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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 1351–1400 of 2050 papers

TitleStatusHype
Learning Dynamic Hierarchical Models for Anytime Scene Labeling—0
Learning for Multi-Model and Multi-Type Fitting—0
Learning from Domain Complexity—0
Learning from missing data with the Latent Block Model—0
Learning Gaussian Graphical Models via Multiplicative Weights—0
Learning high-dimensional probability distributions using tree tensor networks—0
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm—0
Learning manifold to regularize nonnegative matrix factorization—0
Learning of networked spreading models from noisy and incomplete data—0
Learning Sparse Neural Networks through L_0 Regularization—0
Learning Structural Kernels for Natural Language Processing—0
Learning the hypotheses space from data through a U-curve algorithm—0
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
Learning the Hypotheses Space from data Part II: Convergence and Feasibility—0
Learning the Markov order of paths in a network—0
Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Learning under Singularity: An Information Criterion improving WBIC and sBIC—0
Learning Vine Copula Models For Synthetic Data Generation—0
Learning with many experts: model selection and sparsity—0
Learning with tree tensor networks: complexity estimates and model selection—0
Learning Word-Level Confidence For Subword End-to-End ASR—0
Least Angle Regression in Tangent Space and LASSO for Generalized Linear Models—0
( β, )-stability for cross-validation and the choice of the number of folds—0
Leveraging free energy in pretraining model selection for improved fine-tuning—0
Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages—0
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks—0
Lexical Bias In Essay Level Prediction—0
Lifelong Bayesian Optimization—0
A Geometric Modeling of Occam's Razor in Deep Learning—0
Likelihood Adaptively Modified Penalties—0
Limits of Model Selection under Transfer Learning—0
LIMSI@CoNLL'17: UD Shared Task—0
Linear Bandits with Memory: from Rotting to Rising—0
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior—0
LLM4DS: Evaluating Large Language Models for Data Science Code Generation—0
LLMProxy: Reducing Cost to Access Large Language Models—0
LM-BIC Model Selection in Semiparametric Models—0
Logistic principal component analysis via non-convex singular value thresholding—0
Logits-Constrained Framework with RoBERTa for Ancient Chinese NER—0
Lookback for Learning to Branch—0
"Look Ma, No Hands!" A Parameter-Free Topic Model—0
Loss function based second-order Jensen inequality and its application to particle variational inference—0
Loss-guided Stability Selection—0
Lossy Compression with Distortion Constrained Optimization—0
Lower Bounds on Active Learning for Graphical Model Selection—0
Luria-Delbruck, revisited: The classic experiment does not rule out Lamarckian evolution—0
Machine Learning: a Lecture Note—0
Machine Learning-Assisted Analysis of Small Angle X-ray Scattering—0
Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation—0
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