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08/03/2023
A Novel Convolutional Neural Network Architecture with a Continuous Symmetry
This paper introduces a new Convolutional Neural Network (ConvNet) archi...
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07/22/2023
Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition
As a fundamental aspect of human life, two-person interactions contain m...
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07/12/2023
Budgeting Counterfactual for Offline RL
The main challenge of offline reinforcement learning, where data is limi...
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06/30/2023
TD Convergence: An Optimization Perspective
We study the convergence behavior of the celebrated temporal-difference ...
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06/09/2023
Data-Driven Next-Generation Wireless Networking: Embracing AI for Performance and Security
New network architectures, such as the Internet of Things (IoT), 5G, and...
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04/11/2023
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task
Resource limitations make it hard to provide all students with one of th...
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03/15/2023
Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion
Tremendous efforts have been devoted to pedestrian trajectory prediction...
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02/13/2023
Symbolic Discovery of Optimization Algorithms
We present a method to formulate algorithm discovery as program search, ...
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08/16/2022
On the Convergence of Multi-Server Federated Learning with Overlapping Area
Multi-server Federated learning (FL) has been considered as a promising ...
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07/26/2022
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception
Recently, adversarial machine learning attacks have posed serious securi...
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07/10/2022
Towards Adaptive Unknown Authentication for Universal Domain Adaptation by Classifier Paradox
Universal domain adaptation (UniDA) is a general unsupervised domain ada...
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07/01/2022
Offline Policy Optimization with Eligible Actions
Offline policy optimization could have a large impact on many real-world...
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06/06/2022
Generalized Federated Learning via Sharpness Aware Minimization
Federated Learning (FL) is a promising framework for performing privacy-...
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05/27/2022
Provably Sample-Efficient RL with Side Information about Latent Dynamics
We study reinforcement learning (RL) in settings where observations are ...
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05/11/2022
Generalized Modeling and Fundamental Limits for Multiple-Access Integrated Sensing and Communication Systems
In this paper, we propose a generalized state-dependent channel modeling...
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02/11/2022
Tiny Object Tracking: A Large-scale Dataset and A Baseline
Tiny objects, frequently appearing in practical applications, have weak ...
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01/10/2022
IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification
With the proliferation of IoT devices, researchers have developed a vari...
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01/08/2022
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Federated learning (FL) provides a high efficient decentralized machine ...
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12/02/2021
Context-Aware Online Client Selection for Hierarchical Federated Learning
Federated Learning (FL) has been considered as an appealing framework to...
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10/25/2021
Information-Theoretic Limits of Integrated Sensing and Communication with Correlated Sensing and Channel States
Integrated sensing and communication (ISAC) emerges as a new design para...
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09/21/2021
Learning to Guide Human Attention on Mobile Telepresence Robots with 360 degree Vision
Mobile telepresence robots (MTRs) allow people to navigate and interact ...
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06/21/2021
Applying VertexShuffle Toward 360-Degree Video Super-Resolution on Focused-Icosahedral-Mesh
With the emerging of 360-degree image/video, augmented reality (AR) and ...
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06/14/2021
How to Test the Randomness from the Wireless Channel for Security?
We revisit the traditional framework of wireless secret key generation, ...
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09/21/2020
NetReduce: RDMA-Compatible In-Network Reduction for Distributed DNN Training Acceleration
We present NetReduce, a novel RDMA-compatible in-network reduction archi...
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07/16/2020
Provably Good Batch Reinforcement Learning Without Great Exploration
Batch reinforcement learning (RL) is important to apply RL algorithms to...
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02/10/2020
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Off-policy evaluation in reinforcement learning offers the chance of usi...
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11/20/2019
SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation
Deep hashing methods have been proved to be effective and efficient for ...
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10/21/2019
All-Action Policy Gradient Methods: A Numerical Integration Approach
While often stated as an instance of the likelihood ratio trick [Rubinst...
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10/15/2019
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
We establish a connection between the importance sampling estimators typ...
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05/14/2019
Combining Parametric and Nonparametric Models for Off-Policy Evaluation
We consider a model-based approach to perform batch off-policy evaluatio...
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04/17/2019
Off-Policy Policy Gradient with State Distribution Correction
We study the problem of off-policy policy optimization in Markov decisio...
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02/27/2019
Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection
In this paper, we propose a light reflection based face anti-spoofing me...
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07/03/2018
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
In this work, we consider the problem of estimating a behaviour policy f...
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05/23/2018
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms
Efficient exploration is one of the key challenges for reinforcement lea...
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05/23/2018
