Yang Yuan

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MIT ∙ Tsinghua University ∙ cornell university

Secure Data Sharing With Flow Model

In the classical multi-party computation setting, multiple parties joint...
09/24/2020 ∙ by Chenwei Wu, et al. ∙ 18 ∙ share

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Inject Machine Learning into Significance Test for Misspecified Linear Models

Due to its strong interpretability, linear regression is widely used in ...
06/04/2020 ∙ by Jiaye Teng, et al. ∙ 0 ∙ share

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Adversarial Data Encryption

In the big data era, many organizations face the dilemma of data sharing...
02/10/2020 ∙ by Yingdong Hu, et al. ∙ 2 ∙ share

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Learning-Based Low-Rank Approximations

We introduce a "learning-based" algorithm for the low-rank decomposition...
10/30/2019 ∙ by Piotr Indyk, et al. ∙ 14 ∙ share

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System-Level Development of a User-Integrated Semi-Autonomous Lawn Mowing System: Problem Overview, Basic Requirements, and Proposed Architecture

This concept paper outlines some recent efforts toward the design and de...
07/12/2019 ∙ by Albert E. Patterson, et al. ∙ 0 ∙ share

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A Stratified Approach to Robustness for Randomly Smoothed Classifiers

Strong theoretical guarantees of robustness can be given for ensembles o...
06/12/2019 ∙ by Guang-He Lee, et al. ∙ 1 ∙ share

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Asymmetric Valleys: Beyond Sharp and Flat Local Minima

Despite the non-convex nature of their loss functions, deep neural netwo...
02/02/2019 ∙ by Haowei He, et al. ∙ 0 ∙ share

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An empirical study on evaluation metrics of generative adversarial networks

Evaluating generative adversarial networks (GANs) is inherently challeng...
06/19/2018 ∙ by Qiantong Xu, et al. ∙ 0 ∙ share

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An Alternative View: When Does SGD Escape Local Minima?

Stochastic gradient descent (SGD) is widely used in machine learning. Al...
02/17/2018 ∙ by Robert Kleinberg, et al. ∙ 0 ∙ share

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Hyperparameter Optimization: A Spectral Approach

We give a simple, fast algorithm for hyperparameter optimization inspire...
06/02/2017 ∙ by Elad Hazan, 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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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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Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition

We analyze stochastic gradient descent for optimizing non-convex functio...
03/06/2015 ∙ by Rong Ge, et al. ∙ 0 ∙ share

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Yang Yuan

Featured Co-authors

Peter Richtarik

Yu Sun

Shiyu Chang

Rong Ge

Yuanzhi Li

Elad Hazan

Zeyuan Allen-Zhu

Chi Jin

Gao Huang

Karthik Sridharan

Liang Zhang

Secure Data Sharing With Flow Model

Inject Machine Learning into Significance Test for Misspecified Linear Models

Adversarial Data Encryption

Learning-Based Low-Rank Approximations

System-Level Development of a User-Integrated Semi-Autonomous Lawn Mowing System: Problem Overview, Basic Requirements, and Proposed Architecture

A Stratified Approach to Robustness for Randomly Smoothed Classifiers

Asymmetric Valleys: Beyond Sharp and Flat Local Minima

An empirical study on evaluation metrics of generative adversarial networks

An Alternative View: When Does SGD Escape Local Minima?

Hyperparameter Optimization: A Spectral Approach

Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

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

Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition