Advances in Approximate Bayesian Inference

NIPS 2017 Workshop; December 8, 2017
Seaside Ballroom, Long Beach Convention Center, Long Beach, USA

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

  1. AdaGeo: Adaptive Geometric Learning for Optimization and Sampling Poster
    Gabriele Abbati, Alessandra Tosi, Michael A Osborne, Seth Flaxman
  2. Proximity-constrained reinforcement learning
    Abhishek Bhatia, Jaan Altosaar, Shixiang Gu
  3. Sampling and inference for discrete random probability measures in probabilistic programs Poster
    Benjamin Bloem-Reddy, Emile Mathieu, Adam Foster, Tom Rainforth, Hong Ge, María Lomelí, Zoubin Ghahramani, Yee Whye Teh
  4. Nonparametric Inference for Auto-Encoding Variational Bayes Poster
    Erik Bodin, Iman Malik, Carl Henrik Ek, Neill D. F. Campbell
  5. Variational Inference for DPGMM with Coresets Poster
    Zalán Borsos, Olivier Bachem, Andreas Krause
  6. Finite mixture models are typically inconsistent for the number of components
    Diana Cai, Trevor Campbell, Tamara Broderick
  7. An Improved Bayesian Framework for Quadrature Poster
    Henry Chai, Roman Garnett
  8. Stochastic gradient descent performs variational inference, implicitly, and on a different loss Poster
    Pratik Chaudhari, Stefano Soatto
  9. Inference Suboptimality in Variational Autoencoders Poster
    Chris Cremer, Xuechen Li, David Duvenaud
  10. Abstraction Sampling in Graphical Models
    Rina Dechter, Filjor Broka, Kalev Kask, Alexander Ihler
  11. Computing the quality of the Laplace approximation
    Guillaume Dehaene
  12. Learning Implicit Generative Models Using Differentiable Graph Tests
    Josip Djolonga, Andreas Krause
  13. A Universal Marginalizer for Amortized Inference in Generative Models Poster
    Laura Douglas, Iliyan Zarov, Konstantinos Gourgoulias, Chris Lucas, Christopher Hart, Adam Baker, Maneesh Sahani, Yura Perov, Saurabh Johri
  14. Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors Poster
    Ertunc Erdil, Sinan Yildirim, Tolga Tasdizen, Mujdat Cetin
  15. Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF) Poster
    Trefor Evans, Prasanth Nair
  16. Variational Inference based on Robust Divergences Poster
    Futoshi Futami, Issei Sato, Masashi Sugiyama
  17. Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo Poster
    Rong Ge, Holden Lee, Andrej Risteski
  18. Inverting VAEs for Improved Generative Accuracy Poster
    Ian Gemp, Mario Parente, Sridhar Mahadevan
  19. Measuring Cluster Stability for Bayesian non-Parametrics Using the Linear Bootstrap Poster
    Ryan Giordano, Runjing Liu, Nelle Varoquaux, Michael Jordan, Tamara Broderick
  20. Bayesian Gaussian Process Modeling of Large Scale Longitudinal Neuroimaging data
    Qing He, Jian Kang
  21. Sparse Bayesian Logistic Regression with Hierarchical Prior and Variational Inference Poster
    Shunsuke Horii
  22. Generic finite approximations for practical Bayesian nonparametrics
    Jonathan Huggins, Lorenzo Masoero, Tamara Broderick, Lester Mackey
  23. Generalizing and Scaling up Dynamic Topic Models via Inducing Point Variational Inference
    Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt
  24. Online and Distributed Learning of Gaussian Mixture Models by Bayesian Moment Matching
    Priyank Jaini, Pascal Poupart
  25. Efficient acquisition rules for model-based approximate Bayesian computation
    Marko Järvenpää, Michael Gutmann, Arijus Pleska, Aki Vehtari, Pekka Marttinen
  26. Bayesian Q-learning with Assumed Density Filtering Poster
    Heejin Jeong, Daniel Lee
  27. Bayesian Paragraph Vectors Poster
    Geng Ji, Robert Bamler, Erik Sudderth, Stephan Mandt
  28. Adversarial Sequential Monte Carlo Poster
    Kira Kempinska, John Shawe-Taylor
  29. Variational Adaptive-Newton Method Poster
    Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen
  30. Bayesian Nonnegative Matrix Factorization as an Allocation Model Poster
    Burak Kurutmaz, Taylan Cemgil, Umut Simsekli, Sinan Yildirim
  31. Bayesian Nonparametric Clustering and Inference for Health Care Utilization of Interstitial Lung Disease Patients
    Christoph Kurz
  32. Generalizing Hamiltonian Monte Carlo with Neural Networks Poster
    Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein
  33. Bayesian inference for latent Hawkes processes
    Scott Linderman, Yixin Wang, David Blei
  34. Boosting Variational Inference: an Optimization Perspective
    Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Ratsch
  35. On Exploration, Exploitation and Learning in Adaptive Importance Sampling
    Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Yee Whye Teh, Frank Wood, Hongseok Yang, Jan-Willem van de Meent
  36. Thermostat-assisted continuous-tempered Hamiltonian Monte Carlo for multimodal posterior sampling Poster
    Rui Luo, Yaodong Yang, Jun Wang, Yuanyuan Liu
  37. Black-box Stein Divergence Minimization For Learning Latent Variable Models
    Chao Ma, David Barber
  38. CaGeM: A Cluster Aware Deep Generative Model Poster
    Lars Maaløe, Marco Fraccaro, Ole Winther
  39. Sample-then-optimize posterior sampling for Bayesian linear models
    Alexander Matthews, Jiri Hron, Richard Turner, Zoubin Ghahramani
  40. Scalable Bayesian Record Linkage Poster
    Brendan McVeigh, Jared Murray
  41. Taylor Residual Estimators via Automatic Differentiation Poster
    Andrew Miller, Nicholas Foti, Ryan Adams
  42. Unbiased density estimation for stochastically scaled Gaussian vectors using random Riemann sums
    Patrick Muchmore, Paul Marjoram
  43. Variational Inference with Stein Mixtures
    Eric Nalisnick, Padhraic Smyth
  44. Binary Bouncy Particle Sampler
    Ari Pakman
  45. Nesting Probabilistic Programs Poster
    Tom Rainforth
  46. Inference Trees: Adaptive Inference with Exploration Poster
    Tom Rainforth, Yuan Zhou, Xiaoyu Lu, Yee Whye Teh, Frank Wood, Hongseok Yang, Jan-Willem van de Meent
  47. Stabilizing Generative Adverserial Networks using Langevin dynamics
    Julius Ramakers, Stefan Harmeling, Markus Kollmann
  48. Scalable Large-Scale Classification with Latent Variable Augmentation Poster
    Francisco Ruiz, Michalis Titsias, David Blei
  49. Microsimulation Model Calibration using Incremental Mixture Approximate Bayesian Computation Poster
    Carolyn Rutter, Maria Deyoreo, Jonathan Ozik, Nicholson Collier
  50. Locally Private Bayesian Inference for Count Models class="btn btn-default btn-xs">Poster
    Aaron Schein, Steven Wu, Mingyuan Zhou, Hanna Wallach
  51. Consistency of Markov Chain quasi Monte Carlo with multiple proposals Poster
    Tobias Schwedes, Ben Calderhead
  52. Probabilistic reconstruction of cellular differentiation trees from single-cell RNA-seq data Poster
    Miriam Shiffman, Will Stephenson, Geoffrey Schiebinger, Trevor Campbell, Jonathan Huggins, Aviv Regev, Tamara Broderick
  53. Structured Variational Autoencoders for the Beta-Bernoulli Process Poster
    Rachit Singh, Jeffrey Ling, Finale Doshi-Velez
  54. Understanding Expectation Propagation Poster
    Siddharth Swaroop, Richard Turner
  55. Natural Gradients via the Variational Predictive Distribution Poster
    Da Tang, Rajesh Ranganath
  56. Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods Poster
    Alexander Terenin, Eric Xing
  57. Overpruning in Variational Bayesian Neural Networks Poster
    Brian Trippe, Richard Turner
  58. Structured Variational Inference for Coupled Gaussian Processes Poster
    Adam Vincent
  59. Frequentist Consistency of Variational Bayes
    Yixin Wang, David Blei
  60. Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference Poster
    Stefan Webb, Adam Golinski, Robert Zinkov, Frank Wood
  61. Scalable Logit Gaussian Process Classification Poster
    Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper
  62. Inferencing Tweedie Compound Poisson Mixed Models with Adversarial Variational Bayes Poster
    Yaodong Yang, Rui Luo, Reza Khorshidi, Yuanyuan Liu
  63. Diversified Mini-Batch Sampling using Repulsive Point Processes Poster
    Cheng Zhang, Cengiz Oztireli, Stephan Mandt
  64. Embarrassingly Parallel Inference for Gaussian Processes
    Michael Zhang, Sinead Williamson
  65. Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks
    Pengfei Zhang, Ido Nevat, Gareth Peters, Wolfgang Fruhwirt, Yongchao Huang, Michael Osborne
  66. Regularized Variational Sparse Gaussian Processes Poster
    Shandian Zhe
  67. Automating Expectation Maximixation
    Robert Zinkov