
Provably Faster Algorithms for Bilevel Optimization and Applications to MetaLearning
Bilevel optimization has arisen as a powerful tool for many machine lear...
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FiniteTime Analysis for Double Qlearning
Although Qlearning is one of the most successful algorithms for finding...
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Spectral Algorithms for Community Detection in Directed Networks
Community detection in large social networks is affected by degree heter...
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Momentum Qlearning with FiniteSample Convergence Guarantee
Existing studies indicate that momentum ideas in conventional optimizati...
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Feedback Capacities of Gaussian MultipleAccess Wiretap Channels
The feedback capacities of the Gaussian multipleaccess channel (GMAC) a...
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Analysis of Qlearning with Adaptation and Momentum Restart for Gradient Descent
Existing convergence analyses of Qlearning mostly focus on the vanilla ...
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When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence
Generative adversarial imitation learning (GAIL) is a popular inverse re...
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Convergence of MetaLearning with TaskSpecific Adaptation over Partial Parameters
Although modelagnostic metalearning (MAML) is a very successful algori...
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Enhanced First and Zeroth Order Variance Reduced Algorithms for MinMax Optimization
Minmax optimization captures many important machine learning problems s...
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Nonasymptotic Convergence Analysis of Two Timescale (Natural) ActorCritic Algorithms
As an important type of reinforcement learning algorithms, actorcritic ...
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Improving Sample Complexity Bounds for ActorCritic Algorithms
The actorcritic (AC) algorithm is a popular method to find an optimal p...
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Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization
Various types of parameter restart schemes have been proposed for accele...
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MultiStep ModelAgnostic MetaLearning: Convergence and Improved Algorithms
As a popular metalearning approach, the modelagnostic metalearning (M...
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Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack
The multiarmed bandit formalism has been extensively studied under vari...
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Nonasymptotic Convergence of Adamtype Reinforcement Learning Algorithms under Markovian Sampling
Despite the wide applications of Adam in reinforcement learning (RL), th...
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Reanalysis of Variance Reduced Temporal Difference Learning
Temporal difference (TD) learning is a popular algorithm for policy eval...
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Improved ZerothOrder Variance Reduced Algorithms and Analysis for Nonconvex Optimization
Two types of zerothorder stochastic algorithms have recently been desig...
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Faster Stochastic Algorithms via HistoryGradient Aided Batch Size Adaptation
Various schemes for adapting batch size have been recently proposed to a...
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Distributed SGD Generalizes Well Under Asynchrony
The performance of fully synchronized distributed systems has faced a bo...
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Two Timescale OffPolicy TD Learning: Nonasymptotic Analysis over Markovian Samples
Gradientbased temporal difference (GTD) algorithms are widely used in o...
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Momentum Schemes with Stochastic Variance Reduction for Nonconvex Composite Optimization
Two new stochastic variancereduced algorithms named SARAH and SPIDER ha...
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FiniteSample Analysis for SARSA and QLearning with Linear Function Approximation
Though the convergence of major reinforcement learning algorithms has be...
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SGD Converges to Global Minimum in Deep Learning via Starconvex Path
Stochastic gradient descent (SGD) has been found to be surprisingly effe...
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MRGAN: Manifold Regularized Generative Adversarial Networks
Despite the growing interest in generative adversarial networks (GANs), ...
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Minimax Estimation of Neural Net Distance
An important class of distance metrics proposed for training generative ...
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SpiderBoost: A Class of Faster Variancereduced Algorithms for Nonconvex Optimization
There has been extensive research on developing stochastic variance redu...
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Cubic Regularization with Momentum for Nonconvex Optimization
Momentum is a popular technique to accelerate the convergence in practic...
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A Note on Inexact Condition for Cubic Regularized Newton's Method
This note considers the inexact cubicregularized Newton's method (CR), ...
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Convergence of Cubic Regularization for Nonconvex Optimization under KL Property
Cubicregularized Newton's method (CR) is a popular algorithm that guara...
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Kmedoids Clustering of Data Sequences with Composite Distributions
This paper studies clustering of data sequences using the kmedoids algo...
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Parallel Gaussian Channels Corrupted by Independent States With a StateCognitive Helper
We consider a statedependent parallel Gaussian channel with independent...
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Convergence of SGD in Learning ReLU Models with Separable Data
We consider the binary classification problem in which the objective fun...
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StateDependent Interference Channel with Correlated States
This paper investigates the Gaussian statedependent interference channe...
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Sample Complexity of Stochastic VarianceReduced Cubic Regularization for Nonconvex Optimization
The popular cubic regularization (CR) method converges with first and s...
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Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization
The success of deep learning has led to a rising interest in the general...
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Local Geometry of OneHiddenLayer Neural Networks for Logistic Regression
We study the local geometry of a onehiddenlayer fullyconnected neural...
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Secrecy Capacity of Colored Gaussian Noise Channels with Feedback
In this paper, the kth order autoregressive moving average (ARMA(k)) Ga...
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Nonparametric Composite Hypothesis Testing in an Asymptotic Regime
We investigate the nonparametric, composite hypothesis testing problem f...
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Critical Points of Neural Networks: Analytical Forms and Landscape Properties
Due to the success of deep learning to solving a variety of challenging ...
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Characterization of Gradient Dominance and Regularity Conditions for Neural Networks
The past decade has witnessed a successful application of deep learning ...
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Nonconvex LowRank Matrix Recovery with Arbitrary Outliers via MedianTruncated Gradient Descent
Recent work has demonstrated the effectiveness of gradient descent for d...
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Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations
We study the phase retrieval problem, which solves quadratic system of e...
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Nonparametric Detection of Geometric Structures over Networks
Nonparametric detection of existence of an anomalous structure over a ne...
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MedianTruncated Nonconvex Approach for Phase Retrieval with Outliers
This paper investigates the phase retrieval problem, which aims to recov...
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Nonparametric Detection of Anomalous Data Streams
A nonparametric anomalous hypothesis testing problem is investigated, in...
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A KernelBased Nonparametric Test for Anomaly Detection over Line Networks
The nonparametric problem of detecting existence of an anomalous interva...
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Sharp Threshold for Multivariate MultiResponse Linear Regression via Block Regularized Lasso
In this paper, we investigate a multivariate multiresponse (MVMR) linea...
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Yingbin Liang
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Associate Professor, Electrical & Computer Engineering at The Ohio State University