b'Yudong Chen'

research

∙ 09/20/2023

Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model

This paper explores the potential of constructing an AI spoken dialogue ...

0 Xinyu Zhou, et al. ∙

research

∙ 08/29/2023

Clustering Without an Eigengap

We study graph clustering in the Stochastic Block Model (SBM) in the pre...

0 Matthew Zurek, et al. ∙

research

∙ 07/18/2023

VISER: A Tractable Solution Concept for Games with Information Asymmetry

Many real-world games suffer from information asymmetry: one player is o...

0 Jeremy McMahan, et al. ∙

research

∙ 06/28/2023

Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements

For min-max optimization and variational inequalities problems (VIP) enc...

0 Emmanouil-Vasileios Vlatakis-Gkaragkounis, et al. ∙

research

∙ 06/02/2023

Tackling Unbounded State Spaces in Continuing Task Reinforcement Learning

While deep reinforcement learning (RL) algorithms have been successfully...

0 Brahma S. Pavse, et al. ∙

research

∙ 05/31/2023

Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption

We study the infinite-horizon restless bandit problem with the average r...

0 Yige Hong, et al. ∙

research

∙ 05/30/2023

Robust mean change point testing in high-dimensional data with heavy tails

We study a mean change point testing problem for high-dimensional data, ...

0 Mengchu Li, et al. ∙

research

∙ 05/24/2023

Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure

We consider offline Reinforcement Learning (RL), where the agent does no...

0 Xumei Xi, et al. ∙

research

∙ 10/27/2022

Improved Feature Distillation via Projector Ensemble

In knowledge distillation, previous feature distillation methods mainly ...

0 Yudong Chen, et al. ∙

research

∙ 10/03/2022

Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes

We consider Linear Stochastic Approximation (LSA) with a constant stepsi...

0 Dongyan, et al. ∙

research

∙ 06/25/2022

Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning

Thanks to the efficient retrieval speed and low storage consumption, lea...

0 Jianglin Lu, et al. ∙

research

∙ 06/07/2022

Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure

The practicality of reinforcement learning algorithms has been limited d...

0 Tyler Sam, et al. ∙

research

∙ 03/06/2022

Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization

We study the asymmetric matrix factorization problem under a natural non...

0 Liwei Jiang, et al. ∙

research

∙ 01/13/2022

A Geometric Approach to k-means

k-means clustering is a fundamental problem in various disciplines. This...

0 Jiazhen Hong, et al. ∙

research

∙ 11/06/2021

Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning

We study risk-sensitive reinforcement learning (RL) based on the entropi...

0 Yingjie Fei, et al. ∙

research

∙ 11/02/2021

Inference in high-dimensional online changepoint detection

We introduce and study two new inferential challenges associated with th...

0 Yudong Chen, et al. ∙

research

∙ 09/23/2021

Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery

We study the robust recovery of a low-rank matrix from sparsely and gros...

0 Lijun Ding, et al. ∙

research

∙ 08/16/2021

Deep Self-Adaptive Hashing for Image Retrieval

Hashing technology has been widely used in image retrieval due to its co...

0 Qinghong Lin, et al. ∙

research

∙ 02/22/2021

MetaDelta: A Meta-Learning System for Few-shot Image Classification

Meta-learning aims at learning quickly on novel tasks with limited data ...

0 Yudong Chen, et al. ∙

research

∙ 11/03/2020

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines

In this paper, we develop a quadrature framework for large-scale kernel ...

0 Fanghui Liu, et al. ∙

research

∙ 10/25/2020

A Comprehensive Survey on Curriculum Learning

Curriculum learning (CL) is a training strategy that trains a machine le...

0 Xin Wang, et al. ∙

research

∙ 09/28/2020

Likelihood Landscape and Local Minima Structures of Gaussian Mixture Models

In this paper, we study the landscape of the population negative log-lik...

0 Yudong Chen, et al. ∙

research

∙ 08/31/2020

Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle

Existing results for low-rank matrix recovery largely focus on quadratic...

0 Lijun Ding, et al. ∙

research

∙ 06/22/2020

Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

We study risk-sensitive reinforcement learning in episodic Markov decisi...

0 Yingjie Fei, et al. ∙

research

∙ 04/23/2020

Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond

Random features is one of the most sought-after research topics in stati...

0 Fanghui Liu, et al. ∙

research

∙ 03/07/2020

High-dimensional, multiscale online changepoint detection

We introduce a new method for high-dimensional, online changepoint detec...

12 Yudong Chen, et al. ∙

research

∙ 02/17/2020

Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium

We develop provably efficient reinforcement learning algorithms for two-...

0 Qiaomin Xie, et al. ∙

research

∙ 02/16/2020

Structures of Spurious Local Minima in k-means

k-means clustering is a fundamental problem in unsupervised learning. Th...

0 Wei Qian, et al. ∙

research

∙ 11/20/2019

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

In this paper, we propose a fast surrogate leverage weighted sampling st...

16 Fanghui Liu, et al. ∙

research

∙ 11/13/2019

Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery

This paper develops a new class of nonconvex regularizers for low-rank m...

0 Jicong Fan, et al. ∙

research

∙ 06/16/2019

Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities

This work studies the location estimation problem for a mixture of two r...

0 Wei Qian, et al. ∙

research

∙ 06/07/2019

Clustering Degree-Corrected Stochastic Block Model with Outliers

For the degree corrected stochastic block model in the presence of arbit...

0 Xin Qian, et al. ∙

research

∙ 04/22/2019

Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence

The task of recovering a low-rank matrix from its noisy linear measureme...

0 Vasileios Charisopoulos, et al. ∙

research

∙ 04/21/2019

Achieving the Bayes Error Rate in Synchronization and Block Models by SDP, Robustly

We study the statistical performance of semidefinite programming (SDP) r...

0 Yingjie Fei, et al. ∙

research

∙ 09/30/2018

Convex Relaxation Methods for Community Detection

This paper surveys recent theoretical advances in convex optimization ap...

0 Xiaodong Li, et al. ∙

research

∙ 06/14/2018

Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning

In this paper, we study robust large-scale distributed learning in the p...

0 Dong Yin, et al. ∙

research

∙ 04/10/2018

Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm

In this paper, we consider the Tensor Robust Principal Component Analysi...

0 Canyi Lu, et al. ∙

research

∙ 03/20/2018

The Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis

In this paper, we introduce a powerful technique, Leave-One-Out, to the ...

0 Lijun Ding, et al. ∙

research

∙ 03/17/2018

Hidden Integrality of SDP Relaxation for Sub-Gaussian Mixture Models

We consider the problem of estimating the discrete clustering structures...

0 Yingjie Fei, et al. ∙

research

∙ 03/05/2018

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

In large-scale distributed learning, security issues have become increas...

0 Dong Yin, et al. ∙

research

∙ 02/23/2018

Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation

Low-rank modeling plays a pivotal role in signal processing and machine ...

0 Yudong Chen, et al. ∙

research

∙ 08/14/2017

Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization

This paper studies the Tensor Robust Principal Component (TRPCA) problem...

0 Canyi Lu, et al. ∙

research

∙ 05/23/2017

Exponential error rates of SDP for block models: Beyond Grothendieck's inequality

In this paper we consider the cluster estimation problem under the Stoch...

0 Yingjie Fei, et al. ∙

research

∙ 05/16/2017

Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent

We consider the problem of distributed statistical machine learning in a...

0 Yudong Chen, et al. ∙

research

∙ 05/25/2016

Fast Algorithms for Robust PCA via Gradient Descent

We consider the problem of Robust PCA in the fully and partially observe...

0 Xinyang Yi, et al. ∙

research

∙ 12/28/2015

Convexified Modularity Maximization for Degree-corrected Stochastic Block Models

The stochastic block model (SBM) is a popular framework for studying com...

0 Yudong Chen, et al. ∙

research

∙ 09/10/2015

Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees

Optimization problems with rank constraints arise in many applications, ...

0 Yudong Chen, et al. ∙

research

∙ 02/06/2014

Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices

We consider two closely related problems: planted clustering and submatr...

0 Yudong Chen, et al. ∙

research

∙ 12/25/2013

A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates

We consider the mixed regression problem with two components, under adve...

0 Yudong Chen, et al. ∙

research

∙ 10/01/2013

Incoherence-Optimal Matrix Completion

This paper considers the matrix completion problem. We show that it is n...

0 Yudong Chen, et al. ∙

Yudong Chen

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