But What Did You Actually Learn? Improving Inference for Non-Identifiable Deep Latent Variable Models von Yacoby, Yaniv

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The application of machine learning methods to evaluate predictors of live birth in programmed thaw cycles von Vaughan, Denis, Pan, Weiwei, Yacoby, Yaniv, Seidler, Emily A., Leung, Angela Q., Doshi-Velez, Finale, Sakkas, Denny

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Building personalized machine learning models using real-time monitoring data to predict idiographic suicidal thoughts von Wang, Shirley B., Van Genugten, Ruben D. I., Yacoby, Yaniv, Pan, Weiwei, Bentley, Kate H., Bird, Suzanne A., Buonopane, Ralph J., Christie, Alexis, Daniel, Merryn, DeMarco, Dylan, Haim, Adam, Follet, Lia, Fortgang, Rebecca G., Kelly-Brunyak, Flynn, Kleiman, Evan M., Millner, Alexander J., Obi-Obasi, Onyinye, Onnela, J. P., Ramlal, Narise, Ricard, Jordyn R., Smoller, Jordan W., Tambedou, Tida, Zuromski, Kelly L., Nock, Matthew K.

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Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders von Yacoby, Yaniv, Pan, Weiwei, Doshi-Velez, Finale

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Empowering First-Year Computer Science Ph.D. Students to Create a Culture that Values Community and Mental Health von Yacoby, Yaniv, Girash, John, Parkes, David C

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"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment von Yacoby, Yaniv, Green, Ben, GriffinJr, Christopher L, Velez, Finale Doshi

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Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks von Yacoby, Yaniv, Pan, Weiwei, Doshi-Velez, Finale

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Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders von Yacoby, Yaniv, Pan, Weiwei, Doshi-Velez, Finale

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Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders von Yacoby, Yaniv, Pan, Weiwei, Doshi-Velez, Finale

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Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables von Yacoby, Yaniv, Pan, Weiwei, Doshi-Velez, Finale

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An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks von Yao, Jiayu, Yacoby, Yaniv, Coker, Beau, Pan, Weiwei, Doshi-Velez, Finale

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Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables von Yacoby, Yaniv, Pan, Weiwei, Doshi-Velez, Finale

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Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks von Yacoby, Yaniv, Pan, Weiwei, Doshi-Velez, Finale

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BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty von Guénais, Théo, Vamvourellis, Dimitris, Yacoby, Yaniv, Doshi-Velez, Finale, Pan, Weiwei

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Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks von Thakur, Sujay, Lorsung, Cooper, Yacoby, Yaniv, Doshi-Velez, Finale, Pan, Weiwei

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"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment von Yacoby, Yaniv, Green, Ben, Griffin, Christopher L, Finale Doshi Velez

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Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders von Yacoby, Yaniv, Pan, Weiwei, Doshi-Velez, Finale

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An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks von Yao, Jiayu, Yacoby, Yaniv, Coker, Beau, Pan, Weiwei, Doshi-Velez, Finale

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Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks von Thakur, Sujay, Cooper Lorsung, Yacoby, Yaniv, Doshi-Velez, Finale, Pan, Weiwei

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BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty von Guénais, Théo, Vamvourellis, Dimitris, Yacoby, Yaniv, Doshi-Velez, Finale, Pan, Weiwei

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