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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 141–150 of 2050 papers

TitleStatusHype
Quantifying the Capability Boundary of DeepSeek Models: An Application-Driven Performance Analysis—0
Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit ViewCode0
Realistic Evaluation of Deep Partial-Label Learning Algorithms—0
An Innovative Next Activity Prediction Approach Using Process Entropy and DAW-Transformer—0
Benchmarking the rationality of AI decision making using the transitivity axiom—0
Near-Field Localization with Physics-Compliant Electromagnetic Model: Algorithms and Model Mismatch Analysis—0
Capitalizing on a Crisis: A Computational Analysis of all Five Million British Firms During the Covid-19 Pandemic—0
The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data—0
Causal Covariate Shift Correction using Fisher information penalty—0
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol—0
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