NIPS Proceedingsβ

Zoubin Ghahramani

48 Papers

  • MCMC for Variationally Sparse Gaussian Processes (2015)
  • Neural Adaptive Sequential Monte Carlo (2015)
  • Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions (2015)
  • Particle Gibbs for Infinite Hidden Markov Models (2015)
  • Statistical Model Criticism using Kernel Two Sample Tests (2015)
  • Gaussian Process Volatility Model (2014)
  • General Table Completion using a Bayesian Nonparametric Model (2014)
  • Predictive Entropy Search for Efficient Global Optimization of Black-box Functions (2014)
  • Active Learning of Model Evidence Using Bayesian Quadrature (2012)
  • A nonparametric variable clustering model (2012)
  • Collaborative Gaussian Processes for Preference Learning (2012)
  • Continuous Relaxations for Discrete Hamiltonian Monte Carlo (2012)
  • Random function priors for exchangeable arrays with applications to graphs and relational data (2012)
  • Testing a Bayesian Measure of Representativeness Using a Large Image Database (2011)
  • Copula Processes (2010)
  • Tree-Structured Stick Breaking for Hierarchical Data (2010)
  • Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process (2009)
  • Bayesian Exponential Family PCA (2008)
  • The Infinite Factorial Hidden Markov Model (2008)
  • Hidden Common Cause Relations in Relational Learning (2007)
  • Modeling Dyadic Data with Binary Latent Factors (2006)
  • Relational Learning with Gaussian Processes (2006)
  • Bayesian Sets (2005)
  • Infinite latent feature models and the Indian buffet process (2005)
  • Learning Multiple Related Tasks using Latent Independent Component Analysis (2005)
  • Nested sampling for Potts models (2005)
  • Sparse Gaussian Processes using Pseudo-inputs (2005)
  • A Probabilistic Model for Online Document Clustering with Application to Novelty Detection (2004)
  • Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning (2004)
  • Warped Gaussian Processes (2003)
  • Bayesian Monte Carlo (2002)
  • Learning with Multiple Labels (2002)
  • Infinite Mixtures of Gaussian Process Experts (2001)
  • The Infinite Hidden Markov Model (2001)
  • Occam's Razor (2000)
  • Propagation Algorithms for Variational Bayesian Learning (2000)
  • Learning to Parse Images (1999)
  • Variational Inference for Bayesian Mixtures of Factor Analysers (1999)
  • Learning Nonlinear Dynamical Systems Using an EM Algorithm (1998)
  • SMEM Algorithm for Mixture Models (1998)
  • Hierarchical Non-linear Factor Analysis and Topographic Maps (1997)
  • Hidden Markov Decision Trees (1996)
  • Factorial Hidden Markov Models (1995)
  • Active Learning with Statistical Models (1994)
  • Computational Structure of coordinate transformations: A generalization study (1994)
  • Factorial Learning and the EM Algorithm (1994)
  • Forward dynamic models in human motor control: Psychophysical evidence (1994)
  • Supervised learning from incomplete data via an EM approach (1993)

    • 3 Books

  • Advances in Neural Information Processing Systems 27 (2014)
  • Advances in Neural Information Processing Systems 26 (2013)
  • Advances in Neural Information Processing Systems 14 (2001)