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06/20/2023
Personalized Federated Learning with Feature Alignment and Classifier Collaboration
Data heterogeneity is one of the most challenging issues in federated le...
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05/29/2023
The First and Second Order Asymptotics of Covert Communication over AWGN Channels
This paper investigates the asymptotics of the maximal throughput of com...
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03/11/2023
Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout
The label distribution skew induced data heterogeniety has been shown to...
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10/24/2022
Revisiting Sparse Convolutional Model for Visual Recognition
Despite strong empirical performance for image classification, deep neur...
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05/13/2022
An Information-theoretic Method for Collaborative Distributed Learning with Limited Communication
In this paper, we study the information transmission problem under the d...
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09/29/2021
PAC-Bayes Information Bottleneck
Information bottleneck (IB) depicts a trade-off between the accuracy and...
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09/14/2021
On Distributed Learning with Constant Communication Bits
In this paper, we study a distributed learning problem constrained by co...
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09/13/2021
SignGuard: Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering
Gradient-based training in federated learning is known to be vulnerable ...
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08/24/2021
Maximum Likelihood Estimation for Multimodal Learning with Missing Modality
Multimodal learning has achieved great successes in many scenarios. Comp...
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07/30/2021
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning
Gradient quantization is an emerging technique in reducing communication...
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02/27/2021
Lifelong Learning based Disease Diagnosis on Clinical Notes
Current deep learning based disease diagnosis systems usually fall short...
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12/17/2020
Predicting Events in MOBA Games: Dataset, Attribution, and Evaluation
The multiplayer online battle arena (MOBA) games have become increasingl...
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09/17/2020
Finding Influential Instances for Distantly Supervised Relation Extraction
Distant supervision has been demonstrated to be highly beneficial to enh...
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09/06/2020
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer d...
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09/06/2020
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
Counterfactual learning for dealing with missing-not-at-random data (MNA...
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08/14/2020
Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport
With the rapid prevalence and explosive development of MOBA esports (Mul...
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01/25/2020
On the Fairness of Randomized Trials for Recommendation With Heterogeneous Demographics and Beyond
Observed events in recommendation are consequence of the decisions made ...
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12/03/2019
Less Is Better: Unweighted Data Subsampling via Influence Function
In the time of Big Data, training complex models on large-scale data set...
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11/20/2019
On Universal Features for High-Dimensional Learning and Inference
We consider the problem of identifying universal low-dimensional feature...
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10/28/2019
Reproducing Scientific Experiment with Cloud DevOps
The reproducibility of scientific experiment is vital for the advancemen...
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10/08/2019
An Information-theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data
In this paper, we propose an information-theoretic approach to design th...
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06/30/2019
On the Sample Complexity of HGR Maximal Correlation Functions
The Hirschfeld-Gebelein-Rényi (HGR) maximal correlation and the correspo...
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05/16/2019
An Information Theoretic Interpretation to Deep Neural Networks
It is commonly believed that the hidden layers of deep neural networks (...
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11/22/2018