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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 926–950 of 2050 papers

TitleStatusHype
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
Monitored Distillation for Positive Congruent Depth CompletionCode1
Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice—0
Black-box Selective Inference via Bootstrapping—0
DeepDPM: Deep Clustering With an Unknown Number of ClustersCode2
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System—0
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Rich Feature Construction for the Optimization-Generalization DilemmaCode1
Predictor Selection for Synthetic Controls—0
On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging—0
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work—0
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think AgainCode1
Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD—0
Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management—0
Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications—0
Geometric and Topological Inference for Deep Representations of Complex Networks—0
Bayesian Spatial Predictive Synthesis—0
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification TasksCode1
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows—0
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