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Hongyang R. Zhang Assistant professor of Computer Science ho.zhang@northeastern.edu |
Hi! I'm Hongyang (Ryan) Zhang. My research interests lie in the intersection of machine learning, algorithms, and theory. Current focus includes how to learn from limited data using data augmentation, multi-task and transfer learning, theory of neural networks, and large-scale graph algorithms.
I received my Ph.D. in Computer Science from Stanford University, advised by Ashish Goel and Greg Valiant.
I did my undergraduate study in Computer Science from the ACM Honored Class at Shanghai Jiao Tong University.
Here is my resume and Google Scholar profile.
Recent Updates
Mar 2021: Thanks to the Khoury College of Computer Sciences for awarding me my first ever research grant! Joint w/ Huy Nguyen, we'll be thinking about algorithmic fairness and bias questions in ML.
Jan 2021: Teaching Advanced Machine Learning this semester!
Sep 2020: I'm teaching an advanced course in deep learning this semester! Feedback more than welcome.
July 2020: At COLT’20, we show an interesting dynamic of gradient descent for learning over-parametrized two-layer ReLU neural nets. We found that gradient descent first learns lower-order tensors/moments and then learns higher-order tensors/moments (Yuanzhi's video)!
May 2020: At ICML’20, we describe a formal analysis of data augmentation. Inspired by the analysis, we propose an uncertainty-based sampling scheme that achieves SoTA results (Sen's code release)!
Feb 2020: Blog posts that illustrate our recent progress on multi-task learning and data augmentation! The data augmentation blog post is part of Sharon Y. Li's expository series about exciting recent progress in this direction.
Sharp Bias-variance Tradeoffs of Hard Parameter Sharing in High-dimensional Linear Regression
with Fan Yang, Sen Wu, Weijie Su, and Christopher Ré
(asterisk indicates alphabetical authorship or equal contribution)
Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK
with Yuanzhi Li and Tengyu Ma
Conference on Learning Theory (COLT) 2020
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]
Understanding and Improving Information Transfer in Multi-Task Learning
Sen Wu*, H. R. Zhang* and Christopher Ré
International Conference on Learning Representations (ICLR) 2020
[blog] [video] [arxiv]
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)
Recovery Guarantees for Quadratic Tensors with Limited Observations
H. R. Zhang, Vatsal Sharan, Moses Charikar and Yingyu Liang
International Conference on AI and Statistics (AISTATS) 2019
Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations
with Yuanzhi Li and Tengyu Ma
Conference on Learning Theory (COLT) 2018 (Best Paper Award)
Approximate Personalized PageRank on Dynamic Graphs
H. R. Zhang, Peter Lofgren and Ashish Goel
KDD 2016 (oral presentation) [code]
Incentives for Strategic Behavior in Fisher Market Games
with Ning Chen, Xiaotie Deng, Bo Tang
AAAI 2016
A Note on Modeling Retweet Cascades on Twitter
with Ashish Goel, Kamesh Munagala, Aneesh Sharma
Workshop on Algorithms and Models for the Web Graph (WAW) 2015
Connectivity in Random Forests and Credit Networks
with Ashish Goel, Sanjeev Khanna, Sharath Raghvendra
Symposium on Discrete Algorithms (SODA) 2015
Computing the Nucleolus of Matching, Cover and Clique Games
with Ning Chen and Pinyan Lu
AAAI 2012 (oral presentation)
Incentive Ratios of Fisher Markets
with Ning Chen, Xiaotie Deng, Jie Zhang
International Colloquium on Automata, Languages, and Programming (ICALP) 2012
On Strategy-proof Allocation without Payments or Priors
with Li Han, Chunzhi Su, Linpeng Tang
Conference on Web and Internet Economics (WINE) 2011
When Multi-Task Learning Works – And When It Doesn’t
based on joint work with Sen Wu and Chris Ré.
Automating the Art of Data Augmentation – Part III Theory
edited with Sharon Y. Li and Chris Ré.
CS 7140, Advanced Machine Learning, Spring 2021
CS 7180, Special Topics in AI: Algorithmic and Statistical Aspects of Deep Learning, Fall 2020
My PhD thesis has studied two broad sets of questions:
Generalization in over-parameterized models: Non-convex methods are the method of choice for training most ML models in practice. How much data is needed to train a model that generalizes well to unseen data?
Algorithms for large-scale social networks: Dealing with large-scale social network data requires methods that scale to tens of millions of users. In the meantime, real world social networks admit structural properties. How do we exploit their structures to provide better algorithms and guarantees?
My Chinese name is 张泓洋 written in Chinese characters.