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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://bitbucket.org/mvdschaar/mlforhealthlabpub

공식 코드

Learning Overlapping Representations for the Estimation of Individualized Treatment Effects

https://github.com/Dibyakanti/Towards-Size-Independent-Generalization-Bounds-for-Deep-Operator-Nets

공식 코드

Towards Size-Independent Generalization Bounds for Deep Operator Nets

https://github.com/GiangTTran/SparseRandomFeatures

공식 코드

Generalization Bounds for Sparse Random Feature Expansions

https://github.com/HazyResearch/smallfry

공식 코드

On the Downstream Performance of Compressed Word Embeddings

https://github.com/JiancongXiao/Stability-of-Adversarial-Training

공식 코드

Stability Analysis and Generalization Bounds of Adversarial Training

https://github.com/LearnTikhonov/Code

공식 코드

Learning the optimal Tikhonov regularizer for inverse problems

https://github.com/MachineLearningBCAM/Minimax-risk-classifiers-NeurIPS-2020

공식 코드

Minimax Classification with 0-1 Loss and Performance Guarantees

https://github.com/MartinBertran/InstanceAgnosticPolicyEnsembles

공식 코드

Instance based Generalization in Reinforcement Learning

https://github.com/WQgcx/ATPF

공식 코드

Adversarial Transform Particle Filters

https://github.com/ZiqiaoWangGeothe/Conditional-f-Information-Bound

공식 코드

Generalization Bounds via Conditional $f$-Information

https://github.com/ZiqiaoWangGeothe/ld-single-CMI

공식 코드

Tighter Information-Theoretic Generalization Bounds from Supersamples

https://github.com/acmi-lab/ratt_generalization_bound

공식 코드

RATT: Leveraging Unlabeled Data to Guarantee Generalization

https://github.com/activatedgeek/tight-pac-bayes

공식 코드

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization

https://github.com/agonon/pathnorm_toolkit

공식 코드

A path-norm toolkit for modern networks: consequences, promises and challenges

https://github.com/albietz/ckn_kernel

공식 코드

Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations

https://github.com/alexnowakvila/maxminloss

공식 코드

Consistent Structured Prediction with Max-Min Margin Markov Networks

https://github.com/alycia-noel/udj-fl

공식 코드

Achieving Distributive Justice in Federated Learning via Uncertainty Quantification

https://github.com/anastasiskratsios/risk_bound_ablation

공식 코드

Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces

https://github.com/anilkagak2/SelectiveClassification_One_Sided_Prediction

공식 코드

Selective Classification via One-Sided Prediction

https://github.com/avakanski/Evaluation-of-Complexity-Measures-for-Deep-Learning-Generalization-in-Medical-Image-Analysis

공식 코드

Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis

https://github.com/bangann/understanding-invariance

공식 코드

Understanding the Generalization Benefit of Model Invariance from a Data Perspective

https://github.com/bdi-lab/reed

공식 코드

PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning

https://github.com/bellet/HDSL

공식 코드

Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds

https://github.com/benjidupuis/data_dependent_dimensions

공식 코드

Generalization Bounds with Data-dependent Fractal Dimensions

https://github.com/benjidupuis/heavy_tails_generalization

공식 코드

Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation

https://github.com/boschresearch/PAC_GP

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Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds

https://github.com/brantondemoss/GrokkingComplexity

공식 코드

The Complexity Dynamics of Grokking

https://github.com/braqiiit/qrealboost

공식 코드

Quantum Boosting using Domain-Partitioning Hypotheses

https://github.com/cdslabamotong/ltinflearning

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Learnability of Competitive Threshold Models

https://github.com/charliebtan/fractal_dimensions

공식 코드

On the Limitations of Fractal Dimension as a Measure of Generalization

https://github.com/chen123CtrlS/LightweightAtt

공식 코드

Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization

https://github.com/chen123ctrls/efficientft

공식 코드

Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization

https://github.com/chen123ctrls/enhancingicl_svdpruning

공식 코드

Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective

https://github.com/chen123ctrls/t-cotmechanism

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Unveiling the Mechanisms of Explicit CoT Training: How CoT Enhances Reasoning Generalization

https://github.com/chingyaoc/DCL

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Debiased Contrastive Learning

https://github.com/chingyaoc/kV-Margin

공식 코드

Measuring Generalization with Optimal Transport

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