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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 851–860 of 2050 papers

TitleStatusHype
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Linear Bandits with Memory: from Rotting to Rising—0
Best Arm Identification for Stochastic Rising BanditsCode0
When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairnessCode0
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPCode0
What are the mechanisms underlying metacognitive learning?—0
Fast Linear Model Trees by PILOT—0
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection—0
On the Limitation and Experience Replay for GNNs in Continual Learning—0
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