79 citations · 292 across the 52 of their papers we have counts for
4 papers · 2 filters
Probabilistically robust conformal prediction
Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja +3
Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, C…
Preference-Aware Constrained Multi-Objective Bayesian Optimization
Alaleh Ahmadianshalchi, Syrine Belakaria, Janardhan Rao Doppa
This paper addresses the problem of constrained multi-objective optimization over black-box objective functions with practitioner-specified preferences over the objectives when a l…
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis
Subhankar Ghosh, Taha Belkhouja, Yan Yan +1
Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled fra…
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
Aryan Deshwal, Sebastian Ament, Maximilian Balandat +3
We consider the problem of optimizing expensive black-box functions over high-dimensional combinatorial spaces which arises in many science, engineering, and ML applications. We us…