1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
Learning Pseudo-Backdoors for Mixed Integer Programs
Aaron Ferber, Jialin Song, Bistra Dilkina +1
We propose a machine learning approach for quickly solving Mixed Integer Programs (MIP) by learning to prioritize a set of decision variables, which we call pseudo-backdoors, for b…
cs.LG2021
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Umang Gupta, Aaron M Ferber, Bistra Dilkina +1
Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the da…
cs.LG2019
MIPaaL: Mixed Integer Program as a Layer
Aaron Ferber, Bryan Wilder, Bistra Dilkina +1
Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures accuracy between…