About Me

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.

You can reach me by email at chichengz at cs dot arizona dot edu.

To prospective PhD students: you are welcome to apply to our CS or Applied Math, or Statistics PhD programs. I am mostly looking for self-motivated students with solid math background and/or theoretical research experience - please feel free to send me an email if you think there is a match between our research interests.

Research

My research interests lie in theory and applications of machine learning. I primarily work on interactive learning (e.g. active learning, contextual bandits, etc), where learning algorithms are involved in data collection processes. Specifically, I am interested in:

designing and analyzing interactive learning algorithms that have data-efficiency, computational efficiency, and robustness guarantees, as well as

identifying new interaction models which learning algorithms can benefit from.

I am also interested in topics in unsupervised learning, as well as quantifying and utilizing confidence and uncertainty in machine learning.

Publications

Preprints

• Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise.
  Chicheng Zhang and Yinan Li.
  [arXiv]

Conferences

• Multitask Bandit Learning through Heterogeneous Feedback Aggregation.
  Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, and Kamalika Chaudhuri.
  AISTATS 2021.
  [arXiv]

• Active Online Learning with Hidden Shifting Domains.
  Yining Chen, Haipeng Luo, Tengyu Ma, and Chicheng Zhang.
  AISTATS 2021.
  [arXiv]

• Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance.
  Jie Shen and Chicheng Zhang.
  ALT 2021.
  [arXiv]

• Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality.
  Kwang-Sung Jun and Chicheng Zhang.
  NeurIPS 2020.
  [arXiv]

• Efficient Contextual Bandits with Continuous Actions.
  Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, and Aleksandrs Slivkins.
  NeurIPS 2020.
  [arXiv]

• Efficient Active Learning of Sparse Halfspaces with Arbitrary Bounded Noise.
  Chicheng Zhang, Jie Shen, and Pranjal Awasthi.
  NeurIPS 2020 (oral presentation).
  [arXiv]

• Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.
  Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford and Alekh Agarwal.
  ICLR 2020 (talk).
  [arXiv] [code]

• Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting.
  Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins and Chicheng Zhang.
  COLT 2019.
  [arXiv]

• Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback.
  Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, and Sahand N Negahban.
  ICML 2019.
  [arXiv] [code]

• Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case.
  Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, and Chicheng Zhang.
  ICML 2019.
  [arXiv]

• Efficient Active Learning of Sparse Halfspaces.
  Chicheng Zhang.
  COLT 2018.
  [arXiv]

• Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces.
  Songbai Yan and Chicheng Zhang.
  NeurIPS 2017.
  [arXiv] [poster]

• Efficient Online Bandit Multiclass Learning with \( \tilde O(\sqrt{T}) \) Regret.
  Alina Beygelzimer, Francesco Orabona, and Chicheng Zhang.
  ICML 2017.
  [arXiv]

• Search Improves Label for Active Learning.
  Alina Beygelzimer, Daniel Hsu, John Langford, and Chicheng Zhang.
  NeurIPS 2016.
  [arXiv]

• The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions.
  Chicheng Zhang and Kamalika Chaudhuri.
  COLT 2016.
  [arXiv]

• Active Learning from Weak and Strong Labelers.
  Chicheng Zhang and Kamalika Chaudhuri.
  NeurIPS 2015.
  [arXiv]

• Spectral Learning of Large Structured HMMs for Comparative Epigenomics.
  Chicheng Zhang, Jimin Song, Kevin C. Chen, and Kamalika Chaudhuri.
  NeurIPS 2015.
  [arXiv] [code]

• Beyond Disagreement-based Agnostic Active Learning.
  Chicheng Zhang and Kamalika Chaudhuri.
  NeurIPS 2014 (spotlight presentation).
  [pdf] [arXiv]

Workshop Contributions

• A Potential-based Framework for Online Learning with Mistakes and Abstentions.
  Chicheng Zhang and Kamalika Chaudhuri.
  NeurIPS 2016 workshop on Reliable Machine Learning in the Wild.
  [pdf]

• Search Improves Label for Active Learning.
  Alina Beygelzimer, Daniel Hsu, John Langford and Chicheng Zhang.
  ICML Workshop on Data Efficient Machine Learning 2016.
  [pdf]

• Active Learning from Weak and Strong Labelers.
  Chicheng Zhang and Kamalika Chaudhuri.
  ICML Active Learning Workshop 2015.
  [pdf]

• Improved Algorithms for Confidence-Rated Prediction with Error Guarantees.
  Kamalika Chaudhuri and Chicheng Zhang.
  NeurIPS 2013 Workshop on Learning Faster from Easy Data.
  [pdf]

Teaching

• CSC 588: Machine Learning Theory. Spring 2021.
  [Class website]

• CSC 665 Section 2: Machine Learning Theory. Fall 2019.
  [Class website]

Thesis

• Active Learning and Confidence-rated Prediction.
  Chicheng Zhang.
  PhD Thesis, UCSD, 2017.
  [Local version (with typos fixed)] [UC eScholarship]