Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization | Paper Citation Record

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This paper cites Invariant Risk Minimization Games.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Invariant Risk Minimization Games

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization

Reference 2

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This paper cites Empirical or Invariant Risk Minimization? A Sample Complexity Perspective.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

Reference 3

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This paper cites AlBadawy, Ashirbani Saha, and Maciej A.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization AlBadawy, Ashirbani Saha, and Maciej A

Reference 4

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This paper cites Invariant Risk Minimization.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Invariant Risk Minimization

Reference 5

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Recognition in Terra Incognita

Reference 6

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Bekas, E

Reference 7

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization A theory of learning from different domains

Reference 8

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This paper cites Generalizing from Several Related Classification Tasks to a New Unlabeled Sample.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Generalizing from Several Related Classification Tasks to a New Unlabeled Sample

Reference 9

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This paper cites Chapter 19 - Multiobjective Optimization and Advanced Topics.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Chapter 19 - Multiobjective Optimization and Advanced Topics

Reference 10

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Functional Map of the World

Reference 11

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This paper cites Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime

Reference 12

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Rockmore

Reference 14

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This paper cites Domain-Adversarial Training of Neural Networks.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Domain-Adversarial Training of Neural Networks

Reference 15

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This paper cites Domain Generalization for Object Recognition with Multi-task Autoencoders.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Domain Generalization for Object Recognition with Multi-task Autoencoders

Reference 16

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This paper cites Are Vision Transformers Robust to Spurious Correlations?.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Are Vision Transformers Robust to Spurious Correlations?

Reference 17

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This paper cites In Search of Lost Domain Generalization.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization In Search of Lost Domain Generalization

Reference 18

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This paper cites Annotation Artifacts in Natural Language Inference Data.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Annotation Artifacts in Natural Language Inference Data

Reference 19

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Unresolved cited work

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This paper cites Invariant Causal Prediction for Nonlinear Models.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Invariant Causal Prediction for Nonlinear Models

Reference 21

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Understanding Hessian Alignment for Domain Generalization

Reference 22

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Reference 23

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Does Distributionally Robust Supervised Learning Give Robust Classifiers?

Reference 24

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This paper cites Causal-based Time Series Domain Generalization for Vehicle Intention Prediction.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Causal-based Time Series Domain Generalization for Vehicle Intention Prediction

Reference 25

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This paper cites Winning Prize Comes from Losing Tickets : Improve Invariant Learning by Exploring Variant Parameters for Out -of- Distribution Generalization.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Winning Prize Comes from Losing Tickets : Improve Invariant Learning by Exploring Variant Parameters for Out -of- Distribution Generalization

Reference 26

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This paper cites Does Invariant Risk Minimization Capture Invariance?.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Does Invariant Risk Minimization Capture Invariance?

Reference 27

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Out-of- Distribution Generalization with Maximal Invariant Predictor

Reference 28

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization When is invariance useful in an Out-of-Distribution Generalization problem ?

Reference 29

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Out-of-Distribution Generalization via Risk Extrapolation (REx)

Reference 30

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization MNIST handwritten digit database, 2010

Reference 31

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Deeper, Broader and Artier Domain Generalization

Reference 32

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Unresolved cited work

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Deep Learning Face Attributes in the Wild

Reference 34

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Learning Transferable Features with Deep Adaptation Networks

Reference 35

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Domain Generalization via Invariant Feature Representation

Reference 36

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Learning explanations that are hard to vary

Reference 37

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Moment Matching for Multi-Source Domain Adaptation

Reference 38

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Causal inference using invariant prediction: identification and confidence intervals

Reference 39

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Fishr: Invariant Gradient Variances for Out -of- Distribution Generalization

Reference 40

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization The Risks of Invariant Risk Minimization

Reference 41

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 42

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization BREEDS: Benchmarks for Subpopulation Shift

Reference 43

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Do Image Classifiers Generalize Across Time?

Reference 44

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Gradient Matching for Domain Generalization

Reference 45

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

Reference 46

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Self- Distilled Vision Transformer for Domain Generalization

Reference 47

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Deep CORAL: Correlation Alignment for Deep Domain Adaptation

Reference 48

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Reference 49

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization

Reference 50

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Adversarial Discriminative Domain Adaptation

Reference 51

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization An overview of statistical learning theory

Reference 52

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Detect and correct bias in multi-site neuroimaging datasets

Reference 53

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This paper cites The Caltech - UCSD Birds -200-2011 dataset.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization The Caltech - UCSD Birds -200-2011 dataset

Reference 54

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This paper cites Provable Domain Generalization via Invariant-Feature Subspace Recovery.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Provable Domain Generalization via Invariant-Feature Subspace Recovery

Reference 55

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Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization Algorithms

Reference 56

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This paper cites PyTorch Image Models , 2019.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization PyTorch Image Models , 2019

Reference 57

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Observation 30e9758c-c241-4c7a-b0f7-afa7cd2f6ddd · outbound

This paper cites A Broad - Coverage Challenge Corpus for Sentence Understanding through Inference.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization A Broad - Coverage Challenge Corpus for Sentence Understanding through Inference

Reference 58

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Observation 557c47c8-a361-4a56-b66d-d33c22000074 · outbound

This paper cites Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:36.812196Z

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Observation ca5abfac-acef-476c-93ab-69a872a6e420 · outbound

This paper cites Quantifying and Improving Transferability in Domain Generalization.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Quantifying and Improving Transferability in Domain Generalization

Reference 60

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Observation b3cd1302-5dbb-410b-bef0-7623b34063b8 · outbound

This paper cites A Causal Framework to Unify Common Domain Generalization Approaches.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization A Causal Framework to Unify Common Domain Generalization Approaches

Reference 61

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Observation a34bc70a-e2af-463f-bf54-6f2b44509b47 · outbound

This paper cites On Learning Invariant Representations for Domain Adaptation.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization On Learning Invariant Representations for Domain Adaptation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:48:42.629301Z

Source-reported events for the cited work

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Observation dffae9d1-aa3d-4f28-817f-d69768a9a77d · outbound

This paper cites Prompt Vision Transformer for Domain Generalization.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Prompt Vision Transformer for Domain Generalization

Reference 63

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Observation cacd1025-1a51-4ec4-ade6-e739930f2938 · outbound

This paper cites Places: A 10 Million Image Database for Scene Recognition.

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Places: A 10 Million Image Database for Scene Recognition

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T05:48:37.451436Z

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