283 citations · 588 across the 29 of their papers we have counts for
5 papers · 1 filter
Navigating Ensemble Configurations for Algorithmic Fairness
Michael Feffer, Martin Hirzel, Samuel C. Hoffman +3
Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits. A popular approach to train more s…
Advancing Model Pruning via Bi-level Optimization
Yihua Zhang, Yuguang Yao, Parikshit Ram +5
The deployment constraints in practical applications necessitate the pruning of large-scale deep learning models, i.e., promoting their weight sparsity. As illustrated by the Lotte…
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning
Alex Gu, Songtao Lu, Parikshit Ram +1
We consider a generic min-max multi-objective bilevel optimization problem with applications in robust machine learning such as representation learning and hyperparameter optimizat…
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
Yi Zhou, Parikshit Ram, Theodoros Salonidis +3
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a gener…
An Empirical Study of Modular Bias Mitigators and Ensembles
Michael Feffer, Martin Hirzel, Samuel C. Hoffman +3
There are several bias mitigators that can reduce algorithmic bias in machine learning models but, unfortunately, the effect of mitigators on fairness is often not stable when meas…