Person: Zoubin Ghahramani

DBLP: Ghahramani:Zoubin

Facilitated 1 volumes:

Ed

Contributed to:

2015

2014

2013

2013

2013

2013

2012

2011

2009

2008

2006

2005

2004

2003

2000

2015

2018

Wrote 40 papers:

ICML-2015-GalCG #category theory #estimation #multi #process

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data (YG, YC, ZG), pp. 645–654.

ICML-2015-GeCWG #distributed #modelling #process

Distributed Inference for Dirichlet Process Mixture Models (HG, YC, MW, ZG), pp. 2276–2284.

ICML-2015-Hernandez-Lobato #feature model #multi #probability

A Probabilistic Model for Dirty Multi-task Feature Selection (DHL, JMHL, ZG), pp. 1073–1082.

ICML-2015-Hernandez-Lobato15a #constraints #optimisation #predict

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (JMHL, MAG, MWH, RPA, ZG), pp. 1699–1707.

ICML-2015-ShahKG #algorithm #empirical #probability #process

An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process (AS, DAK, ZG), pp. 1594–1603.

ICML-c2-2014-BratieresQNG #graph #grid #predict #process #scalability

Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications (SB, NQ, SN, ZG), pp. 334–342.

ICML-c2-2014-GalG #parallel #process #using

Pitfalls in the use of Parallel Inference for the Dirichlet Process (YG, ZG), pp. 208–216.

ICML-c2-2014-HeaukulaniKG

Beta Diffusion Trees (CH, DAK, ZG), pp. 1809–1817.

ICML-c2-2014-Hernandez-LobatoHG #matrix #modelling #probability #scalability

Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices (JMHL, NH, ZG), pp. 379–387.

ICML-c2-2014-Hernandez-LobatoHG14a #matrix #probability

Probabilistic Matrix Factorization with Non-random Missing Data (JMHL, NH, ZG), pp. 1512–1520.

ICML-c2-2014-HoulsbyHG #learning #matrix #robust

Cold-start Active Learning with Robust Ordinal Matrix Factorization (NH, JMHL, ZG), pp. 766–774.

ICML-c2-2014-KnowlesGP #infinity #metric #normalisation #random #using

A reversible infinite HMM using normalised random measures (DAK, ZG, KP), pp. 1998–2006.

ICML-c2-2014-Lopez-PazSSGS #analysis #component #random

Randomized Nonlinear Component Analysis (DLP, SS, AJS, ZG, BS), pp. 1359–1367.

ICML-c1-2013-HeaukulaniG #modelling #network #probability #social

Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks (CH, ZG), pp. 275–283.

ICML-c2-2013-Lopez-PazHG #dependence #multi #process

Gaussian Process Vine Copulas for Multivariate Dependence (DLP, JMHL, ZG), pp. 10–18.

ICML-c3-2013-DuvenaudLGTG #composition #kernel #parametricity

Structure Discovery in Nonparametric Regression through Compositional Kernel Search (DKD, JRL, RBG, JBT, ZG), pp. 1166–1174.

ICML-c3-2013-ReedG #process #scalability

Scaling the Indian Buffet Process via Submodular Maximization (CR, ZG), pp. 1013–1021.

ICML-c3-2013-WuHG #modelling #multi

Dynamic Covariance Models for Multivariate Financial Time Series (YW, JMHL, ZG), pp. 558–566.

KDD-2013-IwataSG #online #process #social

Discovering latent influence in online social activities via shared cascade poisson processes (TI, AS, ZG), pp. 266–274.

KDD-2013-Lacoste-JulienPDKGG #knowledge base #named #scalability

SIGMa: simple greedy matching for aligning large knowledge bases (SLJ, KP, AD, GK, TG, ZG), pp. 572–580.

ICML-2012-MohamedHG #learning

Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning (SM, KAH, ZG), p. 91.

ICML-2012-PallaKG #infinity #network

An Infinite Latent Attribute Model for Network Data (KP, DAK, ZG), p. 55.

ICML-2012-PoczosGS #dependence #kernel #metric

Copula-based Kernel Dependency Measures (BP, ZG, JGS), p. 213.

ICML-2012-WilsonKG #network #process

Gaussian Process Regression Networks (AGW, DAK, ZG), p. 149.

ICML-2011-KnowlesGG #algorithm #message passing

Message Passing Algorithms for the Dirichlet Diffusion Tree (DAK, JVG, ZG), pp. 721–728.

ICML-2009-AdamsG #learning #named #parametricity

Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.

ICML-2009-Doshi-VelezG #process

Accelerated sampling for the Indian Buffet Process (FDV, ZG), pp. 273–280.

ICML-2008-GaelSTG #infinity #markov

Beam sampling for the infinite hidden Markov model (JVG, YS, YWT, ZG), pp. 1088–1095.

ICML-2008-HellerWG #modelling #statistics

Statistical models for partial membership (KAH, SW, ZG), pp. 392–399.

ICML-2006-AzranG #approach #clustering #data-driven

A new approach to data driven clustering (AA, ZG), pp. 57–64.

ICML-2005-ChuG #learning #process

Preference learning with Gaussian processes (WC, ZG), pp. 137–144.

ICML-2005-HellerG #clustering

Bayesian hierarchical clustering (KAH, ZG), pp. 297–304.

ICML-2005-SnelsonG #approximate #predict

Compact approximations to Bayesian predictive distributions (ES, ZG), pp. 840–847.

ICML-2004-ChuGW #predict #visual notation

A graphical model for protein secondary structure prediction (WC, ZG, DLW).

ICML-2004-QiMPG #automation #predict

Predictive automatic relevance determination by expectation propagation (Y(Q, TPM, RWP, ZG).

ICML-2003-SalakhutdinovRG #optimisation

Optimization with EM and Expectation-Conjugate-Gradient (RS, STR, ZG), pp. 672–679.

ICML-2003-ZhuGL #learning #using

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.

ICPR-v3-2000-AdamsSWG #named

MFDTs: Mean Field Dynamic Trees (NJA, AJS, CKIW, ZG), pp. 3151–3154.

Haskell-2015-ScibiorGG #monad #probability #programming

Practical probabilistic programming with monads (AS, ZG, ADG), pp. 165–176.

POPL-2018-ScibiorKVSYCOMH #higher-order #validation

Denotational validation of higher-order Bayesian inference (AS, OK, MV, SS, HY, YC, KO, SKM, CH, ZG), p. 29.