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09/10/2023
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data
This paper considers a type of incremental aggregated gradient (IAG) met...
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07/04/2023
SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting
Multivariate time series forecasting plays a critical role in diverse do...
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07/04/2023
Bridge the Performance Gap in Peak-hour Series Forecasting: The Seq2Peak Framework
Peak-Hour Series Forecasting (PHSF) is a crucial yet underexplored task ...
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12/02/2022
Multi-Agent Reinforcement Learning with Reward Delays
This paper considers multi-agent reinforcement learning (MARL) where the...
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10/09/2021
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
Achieving sample efficiency in online episodic reinforcement learning (R...
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06/22/2021
The Rate of Convergence of Variation-Constrained Deep Neural Networks
Multi-layer feedforward networks have been used to approximate a wide ra...
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05/17/2021
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Low-complexity models such as linear function representation play a pivo...
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04/07/2021
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps
Eigenvector perturbation analysis plays a vital role in various statisti...
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02/22/2021
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
The softmax policy gradient (PG) method, which performs gradient ascent ...
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02/12/2021
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
Q-learning, which seeks to learn the optimal Q-function of a Markov deci...
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10/02/2020
The Efficacy of L_1 Regularization in Two-Layer Neural Networks
A crucial problem in neural networks is to select the most appropriate n...
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06/04/2020
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
Asynchronous Q-learning aims to learn the optimal action-value function ...
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05/26/2020
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
We investigate the sample efficiency of reinforcement learning in a γ-di...
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07/25/2019
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
Spectral Method is a commonly used scheme to cluster data points lying c...
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07/14/2019
Compressed Subspace Learning Based on Canonical Angle Preserving Property
A standard way to tackle the challenging task of learning from high-dime...
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07/13/2019
Lower Bound for RIP Constants and Concentration of Sum of Top Order Statistics
Restricted Isometry Property (RIP) is of fundamental importance in the t...
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05/23/2019
Johnson-Lindenstrauss Property Implies Subspace Restricted Isometry Property
Dimensionality reduction is a popular approach to tackle high-dimensiona...
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04/14/2019
Minimum Error Entropy Kalman Filter
To date most linear and nonlinear Kalman filters (KFs) have been develop...
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04/18/2018
Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization
This paper addresses consensus optimization problems in a multi-agent ne...
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04/18/2018
A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization
This paper addresses consensus optimization problem in a multi-agent net...
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01/30/2018
Rigorous Restricted Isometry Property of Low-Dimensional Subspaces
Dimensionality reduction is in demand to reduce the complexity of solvin...
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12/05/2017
Linear Convergence of An Iterative Phase Retrieval Algorithm with Data Reuse
Phase retrieval has been an attractive but difficult problem rising from...
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08/16/2017
Active Orthogonal Matching Pursuit for Sparse Subspace Clustering
Sparse Subspace Clustering (SSC) is a state-of-the-art method for cluste...
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08/07/2017
Nonconvex Sparse Logistic Regression with Weakly Convex Regularization
In this work we propose to fit a sparse logistic regression model by a w...
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05/08/2017
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
This paper addresses image classification through learning a compact and...
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11/12/2015
Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints
Consider convex optimization problems subject to a large number of const...
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12/22/2010
