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04/22/2023
Dehazing-NeRF: Neural Radiance Fields from Hazy Images
Neural Radiance Field (NeRF) has received much attention in recent years...
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12/01/2022
Differentially Private Adaptive Optimization with Delayed Preconditioners
Privacy noise may negate the benefits of using adaptive optimizers in di...
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06/20/2022
Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data
Domain adaptive text classification is a challenging problem for the lar...
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06/18/2022
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
Personalized federated learning considers learning models unique to each...
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05/30/2022
To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning
Federated learning (FL) facilitates collaboration between a group of cli...
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04/21/2022
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
Deep learning has recently achieved significant progress in trajectory f...
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02/12/2022
Private Adaptive Optimization with Side Information
Adaptive optimization methods have become the default solvers for many m...
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12/23/2021
Reinforced Meta-path Selection for Recommendation on Heterogeneous Information Networks
Heterogeneous Information Networks (HINs) capture complex relations amon...
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09/13/2021
On Tilted Losses in Machine Learning: Theory and Applications
Exponential tilting is a technique commonly used in fields such as stati...
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06/08/2021
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Tuning hyperparameters is a crucial but arduous part of the machine lear...
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03/18/2021
You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module
3D point-cloud-based perception is a challenging but crucial computer vi...
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03/01/2021
Heterogeneity for the Win: One-Shot Federated Clustering
In this work, we explore the unique challenges – and opportunities – of ...
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12/08/2020
Federated Multi-Task Learning for Competing Constraints
In addition to accuracy, fairness and robustness are two critical concer...
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12/05/2020
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning
Contrastive learning (CL) has been successful as a powerful representati...
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10/30/2020
Cross-Domain Sentiment Classification With Contrastive Learning and Mutual Information Maximization
Contrastive learning (CL) has been successful as a powerful representati...
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07/02/2020
Tilted Empirical Risk Minimization
Empirical risk minimization (ERM) is typically designed to perform well ...
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03/07/2020
Outage Behaviors of NOMA-based Satellite Network over Shadowed-Rician Fading Channels
This paper investigates the application of non-orthogonal multiple acces...
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01/07/2020
FedDANE: A Federated Newton-Type Method
Federated learning aims to jointly learn statistical models over massive...
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11/05/2019
Enhancing the Privacy of Federated Learning with Sketching
In response to growing concerns about user privacy, federated learning h...
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11/03/2019
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches
Communication and privacy are two critical concerns in distributed learn...
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08/21/2019
Federated Learning: Challenges, Methods, and Future Directions
Federated learning involves training statistical models over remote devi...
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05/25/2019
Fair Resource Allocation in Federated Learning
Federated learning involves training statistical models in massive, hete...
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12/14/2018
On the Convergence of Federated Optimization in Heterogeneous Networks
The burgeoning field of federated learning involves training machine lea...
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12/03/2018
LEAF: A Benchmark for Federated Settings
Modern federated networks, such as those comprised of wearable devices, ...
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08/24/2017