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Model Selection

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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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/robustml-lab/model-selector

공식 코드

All models are wrong, some are useful: Model Selection with Limited Labels

https://github.com/rodemann/reliable-pls

공식 코드

In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning

https://github.com/roserhp/atr_tn93

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A novel algebraic approach to time-reversible evolutionary models

https://github.com/roysaptaumich/dp-bss

공식 코드

On the Computational Complexity of Private High-dimensional Model Selection

https://github.com/rsheth80/pmf-automl

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Probabilistic Matrix Factorization for Automated Machine Learning

https://github.com/rubinxin/TSE

공식 코드

Speedy Performance Estimation for Neural Architecture Search

https://github.com/rudazhang/gpsr

공식 코드

Gaussian Process Subspace Regression for Model Reduction

https://github.com/runame/laplace-redux

공식 코드

Laplace Redux -- Effortless Bayesian Deep Learning

https://github.com/salesforce/ensemble-of-averages

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Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

https://github.com/sanaelotfi/bayesian_model_comparison

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Bayesian Model Selection, the Marginal Likelihood, and Generalization

https://github.com/sato9hara/defragTrees

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Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach

https://github.com/satyajitneogiju/FLDCRF-for-sequence-labeling

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Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence Modeling

https://github.com/scikit-adaptation/skada-bench

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SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities

https://github.com/scikit-learn-contrib/metric-learn

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metric-learn: Metric Learning Algorithms in Python

https://github.com/sdfk72auga1kwsi/aalf

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AALF: Almost Always Linear Forecasting

https://github.com/seanie12/safe-route

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SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models

https://github.com/seedatnabeel/data-iq

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Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data

https://github.com/seu-hk/partialclip

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Tuning the Right Foundation Models is What you Need for Partial Label Learning

https://github.com/shaibagon/large_scale_cc

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Large Scale Correlation Clustering Optimization

https://github.com/shakeebmurtaza/wsol_model_selection

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A Realistic Protocol for Evaluation of Weakly Supervised Object Localization

https://github.com/shakty/optimal-design

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Optimal design of experiments to identify latent behavioral types

https://github.com/shams-mehdi/aib9_openmm

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A survey of probabilistic generative frameworks for molecular simulations

https://github.com/sharif-ml-lab/evals

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Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group Annotation

https://github.com/shoaibbinmasud/soft-rank-energy-and-applications

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Multivariate rank via entropic optimal transport: sample efficiency and generative modeling

https://github.com/shubha07m/Energy-optimized-semantic-loss

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Transformers for Green Semantic Communication: Less Energy, More Semantics

https://github.com/shubha07m/MetaGreen-Meta-Learning-Inspired-Transformer-Selection

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MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic Communication

https://github.com/simon-tan/laplace

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LaPLACE: Probabilistic Local Model-Agnostic Causal Explanations

https://github.com/sltzgs/xai4windpowercurves

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XAI for transparent wind turbine power curve models

https://github.com/smaityumich/exponential-tilting

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Understanding new tasks through the lens of training data via exponential tilting

https://github.com/smkia/interpretability

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Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification

https://github.com/solislemuslab/soil-microbiome-nn

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Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome Data

https://github.com/som-shahlab/ITE-model-selection

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A comparison of methods for model selection when estimating individual treatment effects

https://github.com/soply/mpgraph

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Adaptive multi-penalty regularization based on a generalized Lasso path

https://github.com/spm/spm

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BSD: a Bayesian framework for parametric models of neural spectra

https://github.com/sqrhussain/structure-in-gnn

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On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks

https://github.com/stanford-mast/INFaaS

공식 코드

INFaaS: A Model-less and Managed Inference Serving System

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