Accepted Papers

Papers listed here are for archival purposes and do not constitute a proceedings for this workshop.

• Adaptive principal component analysis

Adam Farooq, Yordan Raykov, Luc Evers and Max Little

• Sampling Acquisition Functions for Batch Bayesian Optimization

Alessandro De Palma, Celestine Dünner, Thomas Parnell, Andreea Anghel and Haralampos Pozidis

• Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference

An Bian, Joachim Buhmann and Andreas Krause

• A nonparametric Bayes approach to hypothesis testing

Andrew Bass, Irineo Cabreros, Barbara Engelhardt and John Storey

• A Bayesian nonparametric approach to single cell RNA-seq manifold learning

Archit Verma and Barbara Engelhardt

• Amortized Bayesian inference for clustering models

Ari Pakman and Liam Paninski

• Grouped Gaussian Processes for Solar Forecasting

Astrid Dahl and Edwin Bonilla

• Bayesian Nonparametric Mixture Models Using NIMBLE

Claudia Wehrhahn, Abel Rodriguez and Christopher Paciorek

• Kernel Distillation for Fast Gaussian Processes Prediction

Congzheng Song and Yiming Sun

• Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model

Daniel Coelho de Castro and Sebastian Nowozin

• Stick-Breaking Neural Latent Variable Models

Daniel Flam-Shepherd, Yuxiang Gao and Zhaoyu Guo

• Derivative-Free Semiparametric Bayesian Models for Robot Learning

Diego Romeres, Devesh Jha, Alberto Dalla Libera, Alessandro Chiuso and Daniel Nikovski

• Composing Modeling and Inference Operations with Probabilistic Program Combinators

Eli Sennesh, Adam Scibior, Hao Wu and Jan-Willem van de Meent

• Active Learning Using Gaussian Processes for Imbalanced Datasets

Fariba Yousefi, Mauricio Alvarez, Neil Lawrence and Carl Henrik Ek

• Goodness-of-Fit Tests for High-Dimensional Discrete Distributions with Application to Convergence Diagnostics in Bayesian Nonparametric Inference

Feras Saad, Cameron Freer, Nathanael Ackerman and Vikash Mansinghka

• Fast Inference in Non-Conjugate Gaussian Process Models via Data Augmentation

Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft and Manfred Opper

• Sequence Alignment with Dirichlet Process Mixtures

Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek and Neill Campbell

• Nonparametric Gaussian mixture models for the multi-armed contextual bandit

Iñigo Urteaga and Chris Wiggins

• Bayesian Nonparametric Hawkes Processes

Jaivardhan Kapoor, Antonio Vergari, Manuel Gomez Rodriguez and Isabel Valera

• Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process

Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou and Brent Coull

• Detecting switching interaction using non-parametric Bayesian segmentation approach

Jeric Briones, Takatomi Kubo and Kazushi Ikeda

• Amortized Inference for Latent Feature Models Using Variational Russian Roulette

Kai Xu, Akash Srivastava and Charles Sutton

• Bayesian Learning of Conditional Kernel Means for Automatic Likelihood-Free Inference

Kelvin Hsu and Fabio Ramos

• Deep Nested Hierarchical Dirichlet Processes

Lavanya Sita Tekumalla, Priyanka Agrawal and Indrajit Bhattacharya.

• Distribution-based Clustering using Characteristic Function

Leo Duan and Jeffrey Miller

• Non-Parametric Variational Inference for Gaussian Processes with Graph Convolutional Networks

Linfeng Liu and Liping Liu

• Posterior representations of hierarchical completely random measures in trait allocation models

Lorenzo Masoero, Federico Camerlenghi, Stefano Favaro and Tamara Broderick

• Non-linear Latent Force Models using Volterra Series

Mauricio A Álvarez, Wil Ward and Cristian Guarnizo

• Bayesian Nonparametrics for Geographic RDDs

Maxime Rischard, Zach Branson, Luke Miratrix and Luke Bornn

• Hierarchical Stick-breaking Feature Paintbox

Melanie F. Pradier, Weiwei Pan, Morris Yau, Rachit Singh, and Finale Doshi-Velez

• A non-parametric probabilistic model for monotonic functions

Michael Riis Andersen, Eero Siivola, Gabriel Riutort-Mayol and Aki Vehtari

• Accelerated Inference for Latent Variable Models

Michael Zhang, Sinead Williamson and Fernando Perez-Cruz

• Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data

Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan Huggins, Trevor Campbell, Aviv Regev and Tamara Broderick

• Discovering Dynamic Topic Transitions in Topic Models

Olga Isupova, Danil Kuzin and Lyudmila Mihaylova

• Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors

Prashnna Gyawali, Cameron Knight, Sandesh Ghimire, Milan Horacek, John Sapp and Linwei Wang

• Evaluating Sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics

Runjing Liu, Ryan Giordano, Jordan Michael and Tamara Broderick

• A Bayesian Model for Overlapping Clusters from Distance Data

Sandhya Prabhakaran and Julia Vogt

• On Batch Bayesian Optimization

Sayak Ray Chowdhury and Aditya Gopalan

• Bayesian Nonparametric Boolean Factor Models

Tammo Rukat and Christopher Yau

• A Restricted Space-Filling Algorithm for Bayesian Learning with a Nearest Neighbor Gaussian Process (BLING)

Tucker Keuter and Anjishnu Banerjee

• Scalable Gaussian Processes on Discrete Domains

Vincent Fortuin, Gideon Dresdner, Heiko Strathmann and Gunnar Rätsch

• Learning Mean Functions for Gaussian Processes

Vincent Fortuin

• Variational Bridge Constructs for Approximate Gaussian Process Regression

Wil Ward and Mauricio Álvarez

• Bayesian Nonparametrics for Non-exhaustive Learning

Yicheng Cheng, Bartek Rajwa and Murat Dundar