https://github.com/BayesianTestsML/tutorial
공식 코드Statistical comparison of classifiers through Bayesian hierarchical modelling
Papers with Code 아카이브
Two-sample testing
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In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. The statistics used in two-sample tests can be used to solve many machine learning problems, such as domain adaptation, covariate shift and generative adversarial networks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Jong-Min-Moon/optimal-local-dp-two-sample
공식 코드Minimax Optimal Two-Sample Testing under Local Differential Privacy
https://github.com/Microsoft/PixelDefend
공식 코드PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
https://github.com/Mr8ND/ACORE-LFI
공식 코드Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
https://github.com/PierreBoyeau/decision-making-vaes
공식 코드https://github.com/PierreBoyeau/sbVAE
공식 코드https://github.com/alexisbellot/GCIT
공식 코드Conditional Independence Testing using Generative Adversarial Networks
https://github.com/andreacos/MultiBranch_CNNCopyMove_Disambiguation
공식 코드Copy Move Source-Target Disambiguation through Multi-Branch CNNs
https://github.com/antoninschrab/fl-mmdagg
공식 코드https://github.com/antoninschrab/mmdagg
공식 코드https://github.com/antoninschrab/mmdfuse-paper
공식 코드MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
https://github.com/asonabend/ESRL
공식 코드Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
https://github.com/chau999/credaltwosampletests
공식 코드https://github.com/collin-burns/interpretability-hypothesis-testing
공식 코드https://github.com/computationalmedia/supmmd
공식 코드SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy
https://github.com/corradomonti/learnable-opinion-dynamics
공식 코드https://github.com/dgl-prc/m_testing_adversatial_sample
공식 코드Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
https://github.com/diozaka/eitest
공식 코드https://github.com/elena-kalinina/Code_CMPT_paper
공식 코드A Test for Shared Patterns in Cross-modal Brain Activation Analysis
https://github.com/fengliu90/DK-for-TST
공식 코드https://github.com/fengliu90/MetaTesting
공식 코드Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
https://github.com/fjxmlzn/PacGAN
공식 코드PacGAN: The power of two samples in generative adversarial networks
https://github.com/gdebarghya/Network-TwoSampleTesting
공식 코드https://github.com/hbhzwj/SADIT
공식 코드Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models
https://github.com/hezhangsprinter/SROSR
공식 코드https://github.com/hoonose/privit
공식 코드https://github.com/i-gao/model-equality-testing
공식 코드https://github.com/ido90/Rewards-Deterioration-Detection
공식 코드Detecting Rewards Deterioration in Episodic Reinforcement Learning
https://github.com/ikjunchoi/rff-mmd
공식 코드Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features
https://github.com/inspire-group/unlearning-verification
공식 코드https://github.com/jingzbu/ROCHM
공식 코드Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models
https://github.com/jmkuebler/auto-tst
공식 코드https://github.com/jmkuebler/automl-tst-paper
공식 코드https://github.com/jmkuebler/wits-test
공식 코드https://github.com/krikamol/kcm-test
공식 코드Kernel Conditional Moment Test via Maximum Moment Restriction
https://github.com/maha-93/Clustering-Testing-Networks
공식 코드Graphon based Clustering and Testing of Networks: Algorithms and Theory
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.