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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 731–740 of 2050 papers

TitleStatusHype
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
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationCode0
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary—0
A Strong Baseline for Batch Imitation Learning—0
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
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