Learning Causality for Modern Machine Learning | Paper Citation Record

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This paper cites Linear unit-tests for invariance discovery.

Learning Causality for Modern Machine Learning Linear unit-tests for invariance discovery

Reference 4

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This paper cites Layer Normalization.

Learning Causality for Modern Machine Learning Layer Normalization

Reference 5

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Observation 9eeb4047-847b-4fcb-9428-b1d2ab5a22b3 · outbound

This paper cites Accounting for Unobserved Confounding in Domain Generalization.

Learning Causality for Modern Machine Learning Accounting for Unobserved Confounding in Domain Generalization

Reference 8

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This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Learning Causality for Modern Machine Learning Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 10

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This paper cites Estimating generalization under distribution shifts via domain-invariant representations.

Learning Causality for Modern Machine Learning Estimating generalization under distribution shifts via domain-invariant representations

Reference 14

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This paper cites Robust Learning with Progressive Data Expansion Against Spurious Correlation.

Learning Causality for Modern Machine Learning Robust Learning with Progressive Data Expansion Against Spurious Correlation

Reference 17

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This paper cites Distributionally Robust Losses for Latent Covariate Mixtures.

Learning Causality for Modern Machine Learning Distributionally Robust Losses for Latent Covariate Mixtures

Reference 19

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Observation 1dcb9df6-1ee0-416c-a7b3-4014997c411e · outbound

This paper cites Benchmarking Graph Neural Networks.

Learning Causality for Modern Machine Learning Benchmarking Graph Neural Networks

Reference 20

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This paper cites Toy Models of Superposition.

Learning Causality for Modern Machine Learning Toy Models of Superposition

Reference 21

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Observation b0153e60-1f3c-4c60-9529-4deca9bf8eb8 · outbound

This paper cites Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types.

Learning Causality for Modern Machine Learning Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types

Reference 22

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Observation 11dc3e8c-9d7d-4c97-a351-f4b42945b4fd · outbound

This paper cites AllenNLP: A Deep Semantic Natural Language Processing Platform.

Learning Causality for Modern Machine Learning AllenNLP: A Deep Semantic Natural Language Processing Platform

Reference 23

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Observation 62d00e3a-652f-43d0-831c-584f9d537328 · outbound

This paper cites Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization.

Learning Causality for Modern Machine Learning Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

Reference 24

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Observation 213a0488-c6ff-44bc-9e8e-e143307f917f · outbound

This paper cites Counterfactual Learning on Graphs: A Survey.

Learning Causality for Modern Machine Learning Counterfactual Learning on Graphs: A Survey

Reference 25

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Observation 3149f253-0478-41a0-bf42-a16697179114 · outbound

This paper cites A Survey of Label-noise Representation Learning: Past, Present and Future.

Learning Causality for Modern Machine Learning A Survey of Label-noise Representation Learning: Past, Present and Future

Reference 26

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Observation b7ba944d-4da9-4d76-85d6-8be89b4c90fc · outbound

This paper cites Does distributionally robust supervised learning give robust classifiers? InInternational Conference on Machine Learning, pp.

Learning Causality for Modern Machine Learning Does distributionally robust supervised learning give robust classifiers? InInternational Conference on Machine Learning, pp

Reference 27

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Observation 8cdcd182-f1fe-48ef-a022-94c93d52ef6d · outbound

This paper cites Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons.

Learning Causality for Modern Machine Learning Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons

Reference 28

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This paper cites and Wallace, B.

Learning Causality for Modern Machine Learning and Wallace, B

Reference 29

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Observation 8b0b2a5a-7e87-493f-8d1f-fce515f76282 · outbound

This paper cites DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations.

Learning Causality for Modern Machine Learning DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations

Reference 30

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Observation 39e1288a-bce6-4d6f-9cdd-1307c3caea8e · outbound

This paper cites Empowering Graph Representation Learning with Test-Time Graph Transformation.

Learning Causality for Modern Machine Learning Empowering Graph Representation Learning with Test-Time Graph Transformation

Reference 31

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Observation e1f2cea6-404c-4e3c-ba6e-6cabddc3f100 · outbound

This paper cites No Free Lunch for Approximate MCMC.

Learning Causality for Modern Machine Learning No Free Lunch for Approximate MCMC

Reference 32

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Observation a19420aa-2313-4247-888a-4ba57f68cce1 · outbound

This paper cites Variational Graph Auto-Encoders.

Learning Causality for Modern Machine Learning Variational Graph Auto-Encoders

Reference 33

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This paper cites Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations.

Learning Causality for Modern Machine Learning Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations

Reference 34

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This paper cites Learning ground states of quantum Hamiltonians with graph networks.

Learning Causality for Modern Machine Learning Learning ground states of quantum Hamiltonians with graph networks

Reference 35

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This paper cites Robust Optimization as Data Augmentation for Large-scale Graphs.

Learning Causality for Modern Machine Learning Robust Optimization as Data Augmentation for Large-scale Graphs

Reference 36

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This paper cites When is invariance useful in an Out-of-Distribution Generalization problem ?.

Learning Causality for Modern Machine Learning When is invariance useful in an Out-of-Distribution Generalization problem ?

Reference 37

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This paper cites Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization.

Learning Causality for Modern Machine Learning Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization

Reference 39

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Observation 6d2f5151-84d7-480d-9bb1-bac74c918a94 · outbound

This paper cites Spurious Feature Diversification Improves Out-of-distribution Generalization.

Learning Causality for Modern Machine Learning Spurious Feature Diversification Improves Out-of-distribution Generalization

Reference 40

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Observation 2ae3c64f-6276-446e-a7f5-c3c30184b33e · outbound

This paper cites Graph Rationalization with Environment-based Augmentations.

Learning Causality for Modern Machine Learning Graph Rationalization with Environment-based Augmentations

Reference 41

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Observation 27d89c61-b303-47b3-af72-a371854ca3bb · outbound

This paper cites Calibrating and Improving Graph Contrastive Learning.

Learning Causality for Modern Machine Learning Calibrating and Improving Graph Contrastive Learning

Reference 42

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Observation cec823bc-10ca-4c48-b2e1-7c9692c36f10 · outbound

This paper cites Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks.

Learning Causality for Modern Machine Learning Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks

Reference 43

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Observation 62b7524c-90bf-4b5f-bc10-dda8b18a872c · outbound

This paper cites Fisher discriminant analysis with kernels.

Learning Causality for Modern Machine Learning Fisher discriminant analysis with kernels

Reference 44

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This paper cites Towards Stable Backdoor Purification through Feature Shift Tuning.

Learning Causality for Modern Machine Learning Towards Stable Backdoor Purification through Feature Shift Tuning

Reference 45

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Observation 228bb813-c4a9-48b1-9ab6-23a6e18f7ed1 · outbound

This paper cites TUDataset: A collection of benchmark datasets for learning with graphs.

Learning Causality for Modern Machine Learning TUDataset: A collection of benchmark datasets for learning with graphs

Reference 46

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This paper cites Weisfeiler and Leman go Machine Learning: The Story so far.

Learning Causality for Modern Machine Learning Weisfeiler and Leman go Machine Learning: The Story so far

Reference 47

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This paper cites Empirical Study on Optimizer Selection for Out-of-Distribution Generalization.

Learning Causality for Modern Machine Learning Empirical Study on Optimizer Selection for Out-of-Distribution Generalization

Reference 48

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This paper cites M., Nicolicioiu, A.

Learning Causality for Modern Machine Learning M., Nicolicioiu, A

Reference 49

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This paper cites Discovering environments with XRM.

Learning Causality for Modern Machine Learning Discovering environments with XRM

Reference 50

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Observation 1fb1eec0-cf34-4e92-906b-84e8cae14b6a · outbound

This paper cites Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization.

Learning Causality for Modern Machine Learning Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization

Reference 51

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This paper cites Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization.

Learning Causality for Modern Machine Learning Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization

Reference 52

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Observation f6fe9db2-ae60-4786-8ce5-ce8aaf132aed · outbound

This paper cites Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization.

Learning Causality for Modern Machine Learning Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization

Reference 53

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This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Learning Causality for Modern Machine Learning DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 54

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This paper cites Causality for Machine Learning.

Learning Causality for Modern Machine Learning Causality for Machine Learning

Reference 55

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This paper cites Adversarial Attack and Defense on Graph Data: A Survey.

Learning Causality for Modern Machine Learning Adversarial Attack and Defense on Graph Data: A Survey

Reference 57

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Learning Causality for Modern Machine Learning Unresolved cited work

Reference 58

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This paper cites Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization.

Learning Causality for Modern Machine Learning Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization

Reference 61

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This paper cites ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets.

Learning Causality for Modern Machine Learning ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets

Reference 62

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This paper cites Representation Learning with Contrastive Predictive Coding.

Learning Causality for Modern Machine Learning Representation Learning with Contrastive Predictive Coding

Reference 64

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This paper cites Attack Graph Convolutional Networks by Adding Fake Nodes.

Learning Causality for Modern Machine Learning Attack Graph Convolutional Networks by Adding Fake Nodes

Reference 65

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This paper cites Towards out-of- distribution generalizable predictions of chemical kinetics properties.

Learning Causality for Modern Machine Learning Towards out-of- distribution generalizable predictions of chemical kinetics properties

Reference 66

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This paper cites Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack.

Learning Causality for Modern Machine Learning Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack

Reference 67

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This paper cites Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization.

Learning Causality for Modern Machine Learning Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization

Reference 68

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This paper cites Freeze then train: Towards provable representation learning under spurious correlations and feature noise.arXiv preprint arXiv:2210.11075,.

Learning Causality for Modern Machine Learning Freeze then train: Towards provable representation learning under spurious correlations and feature noise.arXiv preprint arXiv:2210.11075,

Reference 69

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Observation 8c0ab7a5-4099-48fb-856a-30f8fbf31d2c · outbound

This paper cites Explainability in Graph Neural Networks: A Taxonomic Survey.

Learning Causality for Modern Machine Learning Explainability in Graph Neural Networks: A Taxonomic Survey

Reference 70

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This paper cites Understanding Why Generalized Reweighting Does Not Improve Over ERM.

Learning Causality for Modern Machine Learning Understanding Why Generalized Reweighting Does Not Improve Over ERM

Reference 71

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This paper cites Learning useful representations for shifting tasks and distributions.

Learning Causality for Modern Machine Learning Learning useful representations for shifting tasks and distributions

Reference 72

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Observation 37770d28-d54e-47df-99eb-5b03268a7da9 · outbound

This paper cites Rich Feature Construction for the Optimization-Generalization Dilemma.

Learning Causality for Modern Machine Learning Rich Feature Construction for the Optimization-Generalization Dilemma

Reference 73

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Observation 4c47a369-f202-4ba9-91fa-0d2be9c56329 · outbound

This paper cites Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

Learning Causality for Modern Machine Learning Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Reference 74

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This paper cites Fundamental Limits and Tradeoffs in Invariant Representation Learning.

Learning Causality for Modern Machine Learning Fundamental Limits and Tradeoffs in Invariant Representation Learning

Reference 75

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This paper cites A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes.

Learning Causality for Modern Machine Learning A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes

Reference 76

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This paper cites Explaining and Adapting Graph Conditional Shift.

Learning Causality for Modern Machine Learning Explaining and Adapting Graph Conditional Shift

Reference 77

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This paper cites Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization.

Learning Causality for Modern Machine Learning Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization

Reference 78

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This paper cites Adversarial attacks on neural networks for graph data.

Learning Causality for Modern Machine Learning Adversarial attacks on neural networks for graph data

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:04:00.103857Z

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Observation 51bfceb4-8a55-481d-979b-657706a6f7b1 · outbound

This paper cites Nuanced metrics for measuring unintended bias with real data for text classification.

Learning Causality for Modern Machine Learning Nuanced metrics for measuring unintended bias with real data for text classification

Reference 1996

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:04:02.377403Z

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Observation d3f5f3ba-8fcf-44b4-928e-505ad80a7624 · outbound

This paper cites Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks.

Learning Causality for Modern Machine Learning Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks

Reference 1998

Resolution: verified exact; local_arxiv, observed 2026-08-07T01:03:58.618577Z

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Observation 46a4c99b-4e5c-4e64-9198-6c2db8269df0 · outbound

This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.

Learning Causality for Modern Machine Learning Instance Normalization: The Missing Ingredient for Fast Stylization

Reference 1999

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This paper cites Understanding and improving graph injection attack by promoting unnoticeability.

Learning Causality for Modern Machine Learning Understanding and improving graph injection attack by promoting unnoticeability

Reference 2005

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:04:02.203926Z

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Observation 611f9a34-4e45-4161-854c-dd9f3a4c409c · outbound

This paper cites Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance.

Learning Causality for Modern Machine Learning Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance

Reference 2006

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Observation 96c3e17f-5212-4d9a-9e5f-6be60f46e44d · outbound

This paper cites IDEA: Invariant Defense for Graph Adversarial Robustness.

Learning Causality for Modern Machine Learning IDEA: Invariant Defense for Graph Adversarial Robustness

Reference 2008

Resolution: verified exact; local_arxiv, observed 2026-08-07T01:03:55.632582Z

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Observation dab9c4ee-af95-431b-8f8a-53b35b239b94 · outbound

This paper cites E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.

Learning Causality for Modern Machine Learning E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:04:00.634711Z

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Observation 457e0b37-b11f-491f-832e-07aa266065b8 · outbound

This paper cites PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing.

Learning Causality for Modern Machine Learning PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing

Reference 2014

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Observation 87bab897-4c45-48f0-8201-1e7d8a8c2c7a · outbound

This paper cites Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features.

Learning Causality for Modern Machine Learning Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features

Reference 2015

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Observation 958fd22b-e0bc-483b-a354-f713b85cf71c · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Learning Causality for Modern Machine Learning Relational inductive biases, deep learning, and graph networks

Reference 2016

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Observation b0c73fea-4d4e-4224-a0ba-7902fca8efe4 · outbound

This paper cites Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning.

Learning Causality for Modern Machine Learning Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2017

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This paper cites The iWildCam 2020 Competition Dataset.

Learning Causality for Modern Machine Learning The iWildCam 2020 Competition Dataset

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T01:03:47.063625Z

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This paper cites A closer look at distribution shifts and out-of-distribution generalization on graphs.

Learning Causality for Modern Machine Learning A closer look at distribution shifts and out-of-distribution generalization on graphs

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:04:01.550984Z

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Observation a518fa1b-4ceb-4481-8ec6-74a962dc57b6 · outbound

This paper cites Learning a similarity metric discriminatively, with application to face verification.

Learning Causality for Modern Machine Learning Learning a similarity metric discriminatively, with application to face verification

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:04:01.962476Z

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Observation abfe5022-f24d-4d7d-a1e8-4a4e7b3721bb · outbound

This paper cites Invariant Risk Minimization.

Learning Causality for Modern Machine Learning Invariant Risk Minimization

Reference 2021

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Observation 48038931-74da-400c-ac6d-0d2aadf2f3f2 · outbound

This paper cites The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning.

Learning Causality for Modern Machine Learning The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning

Reference 2022

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This paper cites Discovering Symbolic Models from Deep Learning with Inductive Biases.

Learning Causality for Modern Machine Learning Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 2023

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