9 citations · 36 across the 35 of their papers we have counts for
5 papers · 1 filter
Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework
Elias B. Khalil, Pashootan Vaezipoor, Bistra Dilkina
In Mixed Integer Linear Programming (MIP), a (strong) backdoor is a "small" subset of an instance's integer variables with the following property: in a branch-and-bound procedure,…
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling
Kai Wang, Bryan Wilder, Sze-chuan Suen +2
There is significant interest in learning and optimizing a complex system composed of multiple sub-components, where these components may be agents or autonomous sensors. Among the…
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…
Becoming Good at AI for Good
Meghana Kshirsagar, Caleb Robinson, Siyu Yang +13
AI for good (AI4G) projects involve developing and applying artificial intelligence (AI) based solutions to further goals in areas such as sustainability, health, humanitarian aid,…
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…