Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Robust Unscented Kalman Filtering via Recurrent Meta-Adaptation of Sigma-Point Weights
The Unscented Kalman Filter (UKF) is a ubiquitous tool for nonlinear state estimation; however, its performance is limited by the static parameterization of the Unscented Transform (UT). Conventional weighting schemes, g…
Hyperparameter OptimizationCryo-Bench: Benchmarking Foundation Models for Cryosphere Applications
Geo-Foundation Models (GFMs) have been evaluated across diverse Earth observation task including multiple domains and have demonstrated strong potential of producing reliable maps even with sparse labels. However, benchm…
Hyperparameter OptimizationDomain AdaptationExplainable Iterative Data Visualisation Refinement via an LLM Agent
Exploratory analysis of high-dimensional data relies on embedding the data into a low-dimensional space (typically 2D or 3D), based on which visualization plot is produced to uncover meaningful structures and to communic…
Hyperparameter OptimizationBTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to…
Hyperparameter OptimizationOn the Convergence of Single-Loop Stochastic Bilevel Optimization with Approximate Implicit Differentiation
Stochastic Bilevel Optimization has emerged as a fundamental framework for meta-learning and hyperparameter optimization. Despite the practical prevalence of single-loop algorithms--which update lower and upper variables…
Hyperparameter OptimizationComputational EfficiencyBilevel OptimizationA Controlled Comparison of Deep Learning Architectures for Multi-Horizon Financial Forecasting: Evidence from 918 Experiments
Multi-horizon price forecasting is central to portfolio allocation, risk management, and algorithmic trading, yet deep learning architectures have proliferated faster than rigorous financial benchmarks can evaluate them.…
Hyperparameter OptimizationTakeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime
Generalization measures have been studied extensively in the machine learning community to better characterize generalization gaps. However, establishing a reliable generalization measure for statistically singular model…
Hyperparameter OptimizationShobdoSetu: A Data-Centric Framework for Bengali Long-Form Speech Recognition and Speaker Diarization
Bengali is spoken by over 230 million people yet remains severely under-served in automatic speech recognition (ASR) and speaker diarization research. In this paper, we present our system for the DL Sprint 4.0 Bengali Lo…
Hyperparameter OptimizationSpeaker DiarizationSpeech RecognitionPOMDPPlanners: Open-Source Package for POMDP Planning
We present POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process (POMDP) planning algorithms. The package integrates state-of-the-art planning algorithms, a…
Hyperparameter OptimizationUnderstanding the Curse of Unrolling
Algorithm unrolling is ubiquitous in machine learning, particularly in hyperparameter optimization and meta-learning, where Jacobians of solution mappings are computed by differentiating through iterative algorithms. Alt…
Hyperparameter OptimizationBilevel OptimizationUnderstanding the Generalization of Bilevel Programming in Hyperparameter Optimization: A Tale of Bias-Variance Decomposition
Gradient-based hyperparameter optimization (HPO) have emerged recently, leveraging bilevel programming techniques to optimize hyperparameter by estimating hypergradient w.r.t. validation loss. Nevertheless, previous theo…
Hyperparameter OptimizationFew-Shot LearningExplaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
AutoClustering methods aim to automate unsupervised learning tasks, including algorithm selection (AS), hyperparameter optimization (HPO), and pipeline synthesis (PS), by often leveraging meta-learning over dataset meta-…
Hyperparameter OptimizationFeature EngineeringFeature ImportanceMachine Learning in Epidemiology
In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous a…
Interpretable Machine LearningHyperparameter OptimizationMulti-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials
Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accurac…
Hyperparameter OptimizationWeight Decay Improves Language Model Plasticity
Large language models are typically trained in two broad phases: pretraining to produce a base model, followed by further training to improve downstream performance. However, hyperparameter optimization and scaling laws …
Hyperparameter OptimizationCoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization
Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottleneck for traditional methods, which operate within rigid search spaces and lack domain awareness. While Large Language Models…
Hyperparameter OptimizationFeature EngineeringBayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows. An overarching motif of many Bayesian m…
Hyperparameter OptimizationDensity EstimationBayesian InferenceSelecting Hyperparameters for Tree-Boosting
Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple hyperparameters. In this article, we empirically compare several popular…
Hyperparameter OptimizationBilevel Optimization with Lower-Level Uniform Convexity: Theory and Algorithm
Bilevel optimization is a hierarchical framework where an upper-level optimization problem is constrained by a lower-level problem, commonly used in machine learning applications such as hyperparameter optimization. Exis…
Hyperparameter OptimizationBilevel OptimizationRobust Machine Learning Framework for Reliable Discovery of High-Performance Half-Heusler Thermoelectrics
Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high me…
Hyperparameter Optimization