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Generalization Bounds

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코드

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

https://github.com/zhenhuan-yang/simple-pairwise

공식 코드

Simple Stochastic and Online Gradient DescentAlgorithms for Pairwise Learning

https://worksheets.codalab.org/worksheets/0x578f01269d644524b0d4ab2a7a2a6984

공식 코드

Maximum Weighted Loss Discrepancy

https://github.com/AliceDeLorenci/Nonvacuous-Generalization-Bounds-for-DNNs

Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

https://github.com/ChenLiu-1996/DiffusionSpectralEntropy

Deep Learning and the Information Bottleneck Principle

https://github.com/EigenPro/EigenPro

Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses

https://github.com/GiangTTran/SparseRandomFeatureExpansion

Generalization Bounds for Sparse Random Feature Expansions

https://github.com/KarhouTam/FL-bench

Personalized Federated Learning through Local Memorization

https://github.com/MachineLearningBCAM/MRCpy

Minimax Classification with 0-1 Loss and Performance Guarantees

https://github.com/MindCode-4/code-12/tree/main/learning-an-explicit-hyperparameter

Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks

https://github.com/MindCode-4/code-9/tree/main/stable-sam

Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound

https://github.com/MindSpore-scientific/code-14/tree/main/stable-sam

Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound

https://github.com/Zymrael/PAC-Adasampling

A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent

https://github.com/adapt-python/adapt

Bridging Theory and Algorithm for Domain Adaptation

https://github.com/alexstein0/neural-auctions

Optimal Auctions through Deep Learning: Advances in Differentiable Economics

https://github.com/amrutn/Information-in-Language

Deep Learning and the Information Bottleneck Principle

https://github.com/anonymousxzy/compacter

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

https://github.com/antoinedemathelin/wann

Bridging Theory and Algorithm for Domain Adaptation

https://github.com/b-akshay/aknn-classifier

An adaptive nearest neighbor rule for classification

https://github.com/bkiani/random_NN_simplicity

Random deep neural networks are biased towards simple functions

https://github.com/brando90/Generalization-Puzzles-in-Deep-Networks

A Surprising Linear Relationship Predicts Test Performance in Deep Networks

https://github.com/brando90/overparam_experiments

A Surprising Linear Relationship Predicts Test Performance in Deep Networks

https://github.com/cemanil/LNets

Sorting out Lipschitz function approximation

https://github.com/clinicalml/cfrnet

Estimating individual treatment effect: generalization bounds and algorithms

https://github.com/degregat/one-shot-approx-auctions

Optimal Auctions through Deep Learning: Advances in Differentiable Economics

https://github.com/facebookresearch/mtenv

Learning Robust State Abstractions for Hidden-Parameter Block MDPs

https://github.com/facebookresearch/mtrl

Learning Robust State Abstractions for Hidden-Parameter Block MDPs

https://github.com/fzw97/GraphDA

Bridging Theory and Algorithm for Domain Adaptation

https://github.com/gkdziugaite/pacbayes-opt

Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

https://github.com/irom-lab/dragen

Generalization Guarantees for Imitation Learning

https://github.com/jhaochenz/spectral_contrastive_learning

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

https://github.com/jhaochenz96/spectral_contrastive_learning

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

https://github.com/jjgrimm/FairnessReferenceSheet

An Algorithmic Framework for Fairness Elicitation

https://github.com/jonasrothfuss/pacoh_nn

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

https://github.com/kakaobrain/miro

SWAD: Domain Generalization by Seeking Flat Minima

https://github.com/leoandeol/cods

PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

https://github.com/liaoq/Generalization-Puzzles-in-Deep-Networks

A Surprising Linear Relationship Predicts Test Performance in Deep Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.