Lijun Zhang

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Nanjing University ∙ Michigan State University

Assistant Professor at Penn State Milton S. Hershey Medical Center since 2014, Postdoctoral Fellow at Penn State University from 2013-2014, Data Informatics at Emory University from 2009-2013, Research Assistant at University of Louisville from 2005-2009, Software Engineer at SynTest Technologies, Inc. 2005, R&D Intern at PHILIPS Research East Asia 2003-2004, Project Manager at Qingdao Automation Research Institute from 1999-2002, PhD Computer Science at University of Louisville from 2005-2009

Featured Co-authors

Dacheng Tao
271 publications
Tie-Yan Liu
115 publications
Kai Chen
81 publications
Yi Li
78 publications
Yan Yan
75 publications
Xiao Liu
72 publications
Zhi-Hua Zhou
58 publications
Tianbao Yang
56 publications
Bowen Zhou
55 publications
Yong Li
54 publications
Jun Sun
52 publications

ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization

Weakly-supervised temporal action localization aims to localize action i...
04/07/2021 ∙ by Sanqing Qu, et al. ∙ 0 ∙ share

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Online Convex Optimization with Continuous Switching Constraint

In many sequential decision making applications, the change of decision ...
03/21/2021 ∙ by Guanghui Wang, et al. ∙ 0 ∙ share

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Online Strongly Convex Optimization with Unknown Delays

We investigate the problem of online convex optimization with unknown de...
03/21/2021 ∙ by Yuanyu Wan, et al. ∙ 21 ∙ share

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Projection-free Distributed Online Learning with Strongly Convex Losses

To efficiently solve distributed online learning problems with complicat...
03/20/2021 ∙ by Yuanyu Wan, et al. ∙ 0 ∙ share

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Non-stationary Linear Bandits Revisited

In this note, we revisit non-stationary linear bandits, a variant of sto...
03/09/2021 ∙ by Peng Zhao, et al. ∙ 0 ∙ share

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Revisiting Smoothed Online Learning

In this paper, we revisit the problem of smoothed online learning, in wh...
02/13/2021 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting

Although Transformer has made breakthrough success in widespread domains...
02/10/2021 ∙ by Kai Chen, et al. ∙ 20 ∙ share

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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...
01/25/2021 ∙ by Renjue Li, et al. ∙ 3 ∙ share

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SID-NISM: A Self-supervised Low-light Image Enhancement Framework

When capturing images in low-light conditions, the images often suffer f...
12/16/2020 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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Projection-free Online Learning over Strongly Convex Sets

To efficiently solve online problems with complicated constraints, proje...
10/16/2020 ∙ by Yuanyu Wan, et al. ∙ 0 ∙ share

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Improving Neural Network Verification through Spurious Region Guided Refinement

We propose a spurious region guided refinement approach for robustness v...
10/15/2020 ∙ by Pengfei Yang, et al. ∙ 0 ∙ share

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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...
10/12/2020 ∙ by Gaojie Jin, et al. ∙ 1 ∙ share

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Approximate Multiplication of Sparse Matrices with Limited Space

Approximate matrix multiplication with limited space has received ever-i...
09/08/2020 ∙ by Yuanyu Wan, et al. ∙ 0 ∙ share

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Verifying Pufferfish Privacy in Hidden Markov Models

Pufferfish is a Bayesian privacy framework for designing and analyzing p...
08/04/2020 ∙ by Depeng Liu, et al. ∙ 0 ∙ share

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Dynamic Regret of Convex and Smooth Functions

We investigate online convex optimization in non-stationary environments...
07/07/2020 ∙ by Peng Zhao, et al. ∙ 0 ∙ share

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Proving Non-Inclusion of Büchi Automata based on Monte Carlo Sampling

The search for a proof of correctness and the search for counterexamples...
07/05/2020 ∙ by Yong Li, et al. ∙ 0 ∙ share

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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...
06/10/2020 ∙ by Peng Zhao, et al. ∙ 0 ∙ share

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On the Power of Unambiguity in Büchi Complementation

In this work, we exploit the power of unambiguity for the complementatio...
05/18/2020 ∙ by Yong Li, et al. ∙ 0 ∙ share

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Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs

In this paper, we study the problem of stochastic linear bandits with fi...
04/28/2020 ∙ by Bo Xue, et al. ∙ 5 ∙ share

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Minimizing Dynamic Regret and Adaptive Regret Simultaneously

Regret minimization is treated as the golden rule in the traditional stu...
02/06/2020 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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Multi-attention Networks for Temporal Localization of Video-level Labels

Temporal localization remains an important challenge in video understand...
11/15/2019 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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More Adaptive Algorithms for Tracking the Best Expert

In this paper, we consider the problem of prediction with expert advice ...
09/05/2019 ∙ by Shiyin Lu, et al. ∙ 0 ∙ share

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Stochastic Optimization for Non-convex Inf-Projection Problems

In this paper, we study a family of non-convex and possibly non-smooth i...
08/26/2019 ∙ by Yan Yan, et al. ∙ 0 ∙ share

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Bandit Convex Optimization in Non-stationary Environments

Bandit Convex Optimization (BCO) is a fundamental framework for modeling...
07/29/2019 ∙ by Peng Zhao, et al. ∙ 4 ∙ share

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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 measure---adaptive...
06/26/2019 ∙ by Lijun Zhang, et al. ∙ 2 ∙ share

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Multi-Objective Generalized Linear Bandits

In this paper, we study the multi-objective bandits (MOB) problem, where...
05/30/2019 ∙ by Shiyin Lu, et al. ∙ 0 ∙ share

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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...
05/28/2019 ∙ by Pengcheng Li, et al. ∙ 16 ∙ share

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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 ...
05/15/2019 ∙ by Guanghui Wang, et al. ∙ 0 ∙ share

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SAdam: A Variant of Adam for Strongly Convex Functions

The Adam algorithm has become extremely popular for large-scale machine ...
05/08/2019 ∙ by Guanghui Wang, et al. ∙ 0 ∙ share

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Prediction with Unpredictable Feature Evolution

Feature space can change or evolve when learning with streaming data. Se...
04/27/2019 ∙ by Bo-Jian Hou, et al. ∙ 30 ∙ share

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Adaptive Regret of Convex and Smooth Functions

We investigate online convex optimization in changing environments, and ...
04/26/2019 ∙ by Lijun Zhang, et al. ∙ 10 ∙ share

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Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√(T)) for Problems without Bilinear Structure

Previous studies on stochastic primal-dual algorithms for solving min-ma...
04/23/2019 ∙ by Yan Yan, et al. ∙ 0 ∙ share

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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...
01/27/2019 ∙ by Lijun Zhang, et al. ∙ 12 ∙ share

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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...
12/03/2018 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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Adaptive Online Learning in Dynamic Environments

In this paper, we study online convex optimization in dynamic environmen...
10/25/2018 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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Query-Efficient Black-Box Attack by Active Learning

Deep neural network (DNN) as a popular machine learning model is found t...
09/13/2018 ∙ by Pengcheng Li, et al. ∙ 0 ∙ share

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Matrix Completion from Non-Uniformly Sampled Entries

In this paper, we consider matrix completion from non-uniformly sampled ...
06/27/2018 ∙ by Yuanyu Wan, et al. ∙ 4 ∙ share

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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...
05/11/2018 ∙ by Mingrui Liu, et al. ∙ 0 ∙ share

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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 ...
05/06/2018 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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ℓ_1-regression with Heavy-tailed Distributions

In this paper, we consider the problem of linear regression with heavy-t...
05/02/2018 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness

Longitudinal data are common in clinical trials and observational studie...
04/20/2018 ∙ by Chixiang Chen, et al. ∙ 0 ∙ share

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VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

In this paper, we propose a simple variant of the original SVRG, called ...
02/26/2018 ∙ by Fanhua Shang, et al. ∙ 0 ∙ share

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Verifying Probabilistic Timed Automata Against Omega-Regular Dense-Time Properties

Probabilistic timed automata (PTAs) are timed automata (TAs) extended wi...
12/01/2017 ∙ by Hongfei Fu, et al. ∙ 0 ∙ share

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A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer

In this paper, we present a simple analysis of fast rates with high pr...
09/09/2017 ∙ by Tianbao Yang, et al. ∙ 0 ∙ share

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Learning with Feature Evolvable Streams

Learning with streaming data has attracted much attention during the pas...
06/16/2017 ∙ by Bo-Jian Hou, et al. ∙ 0 ∙ share

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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...
08/11/2016 ∙ by Tianbao Yang, et al. ∙ 0 ∙ share

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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...
05/16/2016 ∙ by Tianbao Yang, et al. ∙ 0 ∙ share

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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...
05/04/2015 ∙ by Tianbao Yang, et al. ∙ 0 ∙ share

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Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion

In this paper, we provide a theoretical analysis of the nuclear-norm reg...
04/26/2015 ∙ by Lijun Zhang, et al. ∙ 0 ∙ share

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Theory of Dual-sparse Regularized Randomized Reduction

In this paper, we study randomized reduction methods, which reduce high-...
04/15/2015 ∙ by Tianbao Yang, et al. ∙ 0 ∙ share

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