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 1001–1025 of 2050 papers

TitleStatusHype
Fast Instrument Learning with Faster RatesCode0
Fusion Subspace Clustering for Incomplete Data—0
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Differentially Private Generalized Linear Models Revisited—0
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained ModelsCode0
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net—0
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual TransferCode0
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts—0
Differentially Private Learning with Margin Guarantees—0
Improved Group Robustness via Classifier Retraining on Independent SplitsCode0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
Trinary Tools for Continuously Valued Binary Classifiers—0
Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and PseudoposteriorsCode0
LaF: Labeling-Free Model Selection for Automated Deep Neural Network ReusingCode0
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level PerformancesCode0
Statistical Model Criticism of Variational Auto-Encoders—0
Consensual Aggregation on Random Projected High-dimensional Features for Regression—0
Fundamental limits to learning closed-form mathematical models from data—0
Pareto-optimal clustering with the primal deterministic information bottleneckCode0
System Identification via Nuclear Norm RegularizationCode0
Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice—0
Black-box Selective Inference via Bootstrapping—0
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System—0
Show:102550
← PrevPage 41 of 82Next →

No leaderboard results yet.