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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.

168,932 papers · 148 categories

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3 results for “GP-Hedge”

No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimizationstat.ML

No-PASt-BO improves GP-Hedge by reducing past influence and normalizing acquisition functions.

problem GP-Hedge's reliance on past performance can lead to poor acquisition function dominance.

method No-PASt-BO reduces past influence and normalizes acquisition functions.

result No-PASt-BO outperforms GP-Hedge on both synthetic and real-world tasks.

Bayesian Optimization Acquisition Functions GP-Hedge Normalization Past Influence

2019-08-01abs ↗pdf ↗

DEEP-BO for Hyperparameter Optimization of Deep Networkscs.LG

DEEP-BO optimizes hyperparameters of deep networks, outperforming existing methods.

problem Hyperparameter optimization of deep networks is challenging due to the complexity and sensitivity of DNN performance.

method Enhanced Bayesian Optimization (DEEP-BO) specifically designed for deep networks, incorporating diversification, early termination, and parallelism.

result DEEP-BO outperforms or matches other state-of-the-art methods on six DNN benchmarks.

hyperparameter optimization deep learning Bayesian optimization deep networks batchnorm

2019-05-23abs ↗pdf ↗

aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecturestat.ML

A new method for efficient optimization of expensive simulations on HPC.

problem Efficiently solving computationally expensive simulation-based optimization problems.

method Asynchronous parallel Bayesian optimization with budgeted computational resource.

result Improved efficiency and handling of constraints in optimization.

Bayesian optimization HPC Constrained optimization Parallel computing Asynchronous

2020-03-20abs ↗pdf ↗