13 citations · 69 across the 19 of their papers we have counts for
15 papers · 1 filter
Preference-Guided Diffusion for Multi-Objective Offline Optimization
Yashas Annadani, Syrine Belakaria, Stefano Ermon +2
Offline multi-objective optimization aims to identify Pareto-optimal solutions given a dataset of designs and their objective values. In this work, we propose a preference-guided d…
Non-Myopic Multi-Objective Bayesian Optimization
Syrine Belakaria, Alaleh Ahmadianshalchi, Barbara Engelhardt +2
We consider the problem of finite-horizon sequential experimental design to solve multi-objective optimization (MOO) of expensive black-box objective functions. This problem arises…
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…
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…
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach
Syrine Belakaria, Janardhan Rao Doppa, Nicolo Fusi +1
The rising growth of deep neural networks (DNNs) and datasets in size motivates the need for efficient solutions for simultaneous model selection and training. Many methods for hyp…
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…
Bayesian Optimization over Permutation Spaces
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa +1
Optimizing expensive to evaluate black-box functions over an input space consisting of all permutations of d objects is an important problem with many real-world applications. For…
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…
Bayesian Optimization over Hybrid Spaces
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa
We consider the problem of optimizing hybrid structures (mixture of discrete and continuous input variables) via expensive black-box function evaluations. This problem arises in ma…
Mercer Features for Efficient Combinatorial Bayesian Optimization
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa
Bayesian optimization (BO) is an efficient framework for solving black-box optimization problems with expensive function evaluations. This paper addresses the BO problem setting fo…
Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa +1
We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). The key challenge is to select a sequence of combinatorial…
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-…