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Generalization Bounds

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

https://github.com/kimiandj/slicing_mi_generalization

공식 코드

Slicing Mutual Information Generalization Bounds for Neural Networks

https://github.com/lchizat/2020-implicit-bias-wide-2NN

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Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss

https://github.com/livreq/imda

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Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation

https://github.com/livreq/meta-sgld

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Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis

https://github.com/locuslab/uniform-convergence-NeurIPS19

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Uniform convergence may be unable to explain generalization in deep learning

https://github.com/lopezpaz/causation_learning_theory

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Towards a Learning Theory of Cause-Effect Inference

https://github.com/lucius-lsr/adaptergnn

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AdapterGNN: Parameter-Efficient Fine-Tuning Improves Generalization in GNNs

https://github.com/machinelearningbcam/mrcs-for-covariate-shift-adaptation

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Double-Weighting for Covariate Shift Adaptation

https://github.com/marineLM/linear_predictor_missing

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Linear predictor on linearly-generated data with missing values: non consistency and solutions

https://github.com/matthew-norton/Diametrical_Learning

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Diametrical Risk Minimization: Theory and Computations

https://github.com/mbarsbey/sgd_comp_gen

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Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks

https://github.com/mkhodak/FMRL

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Provable Guarantees for Gradient-Based Meta-Learning

https://github.com/nerdsresearch/robust-fine-tuning

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Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees

https://github.com/neu-statsml-research/generalization-in-graph-neural-networks

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Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion

https://github.com/neu-statsml-research/robust-fine-tuning

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Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees

https://github.com/nicolas-guerra/learning-where-to-learn

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Learning Where to Learn: Training Distribution Selection for Provable OOD Performance

https://github.com/nitarshan/robust-generalization-measures

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In Search of Robust Measures of Generalization

https://github.com/nvlabs/perada

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PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees

https://github.com/omarfoq/knn-per

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Personalized Federated Learning through Local Memorization

https://github.com/optimization-ai/drrho-clip

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Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws

https://github.com/paulviallard/aistats24-complexity-measures

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Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures

https://github.com/paulviallard/ecml21-pb-cbound

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Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound

https://github.com/paulviallard/mlj-disintegrated-pb

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A General Framework for the Practical Disintegration of PAC-Bayesian Bounds

https://github.com/paulviallard/neurips21-pb-robustness

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A PAC-Bayes Analysis of Adversarial Robustness

https://github.com/petrmokrov/energy-guided-entropic-ot

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Energy-guided Entropic Neural Optimal Transport

https://github.com/pingzaiwang/Analysis4TNN

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Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks

https://github.com/piotrkrasnowski/mdl_and_generalization_guarantees_for_representation_learning

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Minimum Description Length and Generalization Guarantees for Representation Learning

https://github.com/pku-ml/adainf

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Do Generated Data Always Help Contrastive Learning?

https://github.com/pmdidro/PMDI_DRO

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Robust Generalization despite Distribution Shift via Minimum Discriminating Information

https://github.com/pyijiezhang/cpe-underfit

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On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization Guarantees

https://github.com/rabeehk/compacter

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Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

https://github.com/rajatsen91/CCIT

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Model-Powered Conditional Independence Test

https://github.com/romainchor/datascience

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Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning

https://github.com/ron-amit/pac_bayes_reg

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Integral Probability Metrics PAC-Bayes Bounds

https://github.com/rpaolino/genvsexp

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Graph Representational Learning: When Does More Expressivity Hurt Generalization?

https://github.com/rubenohana/pac-bayesian_sliced-wasserstein

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Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances

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