Improving realistic semi-supervised learning with doubly robust estimation | Paper Citation Record

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Improving realistic semi-supervised learning with doubly robust estimation write newline

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This paper cites Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation.

Improving realistic semi-supervised learning with doubly robust estimation Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation

Reference 2

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Improving realistic semi-supervised learning with doubly robust estimation E., and McGuinness, K

Reference 3

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This paper cites Regularized Learning for Domain Adaptation under Label Shifts.

Improving realistic semi-supervised learning with doubly robust estimation Regularized Learning for Domain Adaptation under Label Shifts

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This paper cites ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring.

Improving realistic semi-supervised learning with doubly robust estimation ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

Reference 5

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 7

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Improving realistic semi-supervised learning with doubly robust estimation Semi-supervised learning (chapelle, o

Reference 8

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This paper cites Double/debiased machine learning for treatment and structural parameters, 2018.

Improving realistic semi-supervised learning with doubly robust estimation Double/debiased machine learning for treatment and structural parameters, 2018

Reference 9

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Improving realistic semi-supervised learning with doubly robust estimation M., and Syrgkanis, V

Reference 10

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This paper cites An analysis of single-layer networks in unsupervised feature learning.

Improving realistic semi-supervised learning with doubly robust estimation An analysis of single-layer networks in unsupervised feature learning

Reference 11

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Improving realistic semi-supervised learning with doubly robust estimation Class-balanced loss based on effective number of samples

Reference 12

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This paper cites SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning.

Improving realistic semi-supervised learning with doubly robust estimation SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning

Reference 13

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This paper cites Rda: Reciprocal distribution alignment for robust semi-supervised learning.

Improving realistic semi-supervised learning with doubly robust estimation Rda: Reciprocal distribution alignment for robust semi-supervised learning

Reference 14

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This paper cites Towards semi-supervised learning with non-random missing labels.

Improving realistic semi-supervised learning with doubly robust estimation Towards semi-supervised learning with non-random missing labels

Reference 15

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This paper cites Cossl: Co-learning of representation and classifier for imbalanced semi-supervised learning.

Improving realistic semi-supervised learning with doubly robust estimation Cossl: Co-learning of representation and classifier for imbalanced semi-supervised learning

Reference 16

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 17

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This paper cites Decouple then Converge: Handling Unknown Unlabeled Distributions in Long-Tailed Semi-Supervised Learning.

Improving realistic semi-supervised learning with doubly robust estimation Decouple then Converge: Handling Unknown Unlabeled Distributions in Long-Tailed Semi-Supervised Learning

Reference 18

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This paper cites A unified view of label shift estimation.

Improving realistic semi-supervised learning with doubly robust estimation A unified view of label shift estimation

Reference 19

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Improving realistic semi-supervised learning with doubly robust estimation and Bengio, Y

Reference 20

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This paper cites On Non-Random Missing Labels in Semi-Supervised Learning.

Improving realistic semi-supervised learning with doubly robust estimation On Non-Random Missing Labels in Semi-Supervised Learning

Reference 21

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 22

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Improving realistic semi-supervised learning with doubly robust estimation Deepmatch: Balancing deep covariate representations for causal inference using adversarial training

Reference 23

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 24

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 25

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Improving realistic semi-supervised learning with doubly robust estimation J., and Shin, J

Reference 26

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Improving realistic semi-supervised learning with doubly robust estimation and Hinton, G

Reference 27

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Improving realistic semi-supervised learning with doubly robust estimation Temporal Ensembling for Semi-Supervised Learning

Reference 28

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 29

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Improving realistic semi-supervised learning with doubly robust estimation Abc: Auxiliary balanced classifier for class-imbalanced semi-supervised learning

Reference 30

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Improving realistic semi-supervised learning with doubly robust estimation Detecting and correcting for label shift with black box predictors

Reference 31

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Improving realistic semi-supervised learning with doubly robust estimation Three heads are better than one: Complementary experts for long-tailed semi-supervised learning

Reference 32

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Improving realistic semi-supervised learning with doubly robust estimation Long-tail learning via logit adjustment

Reference 33

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Improving realistic semi-supervised learning with doubly robust estimation Label Shift Estimators for Non-Ignorable Missing Data

Reference 34

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 35

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Improving realistic semi-supervised learning with doubly robust estimation Adapting to Shifting Correlations with Unlabeled Data Calibration

Reference 36

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 37

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Improving realistic semi-supervised learning with doubly robust estimation A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects

Reference 38

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Improving realistic semi-supervised learning with doubly robust estimation Balanced meta-softmax for long-tailed visual recognition

Reference 39

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 40

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Improving realistic semi-supervised learning with doubly robust estimation Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure

Reference 41

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Improving realistic semi-supervised learning with doubly robust estimation Don't fear the unlabelled: safe semi-supervised learning via simple debiasing

Reference 42

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Improving realistic semi-supervised learning with doubly robust estimation Adapting neural networks for the estimation of treatment effects

Reference 43

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Improving realistic semi-supervised learning with doubly robust estimation A., Cubuk, E

Reference 44

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Improving realistic semi-supervised learning with doubly robust estimation Are labels informative in semi-supervised learning? estimating and leveraging the missing-data mechanism

Reference 45

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Improving realistic semi-supervised learning with doubly robust estimation P., Ebrahimi, S., and D'Amour, A

Reference 46

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 48

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Improving realistic semi-supervised learning with doubly robust estimation and Athey, S

Reference 49

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Improving realistic semi-supervised learning with doubly robust estimation Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning

Reference 50

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Improving realistic semi-supervised learning with doubly robust estimation and Gan, K

Reference 51

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Improving realistic semi-supervised learning with doubly robust estimation Learning label shift correction for test-agnostic long-tailed recognition

Reference 52

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Improving realistic semi-supervised learning with doubly robust estimation Unresolved cited work

Reference 53

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Improving realistic semi-supervised learning with doubly robust estimation Towards Causal Foundation Model: on Duality between Causal Inference and Attention

Reference 54

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Improving realistic semi-supervised learning with doubly robust estimation Dc-ssl: Addressing mismatched class distribution in semi-supervised learning

Reference 55

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Improving realistic semi-supervised learning with doubly robust estimation Doubly-robust self-training

Reference 56

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