On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,181 papers · 148 categories

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5 results for “GP-EI”

Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbentstat.ML

Paper analyzes GP-EI for Bayesian optimization with no regret and provides guidance on choosing incumbents.

problem Analyzing cumulative regret of GP-EI with different incumbents in noisy Bayesian optimization.

method Analyzes GP-EI with three incumbents (BPMI, BSPMI, BOI) in both SE and Matérn kernels, proving no-regret for BPMI and BSPMI.

result GP-EI with BPMI and BSPMI is a no-regret algorithm for both SE and Matérn kernels, providing theoretical guidance for choosing incumbents.

Bayesian optimization Expected Improvement Cumulative Regret Noisy Gaussian Process No Regret

2025-08-21abs ↗pdf ↗

AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EIcs.LG

Paper tackles efficient allocation of multiple devices to users for AutoML services.

problem Allocating multiple devices to multiple users for AutoML services efficiently.

method Develops a multi-device, multi-tenant algorithm for GP-EI, achieving near-linear speedup.

result Achieves near-linear speedup when users are many more than devices.

AutoML GP-EI Multi-device Multi-tenant Regret bound

2018-03-17abs ↗pdf ↗

On the convergence rate of noisy Bayesian Optimization with Expected Improvementstat.ML

Paper analyzes convergence rate of noisy Bayesian Optimization with Expected Improvement.

problem Theoretical convergence behaviors and rates of Expected Improvement (EI) in Bayesian optimization.

method Analyzes Expected Improvement (EI) under Gaussian process (GP) prior assumption, considering noisy observations.

result Established asymptotic error bound and rate for GP-EI with noisy observations.

Bayesian Optimization Expected Improvement Gaussian Process Convergence Rate Noisy Observations

2025-01-16abs ↗pdf ↗

Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogatescs.AI

Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has been proposed for optimizing hyperparameters of various machine learning algorithms. Those methods adopt probabilistic surrogate models like G…

2016-07-28abs ↗pdf ↗

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Timesstat.ML

Efficiently optimize GPs by reusing candidate solutions multiple times.

problem High computational cost of Gaussian process optimization due to unique historical points.

method Sticking to a candidate solution for multiple evaluation steps and limiting switches.

result Improved efficiency and practicality of Gaussian process optimization algorithms.

Gaussian process optimization active learning sequential decision making active sampling batched optimization

2022-01-30abs ↗pdf ↗