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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 471–480 of 2050 papers

TitleStatusHype
Green Runner: A tool for efficient deep learning component selection—0
Fast Partition-Based Cross-Validation With Centering and Scaling for X^TX and X^TY—0
Towards Improved Variational Inference for Deep Bayesian Models—0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
INTERS: Unlocking the Power of Large Language Models in Search with Instruction TuningCode3
Valid causal inference with unobserved confounding in high-dimensional settingsCode0
An Axiomatic Approach to Model-Agnostic Concept Explanations—0
Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities—0
Experiment Planning with Function Approximation—0
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