
ACMNet: Action Context Modeling Network for WeaklySupervised Temporal Action Localization
Weaklysupervised temporal action localization aims to localize action i...
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Online Convex Optimization with Continuous Switching Constraint
In many sequential decision making applications, the change of decision ...
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Online Strongly Convex Optimization with Unknown Delays
We investigate the problem of online convex optimization with unknown de...
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Projectionfree Distributed Online Learning with Strongly Convex Losses
To efficiently solve distributed online learning problems with complicat...
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Nonstationary Linear Bandits Revisited
In this note, we revisit nonstationary linear bandits, a variant of sto...
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Revisiting Smoothed Online Learning
In this paper, we revisit the problem of smoothed online learning, in wh...
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NAST: NonAutoregressive SpatialTemporal Transformer for Time Series Forecasting
Although Transformer has made breakthrough success in widespread domains...
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Probabilistic Robustness Analysis for DNNs based on PAC Learning
This paper proposes a black box based approach for analysing deep neural...
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SIDNISM: A Selfsupervised Lowlight Image Enhancement Framework
When capturing images in lowlight conditions, the images often suffer f...
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Projectionfree Online Learning over Strongly Convex Sets
To efficiently solve online problems with complicated constraints, proje...
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Improving Neural Network Verification through Spurious Region Guided Refinement
We propose a spurious region guided refinement approach for robustness v...
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How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks
This paper studies the novel concept of weight correlation in deep neura...
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Approximate Multiplication of Sparse Matrices with Limited Space
Approximate matrix multiplication with limited space has received everi...
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Verifying Pufferfish Privacy in Hidden Markov Models
Pufferfish is a Bayesian privacy framework for designing and analyzing p...
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Dynamic Regret of Convex and Smooth Functions
We investigate online convex optimization in nonstationary environments...
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Proving NonInclusion of Büchi Automata based on Monte Carlo Sampling
The search for a proof of correctness and the search for counterexamples...
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Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions
In this paper, we present an improved analysis for dynamic regret of str...
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On the Power of Unambiguity in Büchi Complementation
In this work, we exploit the power of unambiguity for the complementatio...
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Nearly Optimal Regret for Stochastic Linear Bandits with HeavyTailed Payoffs
In this paper, we study the problem of stochastic linear bandits with fi...
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Minimizing Dynamic Regret and Adaptive Regret Simultaneously
Regret minimization is treated as the golden rule in the traditional stu...
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Multiattention Networks for Temporal Localization of Videolevel Labels
Temporal localization remains an important challenge in video understand...
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More Adaptive Algorithms for Tracking the Best Expert
In this paper, we consider the problem of prediction with expert advice ...
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Stochastic Optimization for Nonconvex InfProjection Problems
In this paper, we study a family of nonconvex and possibly nonsmooth i...
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Bandit Convex Optimization in Nonstationary Environments
Bandit Convex Optimization (BCO) is a fundamental framework for modeling...
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Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions
To deal with changing environments, a new performance measureadaptive...
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MultiObjective Generalized Linear Bandits
In this paper, we study the multiobjective bandits (MOB) problem, where...
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Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
Recent studies have highlighted that deep neural networks (DNNs) are vul...
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Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization
In this paper, we study adaptive online convex optimization, and aim to ...
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SAdam: A Variant of Adam for Strongly Convex Functions
The Adam algorithm has become extremely popular for largescale machine ...
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Prediction with Unpredictable Feature Evolution
Feature space can change or evolve when learning with streaming data. Se...
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Adaptive Regret of Convex and Smooth Functions
We investigate online convex optimization in changing environments, and ...
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Stochastic PrimalDual Algorithms with Faster Convergence than O(1/√(T)) for Problems without Bilinear Structure
Previous studies on stochastic primaldual algorithms for solving minma...
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Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the O(1/T) Convergence Rate
Stochastic approximation (SA) is a classical approach for stochastic con...
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A Wasserstein GAN model with the total variational regularization
It is well known that the generative adversarial nets (GANs) are remarka...
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Adaptive Online Learning in Dynamic Environments
In this paper, we study online convex optimization in dynamic environmen...
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QueryEfficient BlackBox Attack by Active Learning
Deep neural network (DNN) as a popular machine learning model is found t...
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Matrix Completion from NonUniformly Sampled Entries
In this paper, we consider matrix completion from nonuniformly sampled ...
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Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
Error bound conditions (EBC) are properties that characterize the growth...
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An Image dehazing approach based on the airlight field estimation
This paper proposes a scheme for single image haze removal based on the ...
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ℓ_1regression with Heavytailed Distributions
In this paper, we consider the problem of linear regression with heavyt...
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Empiricallikelihoodbased criteria for model selection on marginal analysis of longitudinal data with dropout missingness
Longitudinal data are common in clinical trials and observational studie...
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VRSGD: A Simple Stochastic Variance Reduction Method for Machine Learning
In this paper, we propose a simple variant of the original SVRG, called ...
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Verifying Probabilistic Timed Automata Against OmegaRegular DenseTime Properties
Probabilistic timed automata (PTAs) are timed automata (TAs) extended wi...
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A Simple Analysis for Expconcave Empirical Minimization with Arbitrary Convex Regularizer
In this paper, we present a simple analysis of fast rates with high pr...
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Learning with Feature Evolvable Streams
Learning with streaming data has attracted much attention during the pas...
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A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates
This paper focuses on convex constrained optimization problems, where th...
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Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
This work focuses on dynamic regret of online convex optimization that c...
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An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection
In this paper, we consider the problem of column subset selection. We pr...
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Analysis of Nuclear Norm Regularization for Fullrank Matrix Completion
In this paper, we provide a theoretical analysis of the nuclearnorm reg...
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Theory of Dualsparse Regularized Randomized Reduction
In this paper, we study randomized reduction methods, which reduce high...
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Lijun Zhang
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Assistant Professor at Penn State Milton S. Hershey Medical Center since 2014, Postdoctoral Fellow at Penn State University from 20132014, Data Informatics at Emory University from 20092013, Research Assistant at University of Louisville from 20052009, Software Engineer at SynTest Technologies, Inc. 2005, R&D Intern at PHILIPS Research East Asia 20032004, Project Manager at Qingdao Automation Research Institute from 19992002, PhD Computer Science at University of Louisville from 20052009