79 citations · 172 across the 14 of their papers we have counts for
9 papers · 1 filter
Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization
Syrine Belakaria, Aryan Deshwal, Nitthilan Kannappan Jayakodi +1
We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions while…
Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
We consider the problem of black-box multi-objective optimization (MOO) using expensive function evaluations (also referred to as experiments), where the goal is to approximate the…
Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
We consider the problem of constrained multi-objective blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions…
Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
We consider the problem of constrained multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solu…
Scalable Combinatorial Bayesian Optimization with Tractable Statistical models
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa
We study the problem of optimizing expensive blackbox functions over combinatorial spaces (e.g., sets, sequences, trees, and graphs). BOCS (Baptista and Poloczek, 2018) is a state-…
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data
Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook
Domain adaptation (DA) offers a valuable means to reuse data and models for new problem domains. However, robust techniques have not yet been considered for time series data with v…