5 citations · 7 across the 7 of their papers we have counts for
22 papers
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…
Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search
Taoan Huang, Bistra Dilkina, Sven Koenig
Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent path finding. At the high level, CBS repeatedly detects conflicts and resolves one of them by splitting…
Video Game Level Repair via Mixed Integer Linear Programming
Hejia Zhang, Matthew C. Fontaine, Amy K. Hoover +3
Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples. Howe…