About
- I'm an engineer at FAIR in Meta. My current research focuses on generative models and reinforcement learning.
-
I received my PhD degree in CS at the University of Chicago in 2017. I
was very fortunate to be advised by Prof. John Lafferty.
During my PhD, I worked in the intersection of machine learning, optimization and statistics. My research centered around developing novel computationally efficient methods with theoretical guarantees for challenging machine learning problems, with an emphasis on finding exact solutions for nonconvex problems. - I worked as a postdoc researcher in the Statistics Department, Wharton School, University of Pennsylvania. I worked on differential privacy and statistical inference there.
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Prior to Penn, I was a research scientist in Facebook from 2017 to 2019.
Together with my teammates, I help Facebook build its distributed training system for Ads recommendation models. My work concentrates on distributed optimization algorithms and system architecture design. - Earlier, I got my master degree in Max Planck Institute for Informatics and my bachelor degree in Zhejiang University.
Papers
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Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces
DiJia Su, Sainaa Sukhbaatar, Michael Rabbat, Yuandong Tian, Qinqing Zheng
[paper] -
Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
Lucas Lehnert, Sainaa Sukhbaatar, DiJia Su, Qinqing Zheng, Paul Mcvay, Michael Rabbat, Yuandong Tian
COLM 2024
[paper] -
Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning
Zihan Ding, Amy Zhang, Yuandong Tian, Qinqing Zheng
ICLR 2024 Generative Models for Decision Making Workshop
[paper] -
Guided Flows for Generative Modeling and Decision Making
Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, Ricky T. Q. Chen
[paper] -
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
Harshit Sikchi, Qinqing Zheng, Amy Zhang, Scott Niekum
ICLR 2024 (Spotlight)
[paper] [code] -
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover
ICML 2023
[paper] [code] -
ConserWeightive Behavioral Cloning for Reliable Offline Reinforcement Learning
Tung Nguyen, Qinqing Zheng, Aditya Grover
NeurIPS 2022 Foundation Models for Decision Making Workshop
[paper] [code] -
Latent State Marginalization as a Low-cost Approach for Improving Exploration
Dinghuai Zhang, Aaron Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
ICLR 2023
[paper] -
Online Decision Transformer
Qinqing Zheng, Amy Zhang, Aditya Grover
ICML 2022 (Long Oral Presentation)
[paper] [code] [poster] -
Near-Optimal Confidence Sequences for Bounded Random Variables
Arun Kumar Kuchibhotla*, Qinqing Zheng* (*Equal contribution)
ICML 2021 (Spotlight)
[paper] [code] -
A Theorem of the Alternative for Personalized Federated Learning Shuxiao Chen, Qinqing Zheng, Qi Long, Weijie Su
Submitted.
[paper] -
Federated \(f\)-Differential Privacy Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie Su
AISTATS 2021
[paper] [code] -
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion Qinqing Zheng, Jinshuo Dong, Qi Long, Weijie Su
ICML 2020
[paper] [code] -
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training
Qinqing Zheng, Bor-Yiing Su, Jiyan Yang, Alisson Azzolini, Qiang Wu, Ou Jin, Shri Karandikar, Hagay Lupesko, Liang Xiong, Eric Zhou
[paper] -
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent
Qinqing Zheng, John Lafferty
[paper] -
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
Qinqing Zheng, John Lafferty
NeurIPS 2015
[paper] [poster] -
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
Qinqing Zheng , Ryota Tomioka
NeurIPS 2015
[paper] [code] [poster]
Talks
- Princeton University, Nov 2022
- UC Berkeley, March 2022
- Minisymposium on Non-Convex Optimization for Low Complexity Models: Theory and Applications, SIAM Optimization Conference, May 2017
- Microsoft Cambridge Research Seminar, UK, March 2017
- SILO Seminar Series, University of Wisconsin Madison, Jan 2017
- Ming Hsieh Institute Series on Mathematical Foundations of Learning from Signals and Data, USC, Dec 2016
Review
- Conference: ICML, NeurIPS, AISTATS, ICLR
- Journal: Journal of Machine Learning Research (Editorial Board Reviewer), IEEE Transactions on Signal Processing, Annals of Statistics