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07/18/2023
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
We introduce a library, Dataset Grouper, to create large-scale group-str...
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05/29/2023
Unleashing the Power of Randomization in Auditing Differentially Private ML
We present a rigorous methodology for auditing differentially private ma...
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05/18/2023
Modified Gauss-Newton Algorithms under Noise
Gauss-Newton methods and their stochastic version have been widely used ...
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12/10/2022
Stochastic Optimization for Spectral Risk Measures
Spectral risk objectives - also called L-risks - allow for learning syst...
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04/08/2022
Federated Learning with Partial Model Personalization
We consider two federated learning algorithms for training partially per...
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12/17/2021
Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach
We present a federated learning framework that is designed to robustly d...
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06/15/2021
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral
The spectacular success of deep generative models calls for quantitative...
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06/02/2021
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Learning binary representations of instances and classes is a classical ...
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02/02/2021
MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation
Despite major advances in open-ended text generation, there has been lim...
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02/25/2020
Device Heterogeneity in Federated Learning: A Superquantile Approach
We propose a federated learning framework to handle heterogeneous client...
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12/31/2019
Robust Aggregation for Federated Learning
We present a robust aggregation approach to make federated learning robu...
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02/08/2019
