Chicheng Zhang

Member of the Graduate Faculty | Assistant Professor, Computer Science

About

Hello! I am an assistant professor at the Computer Science Department, the University of Arizona. From September 2017 to June 2019, I was a postdoctoral researcher at the machine learning grouof Microsoft Research NYC. I got my PhD in Computer Science at UCSD, where I was lucky to have Prof. Kamalika Chaudhuri as my advisor. Before this, I was an undergraduate student at Peking University, studying learning theory with Prof. Liwei Wang.

Research Area

machine learning, active learning, optimization, artificial intelligence, reinforcement learning, data labeling, bandit algorithms, spectral learning, algorithm, online learning

Recent

Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance

2021

machine learning, robustness, noise tolerance, data privacy, optimization

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

2021

multi-armed bandits, reinforcement learning, feedback aggregation, multi-task learning, heterogeneous data

Provably Efficient Multi-Task Reinforcement Learning with Model Transfer

2021

reinforcement learning, model transfer, multi-task learning, efficient learning, provably efficient learning

Active Online Learning with Hidden Shifting Domains

2021

active learning, domain adaptation, online learning, hidden domains, machine learning

Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise

2021

machine learning, algorithm, active learning, noise, efficiency

Efficient active learning of sparse halfspaces with arbitrary bounded noise

2020

active learning, sparse data, halfspaces, noise bound, efficiency

Efficient Contextual Bandits with Continuous Actions

2020

reinforcement learning, decision making, optimization, machine learning, artificial intelligence

Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality.

2020

bandit algorithms, optimization, decision making, machine learning, algorithmic theory

DeeBatch Active Learning by Diverse, Uncertain Gradient Lower Bounds

2020

machine learning, data analysis, optimization, algorithms, artificial intelligence

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

2019

reinforcement learning, contextual bandits, supervised learning, machine learning, data efficiency

Page 1 of 4

Undergrad Research Opp Volunteer, For Credit
Prerequisite Courses: undergraduate probability and statistics, basic programming, algorithm design Majors: Computer science, data science, math, physics, statistics
Undergrad Research Opp Volunteer, For Credit
Prerequisite Courses: undergraduate probability and statistics, basic programming, algorithm design Majors: Computer science, data science, math, physics, statistics

Advanced Topics in Computational Intelligence
Machine Learning Theory
Principles of Data Science
Principles of Machine Learning

Interactive Deep Learning for ECG Monitoring
2020·Technology

Chicheng Zhang

About

Research Area

Grants(1)

Publications(21)

Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

Provably Efficient Multi-Task Reinforcement Learning with Model Transfer

Active Online Learning with Hidden Shifting Domains

Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise

Efficient active learning of sparse halfspaces with arbitrary bounded noise

Efficient Contextual Bandits with Continuous Actions

Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality.

DeeBatch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Collaborations(19)

Hao Zhang

Rongguang Liang

Ruth Taylor-Piliae

Larry Head

Michael Chertkov

Opportunities(2)

Undergrad Research Opp Volunteer, For Credit

Undergrad Research Opp Volunteer, For Credit

Courses(4)

Advanced Topics in Computational Intelligence

Machine Learning Theory

Principles of Data Science

Principles of Machine Learning

Techs/Patents(1)

Interactive Deep Learning for ECG Monitoring