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 group of 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, bandit algorithms, reinforcement learning, data labeling, spectral learning, artificial intelligence, algorithms, online learning

Recent

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

2021

machine learning, algorithms, active learning, noise, statistical learning

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

reinforcement learning, bandit algorithms, multi-task learning, feedback aggregation, heterogeneous data

Active Online Learning with Hidden Shifting Domains

2021

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

Provably Efficient Multi-Task Reinforcement Learning with Model Transfer

2021

reinforcement learning, multi-task learning, model transfer, efficiency, provability

DeeBatch Active Learning by Diverse, Uncertain Gradient Lower Bounds

2020

active learning, gradient descent, uncertainty estimation, machine learning

Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality.

2020

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

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

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)

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

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

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

Active Online Learning with Hidden Shifting Domains

Provably Efficient Multi-Task Reinforcement Learning with Model Transfer

DeeBatch Active Learning by Diverse, Uncertain Gradient Lower Bounds

Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality.

Efficient active learning of sparse halfspaces with arbitrary bounded noise

Efficient Contextual Bandits with Continuous Actions

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

Collaborations(18)

Hao Zhang

Ruth Taylor-Piliae

Rongguang Liang

Michael Chertkov

Larry Head

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