48 citations · 58 across the 17 of their papers we have counts for
17 papers
Trajectory-Aware Best-Arm Identification for Local Search Allocation in Bayesian Optimization and Beyond
Nobuo Namura, Sho Takemori
Bayesian optimization (BO) is effective for expensive black-box optimization, but its performance often degrades on multimodal or high-dimensional problems. A promising remedy is t…
Adaptive LLM Routing under Budget Constraints
Pranoy Panda, Raghav Magazine, Chaitanya Devaguptapu +2
Large Language Models (LLMs) have revolutionized natural language processing, but their varying capabilities and costs pose challenges in practical applications. LLM routing addres…
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization
Nobuo Namura, Sho Takemori
Real-world optimization problems often involve complex objective functions with costly evaluations. While Bayesian optimization (BO) with Gaussian processes is effective for these…
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori +3
The rise in internet usage has led to the generation of massive amounts of data, resulting in the adoption of various supervised and semi-supervised machine learning algorithms, wh…
Towards Accurate Quantum Chemical Calculations on Noisy Quantum Computers
Naoki Iijima, Satoshi Imamura, Mikio Morita +4
Variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for noisy intermediate-scale quantum (NISQ) computers. It is promising for quantum chemical c…
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori +3
Self-training based semi-supervised learning algorithms have enabled the learning of highly accurate deep neural networks, using only a fraction of labeled data. However, the major…