Zeyuan Allen-Zhu

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Researcher in Machine Learning and Optimization group at Massachusetts Institute of Technology, Researcherat Microsoft

Byzantine-Resilient Non-Convex Stochastic Gradient Descent

We study adversary-resilient stochastic distributed optimization, in whi...
12/28/2020 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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

We formally study how Ensemble of deep learning models can improve test ...
12/17/2020 ∙ by Zeyuan Allen-Zhu, et al. ∙ 78 ∙ share

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Feature Purification: How Adversarial Training Performs Robust Deep Learning

Despite the great empirical success of adversarial training to defend de...
05/20/2020 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Backward Feature Correction: How Deep Learning Performs Deep Learning

How does a 110-layer ResNet learn a high-complexity classifier using rel...
01/13/2020 ∙ by Zeyuan Allen-Zhu, et al. ∙ 5 ∙ share

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What Can ResNet Learn Efficiently, Going Beyond Kernels?

How can neural networks such as ResNet efficiently learn CIFAR-10 with t...
05/24/2019 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Can SGD Learn Recurrent Neural Networks with Provable Generalization?

Recurrent Neural Networks (RNNs) are among the most popular models in se...
02/04/2019 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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The Lingering of Gradients: How to Reuse Gradients over Time

Classically, the time complexity of a first-order method is estimated by...
01/09/2019 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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

Neural networks have great success in many machine learning applications...
11/12/2018 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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A Convergence Theory for Deep Learning via Over-Parameterization

Deep neural networks (DNNs) have demonstrated dominating performance in ...
11/09/2018 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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On the Convergence Rate of Training Recurrent Neural Networks

Despite the huge success of deep learning, our understanding to how the ...
10/29/2018 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Is Q-learning Provably Efficient?

Model-free reinforcement learning (RL) algorithms, such as Q-learning, d...
07/10/2018 ∙ by Chi Jin, et al. ∙ 0 ∙ share

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Operator Scaling via Geodesically Convex Optimization, Invariant Theory and Polynomial Identity Testing

We propose a new second-order method for geodesically convex optimizatio...
04/03/2018 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Byzantine Stochastic Gradient Descent

This paper studies the problem of distributed stochastic optimization in...
03/23/2018 ∙ by Dan Alistarh, et al. ∙ 0 ∙ share

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Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization

The problem of minimizing sum-of-nonconvex functions (i.e., convex funct...
02/12/2018 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits

Regret bounds in online learning compare the player's performance to L^*...
02/09/2018 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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How To Make the Gradients Small Stochastically

In convex stochastic optimization, convergence rates in terms of minimiz...
01/08/2018 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Neon2: Finding Local Minima via First-Order Oracles

We propose a reduction for non-convex optimization that can (1) turn a s...
11/17/2017 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach

The experimental design problem concerns the selection of k points from ...
11/14/2017 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Natasha 2: Faster Non-Convex Optimization Than SGD

We design a stochastic algorithm to train any smooth neural network to ε...
08/29/2017 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls

We propose a rank-k variant of the classical Frank-Wolfe algorithm to so...
08/07/2017 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter

Given a nonconvex function f(x) that is an average of n smooth functions...
02/02/2017 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU

The online problem of computing the top eigenvector is fundamental to ma...
01/06/2017 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Finding Approximate Local Minima Faster than Gradient Descent

We design a non-convex second-order optimization algorithm that is guara...
11/03/2016 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Faster Principal Component Regression and Stable Matrix Chebyshev Approximation

We solve principal component regression (PCR), up to a multiplicative ac...
08/16/2016 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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

We study streaming principal component analysis (PCA), that is to find, ...
07/26/2016 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition

We study k-GenEV, the problem of finding the top k generalized eigenvect...
07/20/2016 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain

We study k-SVD that is to obtain the first k singular vectors of a matri...
07/12/2016 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Katyusha: The First Direct Acceleration of Stochastic Gradient Methods

Nesterov's momentum trick is famously known for accelerating gradient de...
03/18/2016 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Variance Reduction for Faster Non-Convex Optimization

We consider the fundamental problem in non-convex optimization of effici...
03/17/2016 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Optimal Black-Box Reductions Between Optimization Objectives

The diverse world of machine learning applications has given rise to a p...
03/17/2016 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters

The amount of data available in the world is growing faster than our abi...
02/05/2016 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

Accelerated coordinate descent is widely used in optimization due to its...
12/30/2015 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates

In this paper, we provide a novel construction of the linear-sized spect...
06/16/2015 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

Many classical algorithms are found until several years later to outlive...
06/05/2015 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent

First-order methods play a central role in large-scale machine learning....
07/06/2014 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Flow-Based Algorithms for Local Graph Clustering

Given a subset S of vertices of an undirected graph G, the cut-improveme...
07/10/2013 ∙ by Lorenzo Orecchia, et al. ∙ 0 ∙ share

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Local Graph Clustering Beyond Cheeger's Inequality

Motivated by applications of large-scale graph clustering, we study rand...
04/30/2013 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Zeyuan Allen-Zhu

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