Topics

Please kindly note that the following is only a very rough division. It is very likely that a paper touches on several topics, but hopefully the following provides a first-order approximation.

• Algorithms, Learning theory, Matrix and tensor methods

• Multitask learning, Fine-tuning, Foundation models

• Dealing with social data (broadly, I refer to data generated according to human activities and interactions), examples include social networks and transportation networks.

Asterisk marks alphabetical authorship or equal contribution between authors.

Publications by Topic

Algorithms, Learning theory, Matrix and tensor methods

• Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
  Haotian Ju, Dongyue Li, Aneesh Sharma, and H. R. Zhang
  Artificial Intelligence and Statistics (AISTATS) 2023

• Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
  Haotian Ju, Dongyue Li, and H. R. Zhang
  International Conference on Machine Learning (ICML) 2022
  

• On the Generalization Effects of Linear Transformations in Data Augmentation
  Sen Wu*, H. R. Zhang*, Gregory Valiant, and Christopher Ré
  International Conference on Machine Learning (ICML) 2020
  [blog: Automating the Art of Data Augmentation – Part III Theory]

• Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK
  Yuanzhi Li, Tengyu Ma, and H. R. Zhang*
  Conference on Learning Theory (COLT) 2020
  

• Algorithms and Generalization for Large-scale Matrices and Tensors
  Thesis Ph.D. Stanford University 2019
  H. R. Zhang

• Recovery Guarantees for Quadratic Tensors with Sparse Observations
  H. R. Zhang, Vatsal Sharan, Moses Charikar, and Yingyu Liang
  Artificial Intelligence and Statistics (AISTATS) 2019

• Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations
  Yuanzhi Li, Tengyu Ma, and H. R. Zhang*
  Conference on Learning Theory (COLT) 2018. Best Paper Award

Multitask learning, Fine-tuning, Foundation models

• Improved Group Robustness via Classifier Retraining on Independent Splits
  Thien Hang Nguyen, H. R. Zhang, and Huy L. Nguyen
  Transactions on Machine Learning Research (TMLR) 2023

• Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
  Dongyue Li, Haotian Ju, Aneesh Sharma, and H. R. Zhang
  SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023

• Identification of Negative Transfers in Multitask Learning using Surrogate Models
  Dongyue Li, Huy L. Nguyen, and H. R. Zhang
  Transactions on Machine Learning Research (TMLR) 2023. Featured Certification

• Task Modeling: Approximating Multitask Predictions for Cross-task Transfer
  Dongyue Li, Huy L. Nguyen, and H. R. Zhang
  NeurIPS Workshop on Distribution Shifts (2022)

• Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations
  Michael Zhang, Nimit Sohoni, H. R. Zhang, Chelsea Finn, and Christopher Ré
  International Conference on Machine Learning (ICML) 2022. Long presentation
  

• Improved Regularization and Robustness for Fine-tuning in Neural Networks
  Dongyue Li and H. R. Zhang
  Neural Information Processing Systems (NeurIPS) 2021
  

• Observational Supervision for Medical Image Classification using Gaze Data
  Khaled Saab, Sarah Hooper, Nimit Sohoni, Jupinder Parmar, Brian Pogatchnik, Sen Wu, Jared Dunnmon, H. R. Zhang, Daniel Rubin, and Christopher Ré
  Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021
  

• Understanding and Improving Information Transfer in Multi-Task Learning
  Sen Wu*, H. R. Zhang*, and Christopher Ré
  International Conference on Learning Representations (ICLR) 2020
  [video]
  [blog: When Multi-Task Learning Works – And When It Doesn’t]
  

Dealing with social data such as social networks and transportation networks

• Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
  Abhinav Nippani, Dongyue Li, Haotian Ju, Haris N. Koutsopoulos, H. R. Zhang
  NeurIPS 2023, Datasets and Benchmarks Track

• Optimal Intervention on Weighted Networks via Edge Centrality
  Dongyue Li, Tina Eliassi-Rad, and H. R. Zhang
  SIAM International Conference on Data Mining (SDM) 2023

• Pruning based Distance Sketches with Provable Guarantees on Random Graphs
  H. R. Zhang, Huacheng Yu, and Ashish Goel
  The Web Conference (WWW) 2019. Oral presentation
  [code]

• Approximate Personalized PageRank on Dynamic Graphs
  H. R. Zhang, Peter Lofgren, and Ashish Goel
  KDD 2016. Oral presentation
  [code]

• A Note on Modeling Retweet Cascades on Twitter
  Ashish Goel, Kamesh Munagala, Aneesh Sharma, and H. R. Zhang*
  Workshop on Algorithms and Models for the Web Graph (WAW) 2015

• Connectivity in Random Forests and Credit Networks
  Ashish Goel, Sanjeev Khanna, Sharath Raghvendra, and H. R. Zhang*
  Symposium on Discrete Algorithms (SODA) 2015

Algorithmic Game Theory

During undergraduate I worked on Algorithmic Game Theory. Below are the papers from this period, including journal versions of earlier conference proceedings:

• Incentive Ratio: A Game Theoretical Analysis of Market Equilibria
  Ning Chen, Xiaotie Deng, Bo Tang, H. R. Zhang, and Jie Zhang
  Information and Computation, 2022

• Incentives for Strategic Behavior in Fisher Market Games
  Ning Chen, Xiaotie Deng, Bo Tang, and H. R. Zhang*
  AAAI 2016

• Computing the Nucleolus of Matching, Cover and Clique Games
  Ning Chen, Pinyan Lu, and H. R. Zhang*
  AAAI 2012. Oral presentation

• Incentive Ratios of Fisher Markets
  Ning Chen, Xiaotie Deng, H. R. Zhang*, and Jie Zhang
  International Colloquium on Automata, Languages, and Programming (ICALP) 2012

• On Strategy-proof Allocation without Payments or Priors
  Li Han, Chunzhi Su, Linpeng Tang, and H. R. Zhang*
  Conference on Web and Internet Economics (WINE) 2011