SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1225 of 2050 papers

TitleStatusHype
Fitting very flexible models: Linear regression with large numbers of parameters—0
Detecting seasonal episodic-like spatiotemporal memory patterns using animal movement modelling—0
General Hannan and Quinn Criterion for Common Time Series—0
Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way—0
Can We Use Gradient Norm as a Measure of Generalization Error for Model Selection in Practice?—0
Continual Learning Without Knowing Task Identities: Rethinking Occam's Razor—0
Rethinking Parameter Counting: Effective Dimensionality Revisited—0
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
Explainable Multi-class Classification of Medical Data—0
Upper Confidence Bounds for Combining Stochastic Bandits—0
On Statistical Efficiency in LearningCode0
Leave Zero Out: Towards a No-Cross-Validation Approach for Model SelectionCode0
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL—0
Flexible, Non-parametric Modeling Using Regularized Neural NetworksCode0
Speech Enhancement with Zero-Shot Model SelectionCode0
Odd-One-Out Representation LearningCode0
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation—0
Smooth Bandit Optimization: Generalization to Hölder Space—0
Comparison of Anomaly Detectors: Context MattersCode0
Conjugate Mixture Models for Clustering Multimodal Data—0
Efficient model selection in switching linear dynamic systems by graph clustering—0
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
Online Model Selection: a Rested Bandit Formulation—0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Machine learning with incomplete datasets using multi-objective optimization models—0
Show:102550
← PrevPage 49 of 82Next →

No leaderboard results yet.