79 citations · 292 across the 52 of their papers we have counts for
5 papers · 2 filters
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
Syrine Belakaria, Benjamin Letham, Janardhan Rao Doppa +3
We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Our aim is to efficiently learn the importance of different input varia…
Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization
Alaleh Ahmadianshalchi, Syrine Belakaria, Janardhan Rao Doppa
We consider the problem of multi-objective optimization (MOO) of expensive black-box functions with the goal of discovering high-quality and diverse Pareto fronts where we are allo…
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration
Yuanjie Shi, Subhankar Ghosh, Taha Belkhouja +2
Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or…
Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach
Mohammed Amine Gharsallaoui, Bhupinderjeet Singh, Supriya Savalkar +5
Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-ba…
Offline Model-Based Optimization via Policy-Guided Gradient Search
Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang +1
Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation…