Maillard Sampling
2025
Cited
0
Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear Bandits
2025
Cited
0
Resume (English only)
Background
Assistant Professor at the Department of Computer Science, University of Arizona
Research interests lie in the theory and applications of machine learning, especially interactive machine learning
Primary research areas include active learning, contextual bandits, reinforcement learning, and imitation learning
Focuses on designing and analyzing interactive learning algorithms with guarantees on data efficiency, computational efficiency, and robustness
Aims to identify new and natural interaction mechanisms beneficial to learning agents
Co-authors
37 total
John Langford
Microsoft Research New York
Kamalika Chaudhuri
FAIR @ Meta
Akshay Krishnamurthy
University of Massachusetts Amherst
Alekh Agarwal
Google
Co-author 5
Co-author 6
Kwang-Sung Jun
The University of Arizona
Aleksandrs Slivkins
Senior Principal Researcher, Microsoft Research NYC
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