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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/stat4reg/hdim.ui

공식 코드

Valid causal inference with unobserved confounding in high-dimensional settings

https://github.com/steffengra/resourceallocation

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Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care

https://github.com/stellali7/ssl_psr

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A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors

https://github.com/studyzx/gesturegpt_iss

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GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model Agents

https://github.com/sunyue93/sunyue93.github.io

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System Identification via Nuclear Norm Regularization

https://github.com/susan571/lensllm

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LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection

https://github.com/susperius/icisc_rnnfuzz

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Recurrent Neural Networks for Fuzz Testing Web Browsers

https://github.com/suvodipdey/fga

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Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level Performances

https://github.com/t-harden/llm4automl

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Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

https://github.com/takuyaisomura/reverse_engineering

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Triple equivalence for the emergence of biological intelligence

https://github.com/taldatech/deep-latent-particles-pytorch

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Unsupervised Image Representation Learning with Deep Latent Particles

https://github.com/tammok/CVST

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Fast Cross-Validation via Sequential Testing

https://github.com/tamu-aml/tempgp-paper

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The temporal overfitting problem with applications in wind power curve modeling

https://github.com/tanmingkui/multiwaybp

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The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs

https://github.com/tanyang1231/OTCE_Transferability_CVPR21

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OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations

https://github.com/teddy4445/substrat

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SubStrat: A Subset-Based Strategy for Faster AutoML

https://github.com/testing-cs/laf-model-selection

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LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing

https://github.com/tgensol/AMOS

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Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering

https://github.com/themaxiao/ditmos

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DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers

https://github.com/thomaswong2022/numerai-benchmark

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Online learning techniques for prediction of temporal tabular datasets with regime changes

https://github.com/thuan2412/a-principled-approach-for-model-validation-for-domain-generalization

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A principled approach to model validation in domain generalization

https://github.com/thuml/LogME

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LogME: Practical Assessment of Pre-trained Models for Transfer Learning

https://github.com/tian-bai/optcs

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Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization

https://github.com/tillahoffmann/summaries

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Unifying Summary Statistic Selection for Approximate Bayesian Computation

https://github.com/timmytonga/crois

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Improved Group Robustness via Classifier Retraining on Independent Splits

https://github.com/tjujianyu/rfc

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Rich Feature Construction for the Optimization-Generalization Dilemma

https://github.com/tmlabonte/last-layer-retraining

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Towards Last-layer Retraining for Group Robustness with Fewer Annotations

https://github.com/tobna/whattransformertofavor

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Which Transformer to Favor: A Comparative Analysis of Efficiency in Vision Transformers

https://github.com/toinesayan/node-classification-and-label-dependencies

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How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of Documents

https://github.com/tomogwen/fpdcluster

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Fuzzy c-Means Clustering for Persistence Diagrams

https://github.com/tomviering/ill-behaved-learning-curves

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The Shape of Learning Curves: a Review

https://github.com/tongjifinlab/fintsbenchmark

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FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting

https://github.com/tpospisi/DeepCDE

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Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference

https://github.com/tpospisi/NNKCDE

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Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference

https://github.com/tpospisi/cdetools

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Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference

https://github.com/tpospisi/rfcde

공식 코드

Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference

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