Jingzhao Zhang

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MIT

Fast Federated Learning in the Presence of Arbitrary Device Unavailability

Federated Learning (FL) coordinates with numerous heterogeneous devices ...
06/08/2021 ∙ by Xinran Gu, et al. ∙ 0 ∙ share

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Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization

We provide a first-order oracle complexity lower bound for finding stati...
04/18/2021 ∙ by Haochuan Li, et al. ∙ 10 ∙ share

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Provably Efficient Algorithms for Multi-Objective Competitive RL

We study multi-objective reinforcement learning (RL) where an agent's re...
02/05/2021 ∙ by Tiancheng Yu, et al. ∙ 0 ∙ share

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Coping with Label Shift via Distributionally Robust Optimisation

The label shift problem refers to the supervised learning setting where ...
10/23/2020 ∙ by Jingzhao Zhang, et al. ∙ 6 ∙ share

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Stochastic Optimization with Non-stationary Noise

We investigate stochastic optimization problems under relaxed assumption...
06/08/2020 ∙ by Jingzhao Zhang, et al. ∙ 0 ∙ share

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On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions

We provide the first non-asymptotic analysis for finding stationary poin...
02/10/2020 ∙ by Jingzhao Zhang, et al. ∙ 0 ∙ share

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Why ADAM Beats SGD for Attention Models

While stochastic gradient descent (SGD) is still the de facto algorithm ...
12/06/2019 ∙ by Jingzhao Zhang, et al. ∙ 0 ∙ share

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Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition

We provide a theoretical explanation for the fast convergence of gradien...
05/28/2019 ∙ by Jingzhao Zhang, et al. ∙ 0 ∙ share

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Quantifying Exposure Bias for Neural Language Generation

The exposure bias problem refers to the training-inference discrepancy c...
05/25/2019 ∙ by Tianxing He, et al. ∙ 0 ∙ share

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A Probe into Understanding GAN and VAE models

Both generative adversarial network models and variational autoencoders ...
12/13/2018 ∙ by Jingzhao Zhang, et al. ∙ 38 ∙ share

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A Probe Towards Understanding GAN and VAE Models

This project report compares some known GAN and VAE models proposed prio...
12/13/2018 ∙ by Lu Mi, et al. ∙ 0 ∙ share

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R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate

We study smooth stochastic optimization problems on Riemannian manifolds...
11/10/2018 ∙ by Jingzhao Zhang, et al. ∙ 0 ∙ share

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Direct Runge-Kutta Discretization Achieves Acceleration

We study gradient-based optimization methods obtained by directly discre...
05/01/2018 ∙ by Jingzhao Zhang, et al. ∙ 0 ∙ share

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Jingzhao Zhang

Featured Co-authors

James Glass

Suvrit Sra

Sanjiv Kumar

Ali Jadbabaie

Aryan Mokhtari

Srinadh Bhojanapalli

Sashank J Reddi

Longbo Huang

Andreas Veit

Zhiming Zhou

Sai Praneeth Karimireddy

Fast Federated Learning in the Presence of Arbitrary Device Unavailability

Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization

Provably Efficient Algorithms for Multi-Objective Competitive RL

Coping with Label Shift via Distributionally Robust Optimisation

Stochastic Optimization with Non-stationary Noise

On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions

Why ADAM Beats SGD for Attention Models

Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition

Quantifying Exposure Bias for Neural Language Generation

A Probe into Understanding GAN and VAE models

A Probe Towards Understanding GAN and VAE Models

R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate

Direct Runge-Kutta Discretization Achieves Acceleration