13 citations · 69 across the 19 of their papers we have counts for
23 papers
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF
Syrine Belakaria, Joshua Kazdan, Charles Marx +5
Reinforcement learning from human feedback (RLHF) has become a cornerstone of the training and alignment pipeline for large language models (LLMs). Recent advances, such as direct…
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…
Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise
Xiaoxuan Yang, Syrine Belakaria, Biresh Kumar Joardar +5
Resistive random-access memory (ReRAM) is a promising technology for designing hardware accelerators for deep neural network (DNN) inferencing. However, stochastic noise in ReRAM c…
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…
Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs: An Information-Theoretic Approach
Aryan Deshwal, Syrine Belakaria, Ganapati Bhat +2
Mobile system-on-chips (SoCs) are growing in their complexity and heterogeneity (e.g., Arm's Big-Little architecture) to meet the needs of emerging applications, including games an…
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…
Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations that vary in the amount of resources consumed and their accuracy.…
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
Many real-world applications involve black-box optimization of multiple objectives using continuous function approximations that trade-off accuracy and resource cost of evaluation.…
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-Class Management with Sub-Class Service for Autonomous Electric Mobility On-Demand Systems
Syrine Belakaria, Mustafa Ammous, Sameh Sorour +1
Despite the significant advances in vehicle automation and electrification, the next-decade aspirations for massive deployments of autonomous electric mobility on demand (AEMoD) se…
Optimal Vehicle Dimensioning for Multi-Class Autonomous Electric Mobility On-Demand Systems
Syrine Belakaria, Mustafa Ammous, Sameh Sorour +1
Autonomous electric mobility on demand (AEMoD) has recently emerged as a cyber-physical system aiming to bring automation, electrification, and on-demand services for the future pr…
A Multi-Class Dispatching and Charging Scheme for Autonomous Electric Mobility On-Demand
Syrine Belakaria, Mustafa Ammous, Sameh Sorour +1
Despite the significant advances in vehicle automation and electrification, the next-decade aspirations for massive deployments of autonomous electric mobility on demand (AEMoD) se…