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05/28/2023
On the Value of Myopic Behavior in Policy Reuse
Leveraging learned strategies in unfamiliar scenarios is fundamental to ...
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07/29/2022
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
In view of its power in extracting feature representation, contrastive s...
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07/25/2022
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
While single-agent policy optimization in a fixed environment has attrac...
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06/12/2022
Stochastic Gradient Descent without Full Data Shuffle
Stochastic gradient descent (SGD) is the cornerstone of modern machine l...
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02/25/2022
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
Dynamic mechanism design studies how mechanism designers should allocate...
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11/27/2021
Safe Screening for Sparse Conditional Random Fields
Sparse Conditional Random Field (CRF) is a powerful technique in compute...
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10/19/2021
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
To achieve sample efficiency in reinforcement learning (RL), it necessit...
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07/19/2021
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer...
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08/23/2020
Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning
Temporal-Difference (TD) learning with nonlinear smooth function approxi...
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06/22/2020
Beyond O(√(T)) Regret for Constrained Online Optimization: Gradual Variations and Mirror Prox
We study constrained online convex optimization, where the constraints c...
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05/12/2020
Energy-Aware DNN Graph Optimization
Unlike existing work in deep neural network (DNN) graphs optimization fo...
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03/02/2020
Upper Confidence Primal-Dual Optimization: Stochastically Constrained Markov Decision Processes with Adversarial Losses and Unknown Transitions
We consider online learning for episodic Markov decision processes (MDPs...
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10/11/2019
Central Server Free Federated Learning over Single-sided Trust Social Networks
Federated learning has become increasingly important for modern machine ...
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09/25/2019
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Graph node embedding aims at learning a vector representation for all no...
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08/14/2019
Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rates and Global Landscape Analysis
We study the robust one-bit compressed sensing problem whose goal is to ...
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07/17/2019
DeepSqueeze: Decentralization Meets Error-Compensated Compression
Communication is a key bottleneck in distributed training. Recently, an ...
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07/17/2019
DeepSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
Communication is a key bottleneck in distributed training. Recently, an ...
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02/14/2019
A Covariance-Based Hybrid Channel Feedback in FDD Massive MIMO Systems
In this paper, a novel covariance-based channel feedback mechanism is in...
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01/30/2019
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
Factorization machine (FM) is a popular machine learning model to captur...
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10/08/2018
Proximal Online Gradient is Optimum for Dynamic Regret
In online learning, the dynamic regret metric chooses the reference (opt...
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07/06/2018
Enabling Covariance-Based Feedback in Massive MIMO: A User Classification Approach
In this paper, we propose a novel channel feedback scheme for frequency ...
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05/28/2018
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning
Graph embedding is a central problem in social network analysis and many...
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11/29/2017
Downlink Precoding with Mixed Statistical and Imperfect Instantaneous CSI for Massive MIMO Systems
In this paper, the feasibility of a new downlink transmission mode in ma...
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03/17/2017
Nonconvex One-bit Single-label Multi-label Learning
We study an extreme scenario in multi-label learning where each training...
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03/02/2017
The Second Order Linear Model
We study a fundamental class of regression models called the second orde...
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08/02/2015
