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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 ...
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08/29/2023
Clustering Without an Eigengap
We study graph clustering in the Stochastic Block Model (SBM) in the pre...
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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...
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06/28/2023
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
For min-max optimization and variational inequalities problems (VIP) enc...
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06/02/2023
Tackling Unbounded State Spaces in Continuing Task Reinforcement Learning
While deep reinforcement learning (RL) algorithms have been successfully...
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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...
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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, ...
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05/24/2023
Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure
We consider offline Reinforcement Learning (RL), where the agent does no...
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10/27/2022
Improved Feature Distillation via Projector Ensemble
In knowledge distillation, previous feature distillation methods mainly ...
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10/03/2022
Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes
We consider Linear Stochastic Approximation (LSA) with a constant stepsi...
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06/25/2022
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning
Thanks to the efficient retrieval speed and low storage consumption, lea...
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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...
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03/06/2022
Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization
We study the asymmetric matrix factorization problem under a natural non...
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01/13/2022
A Geometric Approach to k-means
k-means clustering is a fundamental problem in various disciplines. This...
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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...
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11/02/2021
Inference in high-dimensional online changepoint detection
We introduce and study two new inferential challenges associated with th...
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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...
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08/16/2021
Deep Self-Adaptive Hashing for Image Retrieval
Hashing technology has been widely used in image retrieval due to its co...
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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 ...
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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 ...
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10/25/2020
A Comprehensive Survey on Curriculum Learning
Curriculum learning (CL) is a training strategy that trains a machine le...
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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...
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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...
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06/22/2020
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
We study risk-sensitive reinforcement learning in episodic Markov decisi...
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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...
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03/07/2020
High-dimensional, multiscale online changepoint detection
We introduce a new method for high-dimensional, online changepoint detec...
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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-...
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02/16/2020
Structures of Spurious Local Minima in k-means
k-means clustering is a fundamental problem in unsupervised learning. Th...
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11/20/2019
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
In this paper, we propose a fast surrogate leverage weighted sampling st...
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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...
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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...
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06/07/2019
Clustering Degree-Corrected Stochastic Block Model with Outliers
For the degree corrected stochastic block model in the presence of arbit...
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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...
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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...
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09/30/2018
Convex Relaxation Methods for Community Detection
This paper surveys recent theoretical advances in convex optimization ap...
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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...
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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...
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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 ...
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03/17/2018
Hidden Integrality of SDP Relaxation for Sub-Gaussian Mixture Models
We consider the problem of estimating the discrete clustering structures...
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03/05/2018
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
In large-scale distributed learning, security issues have become increas...
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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 ...
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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...
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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...
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05/16/2017
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent
We consider the problem of distributed statistical machine learning in a...
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05/25/2016
Fast Algorithms for Robust PCA via Gradient Descent
We consider the problem of Robust PCA in the fully and partially observe...
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12/28/2015
Convexified Modularity Maximization for Degree-corrected Stochastic Block Models
The stochastic block model (SBM) is a popular framework for studying com...
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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, ...
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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...
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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...
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10/01/2013