
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
We study the robust recovery of a lowrank matrix from sparsely and gros...
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Deep SelfAdaptive Hashing for Image Retrieval
Hashing technology has been widely used in image retrieval due to its co...
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MetaDelta: A MetaLearning System for Fewshot Image Classification
Metalearning aims at learning quickly on novel tasks with limited data ...
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Towards a Unified Quadrature Framework for LargeScale Kernel Machines
In this paper, we develop a quadrature framework for largescale kernel ...
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A Comprehensive Survey on Curriculum Learning
Curriculum learning (CL) is a training strategy that trains a machine le...
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Likelihood Landscape and Local Minima Structures of Gaussian Mixture Models
In this paper, we study the landscape of the population negative loglik...
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Lowrank matrix recovery with nonquadratic loss: projected gradient method and regularity projection oracle
Existing results for lowrank matrix recovery largely focus on quadratic...
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RiskSensitive Reinforcement Learning: NearOptimal RiskSample Tradeoff in Regret
We study risksensitive reinforcement learning in episodic Markov decisi...
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Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond
Random features is one of the most soughtafter research topics in stati...
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Highdimensional, multiscale online changepoint detection
We introduce a new method for highdimensional, online changepoint detec...
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Learning ZeroSum SimultaneousMove Markov Games Using Function Approximation and Correlated Equilibrium
We develop provably efficient reinforcement learning algorithms for two...
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Structures of Spurious Local Minima in kmeans
kmeans clustering is a fundamental problem in unsupervised learning. Th...
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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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Factor GroupSparse Regularization for Efficient LowRank Matrix Recovery
This paper develops a new class of nonconvex regularizers for lowrank m...
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Global Convergence of Least Squares EM for Demixing Two LogConcave Densities
This work studies the location estimation problem for a mixture of two r...
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Clustering DegreeCorrected Stochastic Block Model with Outliers
For the degree corrected stochastic block model in the presence of arbit...
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Lowrank matrix recovery with composite optimization: good conditioning and rapid convergence
The task of recovering a lowrank matrix from its noisy linear measureme...
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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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Convex Relaxation Methods for Community Detection
This paper surveys recent theoretical advances in convex optimization ap...
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Defending Against Saddle Point Attack in ByzantineRobust Distributed Learning
In this paper, we study robust largescale distributed learning in the p...
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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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The Leaveoneout Approach for Matrix Completion: Primal and Dual Analysis
In this paper, we introduce a powerful technique, LeaveOneOut, to the ...
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Hidden Integrality of SDP Relaxation for SubGaussian Mixture Models
We consider the problem of estimating the discrete clustering structures...
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ByzantineRobust Distributed Learning: Towards Optimal Statistical Rates
In largescale distributed learning, security issues have become increas...
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Harnessing Structures in Big Data via Guaranteed LowRank Matrix Estimation
Lowrank modeling plays a pivotal role in signal processing and machine ...
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Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted LowRank Tensors via Convex Optimization
This paper studies the Tensor Robust Principal Component (TRPCA) problem...
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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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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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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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Convexified Modularity Maximization for Degreecorrected Stochastic Block Models
The stochastic block model (SBM) is a popular framework for studying com...
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Fast lowrank estimation by projected gradient descent: General statistical and algorithmic guarantees
Optimization problems with rank constraints arise in many applications, ...
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StatisticalComputational 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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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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IncoherenceOptimal Matrix Completion
This paper considers the matrix completion problem. We show that it is n...
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Completing Any Lowrank Matrix, Provably
Matrix completion, i.e., the exact and provable recovery of a lowrank m...
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Detecting Overlapping Temporal Community Structure in TimeEvolving Networks
We present a principled approach for detecting overlapping temporal comm...
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Breaking the Small Cluster Barrier of Graph Clustering
This paper investigates graph clustering in the planted cluster model in...
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Robust High Dimensional Sparse Regression and Matching Pursuit
We consider high dimensional sparse regression, and develop strategies a...
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Improved Graph Clustering
Graph clustering involves the task of dividing nodes into clusters, so t...
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Clustering Partially Observed Graphs via Convex Optimization
This paper considers the problem of clustering a partially observed unwe...
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Lowrank Matrix Recovery from Errors and Erasures
This paper considers the recovery of a lowrank matrix from an observed ...
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Matrix completion with column manipulation: Nearoptimal samplerobustnessrank tradeoffs
This paper considers the problem of matrix completion when some number o...
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