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