TPE
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c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization
arXiv:2211.14411v5 Announce Type: replace Abstract: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as on memory usage or latency, on top of the performance requirement. In this work, we propose constrained TPE (c-TPE), an extension of the widely-used versatile Bayesian optimization method, tree-structured Parzen estimator (TPE), to handle these constraints.
Are the Parker and Focused Transport Equations Equivalent for Galactic Cosmic Ray Modulation?
arXiv:2606.09298v1 Announce Type: new Abstract: The Parker transport equation (TPE) has been the equation of choice for the past 60 years in studies of galactic cosmic ray (GCR) modulation. Conversely, the focused TPE describes the same processes on a more fundamental level than the Parker TPE by modelling an anisotropic distribution rather than an isotropic one. It is usually assumed that the Parker TPE is valid for modelling GCRs, but the two TPEs have not been tested against each other in...
Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance
arXiv:2304.11127v5 Announce Type: replace Abstract: Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structured Parzen estimator (TPE) is a widely used Bayesian optimization method in recent parameter tuning frameworks such as Hyperopt and Optuna. Despite its popularity, the roles of each control parameter in TPE and the algorithm intuition have not been discussed so far.
LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
Announce Type: new Abstract: Retrieval systems underpin modern AI applications -- spanning visual search, recommendation engines, and multi-modal question answering. Modern multi-stage retrieval systems require the joint optimization of highly coupled parameters, yet traditional hyperparameter optimization (HPO) methods -- including Tree-structured Parzen Estimators (TPE) and Gaussian Process Bayesian Optimization -- rely on an independence assumption that fundamentally prevents them from...
How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
Announce Type: new Abstract: Hyperparameter optimization (HPO) for Random Forest faces a specific difficulty in tuning the number of trees: the predictive score typically improves monotonically with ensemble size, so standard methods such as Tree-structured Parzen Estimator (TPE) and Hyperband require a predefined search range and often drive the estimate toward its right boundary. Early-stopping strategies avoid fixing such a range, but can be sensitive to score noise and prone to premature...
Can LLMs Beat Classical Hyperparameter Optimization Algorithms?
Computer Science > Machine Learning [Submitted on 25 Mar 2026 (v1), last revised 17 Apr 2026 (this version, v5)] Title:Can LLMs Beat Classical Hyperparameter Optimization Algorithms?
Spain's total solar eclipse 2026 comes with a catch — here's how to avoid ruining your view
Spain's total solar eclipse 2026 comes with a catch — here's how to avoid ruining your view With the eclipse occurring near sunset over a complex landscape, eclipse chasers must do their research before the big moment on Aug. 12, 2026. On Aug. 12, 2026, millions of people across Spain will witness a solar eclipse. Trouble is, some will think they're seeing the main event when they're not, while others will have their view of the all-important, 100% eclipsed sun blocked by mountains or clouds.