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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/YerevaNN/DIIN-in-Keras

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Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report

https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark

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Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

https://github.com/a-nakai-k/mogp-for-graph-structured-data

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Multi-Output Gaussian Processes for Graph-Structured Data

https://github.com/aaltopml/bosmos

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Online simulator-based experimental design for cognitive model selection

https://github.com/abess-team/abess-A-Fast-Best-Subset-Selection-Library-in-Python-and-R

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abess: A Fast Best Subset Selection Library in Python and R

https://github.com/acerbilab/pyvbmc

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PyVBMC: Efficient Bayesian inference in Python

https://github.com/adrida/deltaxplainer

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Dynamic Interpretability for Model Comparison via Decision Rules

https://github.com/aeon-toolkit/aeon

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aeon: a Python toolkit for learning from time series

https://github.com/agiresearch/desocial

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DeSocial: Blockchain-based Decentralized Social Networks

https://github.com/ai-4-atmosphere-remote-sensing/mt-hccar

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MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property Retrieval

https://github.com/ai-in-complex-systems-lab/llm-honeypot

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LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems

https://github.com/ai-secure/secretgen

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SecretGen: Privacy Recovery on Pre-Trained Models via Distribution Discrimination

https://github.com/aiandglobaldevelopmentlab/eo-poverty-review

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A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty

https://github.com/aiaudit-org/raw2logit

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Data Models for Dataset Drift Controls in Machine Learning With Optical Images

https://github.com/aicenter/GenerativeAD.jl

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Comparison of Anomaly Detectors: Context Matters

https://github.com/aidaafshar/learning-rate-free-reinforcement-learning

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Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives

https://github.com/akshaykr/oracle_cb

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Model selection for contextual bandits

https://github.com/albertometelli/stochastic-rising-bandits

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Stochastic Rising Bandits

https://github.com/albicaron/estite

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Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review

https://github.com/alejandrodelaconcha/vsgs

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Offline detection of change-points in the mean for stationary graph signals

https://github.com/alessiospuriomancini/cosmopower

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The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison

https://github.com/alexandresegers/sgdGMF_Paper

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Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data

https://github.com/alexcapstick/llm-elicited-priors

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AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling

https://github.com/aleximmer/ntk-marglik

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Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

https://github.com/alexjungaalto/ResearchPublic

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On the Sample Complexity of Graphical Model Selection for Non-Stationary Processes

https://github.com/alexklwong/mondi-python

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Monitored Distillation for Positive Congruent Depth Completion

https://github.com/aliaksah/depmixS4pp

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An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models

https://github.com/aliciacurth/cateselection

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In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation

https://github.com/allerbo/non_constant_kgd

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Changing the Kernel During Training Leads to Double Descent in Kernel Regression

https://github.com/allerbo/prada_net

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Flexible, Non-parametric Modeling Using Regularized Neural Networks

https://github.com/allerbo/training_without_y

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Supervised Models Can Generalize Also When Trained on Random Label

https://github.com/amazon-science/causal-self-compatibility

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Self-Compatibility: Evaluating Causal Discovery without Ground Truth

https://github.com/amballaavinash/modelselection

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Automated Model Selection for Tabular Data

https://github.com/ameerd/gaussians

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Decomposing Gaussians with Unknown Covariance

https://github.com/anandhperumal/ANA-at-SemEval-2020-Task-4-UNION

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ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)

https://github.com/anazaret/xges

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Extremely Greedy Equivalence Search

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