World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
7878
World Ranking
11487
National Ranking
4721

Overview

Zeyuan Allen-Zhu is affiliated with Meta Platforms, Inc. in the United States. Their research primarily focuses on fields related to computer science, with a strong emphasis on artificial intelligence.

The main areas of study covered in their work include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computational Mechanics
  • Statistical and Nonlinear Physics
  • Information Systems

Major topics explored within these areas involve:

  • Natural Language Processing Techniques
  • Topic Modeling
  • Machine Learning and Algorithms
  • Stochastic Gradient Optimization Techniques
  • Adversarial Robustness in Machine Learning
  • Domain Adaptation and Few-Shot Learning
  • Sparse and Compressive Sensing Techniques

The scientist has contributed numerous publications, with a strong presence in the following venues:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Mathematical Programming

Recent papers include:

  • "LoRA: Low-Rank Adaptation of Large Language Models" (2021, arXiv (Cornell University))
  • "Learning and generalization in overparameterized neural networks, going beyond two layers" (2025, arXiv (Cornell University))
  • "Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning" (2020, arXiv (Cornell University))
  • "Near-optimal discrete optimization for experimental design: a regret minimization approach" (2020, Mathematical Programming)
  • "Byzantine-Resilient Non-Convex Stochastic Gradient Descent" (2020, arXiv (Cornell University))

Frequent co-authors with whom collaborative work has been published include:

  • Yuanzhi Li
  • Zicheng Xu
  • Ye Tian

Best Publications

  • LoRA: Low-Rank Adaptation of Large Language Models.

    Edward J. Hu;Yelong Shen;Phillip Wallis;Zeyuan Allen-Zhu

  • A Convergence Theory for Deep Learning via Over-Parameterization

    Zeyuan Allen-Zhu;Yuanzhi Li;Zhao Song

  • A Convergence Theory for Deep Learning via Over-Parameterization

    Zeyuan Allen-Zhu;Yuanzhi Li;Zhao Song

  • Katyusha: The First Direct Acceleration of Stochastic Gradient Methods

    Zeyuan Allen-Zhu

  • Is Q-learning Provably Efficient?

    Chi Jin;Zeyuan Allen-Zhu;Sebastien Bubeck;Michael I. Jordan

  • Variance reduction for faster non-convex optimization

    Zeyuan Allen-Zhu;Elad Hazan

  • A simple, combinatorial algorithm for solving SDD systems in nearly-linear time

    Jonathan A. Kelner;Lorenzo Orecchia;Aaron Sidford;Zeyuan Allen Zhu

  • Natasha 2: Faster Non-Convex Optimization Than SGD

    Zeyuan Allen-Zhu

  • Finding approximate local minima faster than gradient descent

    Naman Agarwal;Zeyuan Allen-Zhu;Brian Bullins;Elad Hazan

  • Byzantine Stochastic Gradient Descent

    Dan Alistarh;Zeyuan Allen-Zhu;Jerry Li

  • Asymptotically optimal strategy-proof mechanisms for two-facility games

    Pinyan Lu;Xiaorui Sun;Yajun Wang;Zeyuan Allen Zhu

  • Improved SVRG for non-strongly-convex or sum-of-non-convex objectives

    Zeyuan Allen-Zhu;Yang Yuan

  • Even faster accelerated coordinate descent using non-uniform sampling

    Zeyuan Allen-Zhu;Zheng Qu;Peter Richtárik;Yang Yuan

  • Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers

    Zeyuan Allen-Zhu;Yuanzhi Li;Yingyu Liang

  • Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning.

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Variance Reduction for Faster Non-Convex Optimization

    Zeyuan Allen-Zhu;Elad Hazan

  • On the Convergence Rate of Training Recurrent Neural Networks

    Zeyuan Allen-Zhu;Yuanzhi Li;Zhao Song

  • What Can ResNet Learn Efficiently, Going Beyond Kernels?

    Zeyuan Allen-Zhu;Yuanzhi Li

  • A novel click model and its applications to online advertising

    Zeyuan Allen Zhu;Weizhu Chen;Tom Minka;Chenguang Zhu

  • Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers

    Unknown

  • Randomized accuracy-aware program transformations for efficient approximate computations

    Zeyuan Allen Zhu;Sasa Misailovic;Jonathan A. Kelner;Martin Rinard

  • Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent

    Unknown

  • NEON2: Finding Local Minima via First-Order Oracles

    Zeyuan Allen-Zhu;Yuanzhi Li

  • A Local Algorithm for Finding Well-Connected Clusters

    Zeyuan Allen Zhu;Silvio Lattanzi;Vahab Mirrokni

  • LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Feature Purification: How Adversarial Training Performs Robust Deep Learning

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Much Faster Algorithms for Matrix Scaling

    Zeyuan Allen-Zhu;Yuanzhi Li;Rafael Oliveira;Avi Wigderson

  • P-packSVM: Parallel Primal grAdient desCent Kernel SVM

    Zeyuan Allen Zhu;Weizhu Chen;Gang Wang;Chenguang Zhu

  • First Efficient Convergence for Streaming k-PCA: A Global, Gap-Free, and Near-Optimal Rate

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Backward Feature Correction: How Deep Learning Performs Deep Learning

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Byzantine Stochastic Gradient Descent

    Dan Alistarh;Zeyuan Allen-Zhu;Jerry Li

  • How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD

    Zeyuan Allen-Zhu

  • Even Faster SVD Decomposition Yet Without Agonizing Pain

    Zeyuan Allen Zhu;Yuanzhi Li

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