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05/25/2023
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
Collaborative learning techniques have the potential to enable training ...
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05/25/2023
Strategic Data Sharing between Competitors
Collaborative learning techniques have significantly advanced in recent ...
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12/20/2022
Human-Guided Fair Classification for Natural Language Processing
Text classifiers have promising applications in high-stake tasks such as...
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06/24/2022
Data Leakage in Federated Averaging
Recent attacks have shown that user data can be recovered from FedSGD up...
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06/22/2021
FLEA: Provably Fair Multisource Learning from Unreliable Training Data
Fairness-aware learning aims at constructing classifiers that not only m...
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02/11/2021
Fairness-Aware Learning from Corrupted Data
Addressing fairness concerns about machine learning models is a crucial ...
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02/11/2021
Fairness Through Regularization for Learning to Rank
Given the abundance of applications of ranking in recent years, addressi...
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02/24/2020
On the Sample Complexity of Adversarial Multi-Source PAC Learning
We study the problem of learning from multiple untrusted data sources, a...
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01/29/2019
Robust Learning from Untrusted Sources
Modern machine learning methods often require more data for training tha...
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09/27/2018
The Convergence of Sparsified Gradient Methods
Distributed training of massive machine learning models, in particular d...
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03/23/2018