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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 201–225 of 2050 papers

TitleStatusHype
Testing Conditional Independence in Supervised Learning AlgorithmsCode1
Variational Bayesian Monte CarloCode1
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
A comparison of methods for model selection when estimating individual treatment effectsCode1
Population Based Training of Neural NetworksCode1
A network approach to topic modelsCode1
RBFOpt: an open-source library for black-box optimization with costly function evaluationsCode1
Deep Domain Confusion: Maximizing for Domain InvarianceCode1
How Many Topics? Stability Analysis for Topic ModelsCode1
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
Topic Modeling and Link-Prediction for Material Property Discovery—0
Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III—0
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at ScaleCode0
Leveraging Predictive Equivalence in Decision TreesCode0
The use of cross validation in the analysis of designed experimentsCode0
Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances—0
Evaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts—0
Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives—0
The Sample Complexity of Parameter-Free Stochastic Convex Optimization—0
Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity—0
A Statistical Framework for Model Selection in LSTM Networks—0
Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques—0
Nonlinear Causal Discovery for Grouped Data—0
Crowd-SFT: Crowdsourcing for LLM Alignment—0
Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems—0
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