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Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data.
Using embeddings to correct for unobserved confounding in networks
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Deep causal representation learning for unsupervised domain adaptation
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Mixture Models: Inference and Applications to Clustering
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Markov Chain Monte Carlo in Practice
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Posterior predictive assessment of model fitness via realized discrepancies
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Probabilistic principal component analysis
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Inference of population structure using multilocus genotype data
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Probabilities of causation: Bounds and identification
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Johansson, F. D., Shalit, U., Kallus, N., & Sontag, D. (2020) · 2001
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Latent Dirichlet allocation
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Towards clarifying the theory of the deconfounder
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CausalVAE: Disentangled representation learning via neural structural causal models
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Bayesian mixed membership models for soft clustering and classification
Erosheva, E. A. & Fienberg, S. E. (2005) · 2005
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Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., & LeCun, Y. (2006) · 2006
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Is independence all you need? On the generalization of representations learned from correlated data
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Interventions and causal inference
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Representation learning via invariant causal mechanisms
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Causal effects of linguistic properties
Pryzant, R., Card, D., Jurafsky, D., Veitch, V., & Sridhar, D. (2020) · 2010
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Disentangled generative causal representation learning
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Latent aspect rating analysis on review text data: A rating regression approach
Wang, H., Lu, Y., & Zhai, C. (2010) · 2010
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Underspecification presents challenges for credibility in modern machine learning
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Causality: Models, Reasoning, and Inference
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Invariant representation learning for treatment effect estimation
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Latent aspect rating analysis without aspect keyword supervision
Wang, H., Lu, Y., & Zhai, C. (2011) · 2011
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The mnist database of handwritten digit images for machine learning research [best of the web]
Deng, L. (2012) · 2012
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On causal and anticausal learning
Schölkopf, B., Janzing, D., et al. (2012) · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · 2013
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Why ask why? Forward causal inference and reverse causal questions
Gelman, A. & Imbens, G. (2013) · 2013
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Semi-supervised learning in causal and anticausal settings
Schölkopf, B., Janzing, D., et al. (2013) · 2013
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Invariant representations without adversarial training
Moyer, D., Gao, S., Brekelmans, R., Steeg, G. V., & Galstyan, A. (2018) · 2018
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Learning independent causal mechanisms
Parascandolo, G., Kilbertus, N., Rojas-Carulla, M., & Schölkopf, B. (2018) · 2018
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Multiple causal inference with latent confounding
Ranganath, R. & Perotte, A. (2018) · 2018
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Disentangling the independently controllable factors of variation by interacting with the world
Thomas, V., Bengio, E., et al. (2018) · 2018
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Group-based learning of disentangled representations with generalizability for novel contents
Hosoya, H. (2019) · 2019
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Mixed membership stochastic blockmodels
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Generative adversarial networks
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Auto-encoding variational Bayes
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Stochastic backpropagation and variational inference in deep latent gaussian models
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Causal and anti-causal learning in pattern recognition for neuroimaging
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Visual causal feature learning
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Comment: The challenges of multiple causes
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Support and invertibility in domain-invariant representations
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Weakly supervised disentanglement with guarantees
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Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness
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Why are big data matrices approximately low rank?
Udell, M. & Townsend, A. (2019) · 2019
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On learning invariant representations for domain adaptation
Zhao, H., Des Combes, R. T., Zhang, K., & Gordon, G. (2019) · 2019
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A calculus for stochastic interventions: Causal effect identification and surrogate experiments
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Variational autoencoders and nonlinear ICA: A unifying framework
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Weakly-supervised disentanglement without compromises
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Semiparametric causal sufficient dimension reduction of high dimensional treatments
Nabi, R., McNutt, T., & Shpitser, I. (2020) · 2020
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Causal estimation with functional confounders
Puli, A., Perotte, A., & Ranganath, R. (2020) · 2020
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Adapting text embeddings for causal inference
Veitch, V., Sridhar, D., & Blei, D. (2020) · 2020
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Identifying spurious correlations for robust text classification
Wang, Z. & Culotta, A. (2020) · 2020
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Environment inference for invariant learning
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Explaining black-box algorithms using probabilistic contrastive counterfactuals
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Causal inference with latent treatments
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Invariant causal representation learning
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Causes of effects: Learning individual responses from population data
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Predictive modeling in the presence of nuisance-induced spurious correlations
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Counterfactual invariance to spurious correlations: Why and how to pass stress tests
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A proxy variable view of shared confounding
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Local explanations via necessity and sufficiency: unifying theory and practice
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