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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1101–1150 of 2050 papers

TitleStatusHype
Rejoinder on: Minimal penalties and the slope heuristics: a survey—0
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks—0
Parameter-wise co-clustering for high-dimensional data—0
Reliable ABC model choice via random forests—0
Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line—0
Response-based Learning for Machine Translation of Open-domain Database Queries—0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Rethinking Parameter Counting: Effective Dimensionality Revisited—0
Understanding Model Selection For Learning In Strategic Environments—0
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
Revealing consensus and dissensus between network partitions—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search—0
Ridge Regression Revisited: Debiasing, Thresholding and Bootstrap—0
Rising Rested Bandits: Lower Bounds and Efficient Algorithms—0
Risk Aware Benchmarking of Large Language Models—0
Risk-consistency of cross-validation with lasso-type procedures—0
Risk-Controlling Model Selection via Guided Bayesian Optimization—0
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems—0
Robust and Parallel Bayesian Model Selection—0
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs—0
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption—0
Robust Graphical Modeling with t-Distributions—0
Robust high dimensional factor models with applications to statistical machine learning—0
Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models—0
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations—0
Robust Model Selection of Gaussian Graphical Models—0
Robustness in sparse linear models: relative efficiency based on robust approximate message passing—0
Robust & Precise Knowledge Distillation-based Novel Context-Aware Predictor for Disease Detection in Brain and Gastrointestinal—0
Robust Output Analysis with Monte-Carlo Methodology—0
Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background—0
Robust Regression with Twinned Gaussian Processes—0
Robust Social Planning—0
S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning—0
Saliency Revisited: Analysis of Mouse Movements versus Fixations—0
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles—0
Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications—0
Sampling Requirements for Stable Autoregressive Estimation—0
Saturn: Efficient Multi-Large-Model Deep Learning—0
Scalable Bayesian Transformed Gaussian Processes—0
Scalable Model Selection for Belief Networks—0
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks—0
Scaling Inference-Efficient Language Models—0
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments—0
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics—0
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes—0
SCAMS: Simultaneous Clustering and Model Selection—0
Score-based Causal Learning in Additive Noise Models—0
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