Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (AABI, 2019)

Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (CoRR, 2019)

Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks. - Sujay Thakur; Cooper Lorsung; Yaniv Yacoby; Finale Doshi-Velez; Weiwei Pan (CoRR, 2020)

BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty. - Théo Guénais; Dimitris Vamvourellis; Yaniv Yacoby; Finale Doshi-Velez; Weiwei Pan (CoRR, 2020)

Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (CoRR, 2020)

"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment. - Yaniv Yacoby; Ben Green 004; Christopher L. Griffin; Finale Doshi-Velez (HCOMP, 2022)

An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks. - Jiayu Yao; Yaniv Yacoby; Beau Coker; Weiwei Pan; Finale Doshi-Velez (CoRR, 2022)

Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (J. Mach. Learn. Res., 2022)

Empowering First-Year Computer Science Ph.D. Students to Create a Culture that Values Community and Mental Health. - Yaniv Yacoby; John Girash; David C. Parkes (SIGCSE (1), 2023)

Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders. - Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (CoRR, 2024)

Transparent Trade-offs between Properties of Explanations. - Hiwot Belay Tadesse; Alihan Hüyük; Yaniv Yacoby; Weiwei Pan; Finale Doshi-Velez (UAI, 2025)