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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 1401–1410 of 2050 papers

TitleStatusHype
Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders—0
Zero-Shot Forecasting Mortality Rates: A Global Study—0
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!—0
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection—0
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets—0
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems—0
Gmail Smart Compose: Real-Time Assisted Writing—0
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
The Mismeasure of Man and Models: Evaluating Allocational Harms in Large Language Models—0
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