Towards Understanding Extrapolation: a Causal Lens | Paper Citation Record

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This paper cites Measuring robustness to natural distribution shifts in image classification.

Towards Understanding Extrapolation: a Causal Lens Measuring robustness to natural distribution shifts in image classification

Reference 1

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Observation be49a158-0174-45c0-a92b-6917884f6308 · outbound

This paper cites Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pages 5389–5400.

Towards Understanding Extrapolation: a Causal Lens Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pages 5389–5400

Reference 2

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Observation 78262789-f475-44c8-8289-4dfd02f143a1 · outbound

This paper cites Wilds: A benchmark of in-the-wild distribution shifts.

Towards Understanding Extrapolation: a Causal Lens Wilds: A benchmark of in-the-wild distribution shifts

Reference 3

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This paper cites In Search of Lost Domain Generalization.

Towards Understanding Extrapolation: a Causal Lens In Search of Lost Domain Generalization

Reference 4

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This paper cites Improving predictive inference under covariate shift by weighting the log-likelihood function.

Towards Understanding Extrapolation: a Causal Lens Improving predictive inference under covariate shift by weighting the log-likelihood function

Reference 5

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This paper cites Correcting sample selection bias by unlabeled data.

Towards Understanding Extrapolation: a Causal Lens Correcting sample selection bias by unlabeled data

Reference 6

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This paper cites Direct importance estimation for covariate shift adaptation.

Towards Understanding Extrapolation: a Causal Lens Direct importance estimation for covariate shift adaptation

Reference 7

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This paper cites Domain adaptation under target and conditional shift.

Towards Understanding Extrapolation: a Causal Lens Domain adaptation under target and conditional shift

Reference 8

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This paper cites Low-dimensional density ratio estimation for covariate shift correction.

Towards Understanding Extrapolation: a Causal Lens Low-dimensional density ratio estimation for covariate shift correction

Reference 9

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This paper cites The risks of invariant risk minimiza- tion.

Towards Understanding Extrapolation: a Causal Lens The risks of invariant risk minimiza- tion

Reference 10

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This paper cites Generalizing to unseen domains via distribution matching.

Towards Understanding Extrapolation: a Causal Lens Generalizing to unseen domains via distribution matching

Reference 11

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This paper cites Domain generalization by marginal transfer learning.

Towards Understanding Extrapolation: a Causal Lens Domain generalization by marginal transfer learning

Reference 12

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Observation 85b2fe03-46e8-4c43-a900-c24ee6680b2f · outbound

This paper cites Test-time training with self-supervision for generalization under distribution shifts.

Towards Understanding Extrapolation: a Causal Lens Test-time training with self-supervision for generalization under distribution shifts

Reference 13

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This paper cites Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, 34, 2021.

Towards Understanding Extrapolation: a Causal Lens Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, 34, 2021

Reference 14

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This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Towards Understanding Extrapolation: a Causal Lens Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 15

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This paper cites Think Locally, Act Globally: Federated Learning with Local and Global Representations.

Towards Understanding Extrapolation: a Causal Lens Think Locally, Act Globally: Federated Learning with Local and Global Representations

Reference 16

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This paper cites A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts.

Towards Understanding Extrapolation: a Causal Lens A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts

Reference 17

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This paper cites Partial disentanglement for domain adaptation.

Towards Understanding Extrapolation: a Causal Lens Partial disentanglement for domain adaptation

Reference 18

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This paper cites Subspace identification for multi-source domain adaptation.

Towards Understanding Extrapolation: a Causal Lens Subspace identification for multi-source domain adaptation

Reference 19

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This paper cites Test-time training with masked autoencoders.

Towards Understanding Extrapolation: a Causal Lens Test-time training with masked autoencoders

Reference 20

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This paper cites Tesla: Test- time self-learning with automatic adversarial augmentation.

Towards Understanding Extrapolation: a Causal Lens Tesla: Test- time self-learning with automatic adversarial augmentation

Reference 21

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This paper cites A theory of learning from different domains.

Towards Understanding Extrapolation: a Causal Lens A theory of learning from different domains

Reference 22

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This paper cites Covariate shift adaptation by importance weighted cross validation.

Towards Understanding Extrapolation: a Causal Lens Covariate shift adaptation by importance weighted cross validation

Reference 23

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This paper cites Additive decoders for latent variables identification and cartesian-product extrapolation.

Towards Understanding Extrapolation: a Causal Lens Additive decoders for latent variables identification and cartesian-product extrapolation

Reference 24

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This paper cites Com- positional generalization from first principles.

Towards Understanding Extrapolation: a Causal Lens Com- positional generalization from first principles

Reference 25

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This paper cites Provable Compositional Generalization for Object-Centric Learning.

Towards Understanding Extrapolation: a Causal Lens Provable Compositional Generalization for Object-Centric Learning

Reference 26

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This paper cites Toward compositional generalization in object-oriented world modeling.

Towards Understanding Extrapolation: a Causal Lens Toward compositional generalization in object-oriented world modeling

Reference 27

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This paper cites Learning to extrapolate: A transductive approach.

Towards Understanding Extrapolation: a Causal Lens Learning to extrapolate: A transductive approach

Reference 28

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This paper cites Engression: Extrapolation for nonlinear regression? In 2023 IMS International Conference on Statistics and Data Science (ICSDS), page 232, 2023.

Towards Understanding Extrapolation: a Causal Lens Engression: Extrapolation for nonlinear regression? In 2023 IMS International Conference on Statistics and Data Science (ICSDS), page 232, 2023

Reference 29

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This paper cites First steps toward understanding the extrapolation of nonlinear models to unseen domains.

Towards Understanding Extrapolation: a Causal Lens First steps toward understanding the extrapolation of nonlinear models to unseen domains

Reference 30

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This paper cites Identifying representations for intervention extrapolation.

Towards Understanding Extrapolation: a Causal Lens Identifying representations for intervention extrapolation

Reference 31

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Towards Understanding Extrapolation: a Causal Lens Independent component analysis: algorithms and applications

Reference 32

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This paper cites Variational au- toencoders and nonlinear ica: A unifying framework.

Towards Understanding Extrapolation: a Causal Lens Variational au- toencoders and nonlinear ica: A unifying framework

Reference 33

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Towards Understanding Extrapolation: a Causal Lens Kingma, and Aapo Hyvarinen

Reference 34

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Towards Understanding Extrapolation: a Causal Lens Unsupervised feature extraction by time-contrastive learning and nonlinear ica

Reference 35

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Towards Understanding Extrapolation: a Causal Lens Nonlinear ica using auxiliary variables and generalized contrastive learning

Reference 36

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This paper cites Self-supervised learning with data augmentations provably isolates content from style, 2021.

Towards Understanding Extrapolation: a Causal Lens Self-supervised learning with data augmentations provably isolates content from style, 2021

Reference 37

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This paper cites Learning Discrete Concepts in Latent Hierarchical Models.

Towards Understanding Extrapolation: a Causal Lens Learning Discrete Concepts in Latent Hierarchical Models

Reference 38

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Towards Understanding Extrapolation: a Causal Lens Provably learning object-centric representations

Reference 39

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This paper cites A dirt-t approach to unsupervised domain adaptation.

Towards Understanding Extrapolation: a Causal Lens A dirt-t approach to unsupervised domain adaptation

Reference 40

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Towards Understanding Extrapolation: a Causal Lens Semi-supervised learning by entropy minimization

Reference 41

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This paper cites Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.

Towards Understanding Extrapolation: a Causal Lens Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Reference 42

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This paper cites Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation.

Towards Understanding Extrapolation: a Causal Lens Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation

Reference 43

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This paper cites Revisiting realistic test-time training: Sequential inference and adaptation by anchored clustering.

Towards Understanding Extrapolation: a Causal Lens Revisiting realistic test-time training: Sequential inference and adaptation by anchored clustering

Reference 44

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This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Towards Understanding Extrapolation: a Causal Lens Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 45

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Towards Understanding Extrapolation: a Causal Lens Contrastive multiview coding

Reference 46

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Towards Understanding Extrapolation: a Causal Lens Deep residual learning for image recognition

Reference 47

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Towards Understanding Extrapolation: a Causal Lens Lora: Low-rank adaptation of large language models

Reference 48

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This paper cites Towards understanding gd with hard and conjugate pseudo-labels for test-time adaptation.

Towards Understanding Extrapolation: a Causal Lens Towards understanding gd with hard and conjugate pseudo-labels for test-time adaptation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:03.816598Z

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Towards Understanding Extrapolation: a Causal Lens Test time adaptation via conjugate pseudo-labels

Reference 50

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Towards Understanding Extrapolation: a Causal Lens Ttn: A domain-shift aware batch normalization in test-time adaptation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:03.797781Z

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This paper cites Learning to Generalize across Domains on Single Test Samples.

Towards Understanding Extrapolation: a Causal Lens Learning to Generalize across Domains on Single Test Samples

Reference 52

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This paper cites Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization.

Towards Understanding Extrapolation: a Causal Lens Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization

Reference 53

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This paper cites Test- time adaptation of discriminative models via diffusion generative feedback.

Towards Understanding Extrapolation: a Causal Lens Test- time adaptation of discriminative models via diffusion generative feedback

Reference 54

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This paper cites Swapprompt: Test-time prompt adaptation for vision-language models.

Towards Understanding Extrapolation: a Causal Lens Swapprompt: Test-time prompt adaptation for vision-language models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:03.766108Z

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Observation 3ce54e17-e431-43bf-ae18-53c099210abb · outbound

This paper cites Actmad: Activation matching to align distributions for test-time-training.

Towards Understanding Extrapolation: a Causal Lens Actmad: Activation matching to align distributions for test-time-training

Reference 56

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Observation 7ee53472-b8ef-4c49-93d2-e4d1d8605797 · outbound

This paper cites On the robustness of open-world test-time training: Self-training with dynamic prototype expansion.

Towards Understanding Extrapolation: a Causal Lens On the robustness of open-world test-time training: Self-training with dynamic prototype expansion

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:03.747885Z

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Towards Understanding Extrapolation: a Causal Lens Feature alignment and uniformity for test time adaptation

Reference 58

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Observation abcafff4-fc2c-4d03-b633-9f28132dbf7e · outbound

This paper cites Test-Time Adaptation via Self-Training with Nearest Neighbor Information.

Towards Understanding Extrapolation: a Causal Lens Test-Time Adaptation via Self-Training with Nearest Neighbor Information

Reference 59

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This paper cites Improved test-time adaptation for domain generalization.

Towards Understanding Extrapolation: a Causal Lens Improved test-time adaptation for domain generalization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:03.728172Z

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Observation 85d7b9cc-6e79-4341-b429-793698e722f4 · outbound

This paper cites Efficient test-time model adaptation without forgetting.

Towards Understanding Extrapolation: a Causal Lens Efficient test-time model adaptation without forgetting

Reference 61

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Towards Understanding Extrapolation: a Causal Lens Delta: Degradation-free fully test-time adaptation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:03.709658Z

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Towards Understanding Extrapolation: a Causal Lens Towards stable test-time adaptation in dynamic wild world

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:03.698038Z

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Observation 2b31b154-d94f-4488-9d5c-e635a863500a · outbound

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Towards Understanding Extrapolation: a Causal Lens Test-time classifier adjustment module for model-agnostic domain generalization

Reference 64

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Observation dd7321c4-1e32-4aa9-953a-a58c931aa317 · outbound

This paper cites Contrastive test-time adaptation.

Towards Understanding Extrapolation: a Causal Lens Contrastive test-time adaptation

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:03.383389Z

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This paper cites On pitfalls of test-time adaptation.

Towards Understanding Extrapolation: a Causal Lens On pitfalls of test-time adaptation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:03.672078Z

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Observation 0ef1fe4c-f105-4b06-85e4-fc3b918ff63d · outbound

This paper cites Auto-Encoding Variational Bayes.

Towards Understanding Extrapolation: a Causal Lens Auto-Encoding Variational Bayes

Reference 67

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This paper cites Adam: A method for stochastic optimization.

Towards Understanding Extrapolation: a Causal Lens Adam: A method for stochastic optimization

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:03.659853Z

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Observation 6faebd57-75b3-4199-8585-a2d1485dd129 · outbound

This paper cites Learning multiple layers of features from tiny images.

Towards Understanding Extrapolation: a Causal Lens Learning multiple layers of features from tiny images

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:03.398036Z

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This paper cites Improved Baselines with Momentum Contrastive Learning.

Towards Understanding Extrapolation: a Causal Lens Improved Baselines with Momentum Contrastive Learning

Reference 70

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This paper cites Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification.

Towards Understanding Extrapolation: a Causal Lens Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification

Reference 71

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Observation e25ee6c0-4bbf-4a0c-8b64-846837976702 · outbound

This paper cites In this stage, only the encoder is updated.

Towards Understanding Extrapolation: a Causal Lens In this stage, only the encoder is updated

Reference 72

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