https://github.com/minehly/awesome-paper-for-graph-learning-theory
Generalization bounds for graph convolutional neural networks via Rademacher complexity
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Generalization Bounds
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https://github.com/nanzhaogang/contrib/tree/master/application/learning-an-explicit-hyperparameter-prediction-function-conditioned-on-tasks
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/oddrose/cfrnet
Estimating individual treatment effect: generalization bounds and algorithms
https://github.com/pmontman/paper-global-forec-princip
Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality
https://github.com/pwc-1/Paper-9/tree/main/3/stable-sam
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
https://github.com/pwc-1/Paper-9/tree/main/4/stable-sam
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
https://github.com/rynewu224/graphda
https://github.com/salesforce/ensemble-of-averages
https://github.com/shaoshitong/NICO
NICO++: Towards Better Benchmarking for Domain Generalization
https://github.com/sschrod/bites
Estimating individual treatment effect: generalization bounds and algorithms
https://github.com/stellatogrp/l2ws
https://github.com/stephanlorenzen/majorityvotebounds
https://github.com/thuml/Transfer-Learning-Library
https://github.com/xinshuli2022/cite
Estimating individual treatment effect: generalization bounds and algorithms
https://github.com/yao-lab/margin
Rethinking Breiman's Dilemma in Neural Networks: Phase Transitions of Margin Dynamics
https://github.com/yilun-wu/MetaL-Benchmark
https://github.com/yingcong-li/dissecting-cot
Transformers as Algorithms: Generalization and Stability in In-context Learning
https://github.com/ys-zong/medfair
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.