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04/21/2023
Noise-Reuse in Online Evolution Strategies
Online evolution strategies have become an attractive alternative to aut...
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02/20/2023
Progressive Knowledge Distillation: Building Ensembles for Efficient Inference
We study the problem of progressive distillation: Given a large, pre-tra...
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02/16/2023
Federated Learning as a Network Effects Game
Federated Learning (FL) aims to foster collaboration among a population ...
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02/06/2023
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts
Training machine learning models robust to distribution shifts is critic...
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12/17/2022
On Noisy Evaluation in Federated Hyperparameter Tuning
Hyperparameter tuning is critical to the success of federated learning a...
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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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11/21/2022
Validating Large Language Models with ReLM
Although large language models (LLMs) have been touted for their ability...
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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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06/16/2022
On Privacy and Personalization in Cross-Silo Federated Learning
While the application of differential privacy (DP) has been well-studied...
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06/03/2022
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions
Supervised learning methods trained with maximum likelihood objectives o...
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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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03/18/2022
Provably Fair Federated Learning via Bounded Group Loss
In federated learning, fair prediction across various protected groups (...
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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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11/07/2021
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
Input pipelines, which ingest and transform input data, are an essential...
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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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08/30/2021
Private Multi-Task Learning: Formulation and Applications to Federated Learning
Many problems in machine learning rely on multi-task learning (MTL), in ...
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06/15/2021
On Large-Cohort Training for Federated Learning
Federated learning methods typically learn a model by iteratively sampli...
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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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03/01/2021
Heterogeneity for the Win: One-Shot Federated Clustering
In this work, we explore the unique challenges – and opportunities – of ...
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02/23/2021
Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods
In this work we introduce a simple baseline for meta-learning. Our uncon...
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02/17/2021
Label Leakage and Protection in Two-party Split Learning
In vertical federated learning, two-party split learning has become an i...
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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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11/28/2020
Is Support Set Diversity Necessary for Meta-Learning?
Meta-learning is a popular framework for learning with limited data in w...
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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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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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11/01/2019
Progressive Compressed Records: Taking a Byte out of Deep Learning Data
Deep learning training accesses vast amounts of data at high velocity, p...
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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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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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02/28/2019
One-Shot Federated Learning
We present one-shot federated learning, where a central server learns a ...
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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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10/11/2018
Efficient Augmentation via Data Subsampling
Data augmentation is commonly used to encode invariances in learning met...
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03/16/2018
A Kernel Theory of Modern Data Augmentation
Data augmentation, a technique in which a training set is expanded with ...
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05/30/2017