Machine Learning Theory & Algorithms
Multitask and Transfer Learning
Social networks
Others
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
[ICML Updatable ML Workshop 2022]
[code]
Analysis of Information Transfer from Heterogeneous Sources via Precise High-dimensional Asymptotics
Fan Yang, H.R. Zhang, Sen Wu, Weijie Su, and Christopher Ré
Working paper, 2021
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
[code]
[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 Limited 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
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
[NeurIPS DistShift Workshop 2021]
[ICML UDL Workshop 2021]
Improved Regularization and Robustness for Fine-tuning in Neural Networks
Dongyue Li and H. R. Zhang
Neural Information Processing Systems (NeurIPS) 2021
[code]
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]
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.
[International workshop on Epidemiology meets Data Mining
and Knowledge discovery (epiDAMIK) 2022]
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
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
Remark: Some of my papers, marked by an asterisk, use an alphabetical ordering of author names.