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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...
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Projection-free 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 ever-i...
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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 non-stationary environments...
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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...
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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 Heavy-Tailed 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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Multi-attention Networks for Temporal Localization of Video-level 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 Non-convex Inf-Projection Problems
In this paper, we study a family of non-convex and possibly non-smooth i...
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Bandit Convex Optimization in Non-stationary 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 measure---adaptive...
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Multi-Objective Generalized Linear Bandits
In this paper, we study the multi-objective 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 large-scale 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 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...
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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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Query-Efficient Black-Box Attack by Active Learning
Deep neural network (DNN) as a popular machine learning model is found t...
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Matrix Completion from Non-Uniformly Sampled Entries
In this paper, we consider matrix completion from non-uniformly 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_1-regression with Heavy-tailed Distributions
In this paper, we consider the problem of linear regression with heavy-t...
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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...
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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 ...
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Verifying Probabilistic Timed Automata Against Omega-Regular Dense-Time Properties
Probabilistic timed automata (PTAs) are timed automata (TAs) extended wi...
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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...
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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 Full-rank Matrix Completion
In this paper, we provide a theoretical analysis of the nuclear-norm reg...
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Theory of Dual-sparse Regularized Randomized Reduction
In this paper, we study randomized reduction methods, which reduce high-...
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Binary Excess Risk for Smooth Convex Surrogates
In statistical learning theory, convex surrogates of the 0-1 loss are hi...
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Beating the Minimax Rate of Active Learning with Prior Knowledge
Active learning refers to the learning protocol where the learner is all...
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Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections
We consider stochastic strongly convex optimization with a complex inequ...
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