Search Results for author: Zoubin Ghahramani

Found 101 papers, 31 papers with code

Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers

no code implementations16 Sep 2021 Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zack Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

The performance of deep neural networks can be highly sensitive to the choice of a variety of meta-parameters, such as optimizer parameters and model hyperparameters.

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

no code implementations10 May 2021 Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

Deep Gaussian processes (DGPs) have struggled for relevance in applications due to the challenges and cost associated with Bayesian inference.

Bayesian Inference Gaussian Processes

Metropolis Algorithms for Representative Subgraph Sampling

1 code implementation ‏‏‎ ‎ 2020 Christian Hübler, Hans-Peter Kriegel, Karsten Borgwardt, Zoubin Ghahramani

While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and the Internet are now generating graph data with thousands and millions of nodes.

Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects

no code implementations21 Apr 2020 Will Y. Zou, Smitha Shyam, Michael Mui, Mingshi Wang, Jan Pedersen, Zoubin Ghahramani

We propose to formulate the effectiveness of treatment as a parametrizable model, expanding to a multitude of treatment intensities and complexities through the continuous policy treatment function, and the likelihood of matching.

Causal Inference

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

no code implementations ICML 2020 Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van Den Broeck, Kristian Kersting, Zoubin Ghahramani

Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines.

DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models

1 code implementation7 Feb 2020 Mohamed Tarek, Kai Xu, Martin Trapp, Hong Ge, Zoubin Ghahramani

Since DynamicPPL is a modular, stand-alone library, any probabilistic programming system written in Julia, such as Turing. jl, can use DynamicPPL to specify models and trace their model parameters.

Probabilistic Programming

Resource-Efficient Neural Networks for Embedded Systems

no code implementations7 Jan 2020 Wolfgang Roth, Günther Schindler, Matthias Zöhrer, Lukas Pfeifenberger, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches.

Autonomous Navigation Network Pruning

Bayesian Learning of Sum-Product Networks

1 code implementation NeurIPS 2019 Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani

While parameter learning in SPNs is well developed, structure learning leaves something to be desired: Even though there is a plethora of SPN structure learners, most of them are somewhat ad-hoc and based on intuition rather than a clear learning principle.

Probabilistic Meta-Representations Of Neural Networks

no code implementations1 Oct 2018 Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani

Existing Bayesian treatments of neural networks are typically characterized by weak prior and approximate posterior distributions according to which all the weights are drawn independently.

Automatic Bayesian Density Analysis

no code implementations24 Jul 2018 Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera

Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference.

Anomaly Detection Bayesian Inference +1

Handling Incomplete Heterogeneous Data using VAEs

2 code implementations10 Jul 2018 Alfredo Nazabal, Pablo M. Olmos, Zoubin Ghahramani, Isabel Valera

Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data.

Imputation

Variational Bayesian dropout: pitfalls and fixes

no code implementations ICML 2018 Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani

Dropout, a stochastic regularisation technique for training of neural networks, has recently been reinterpreted as a specific type of approximate inference algorithm for Bayesian neural networks.

Antithetic and Monte Carlo kernel estimators for partial rankings

no code implementations1 Jul 2018 Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani

We also present a novel variance reduction scheme based on an antithetic variate construction between permutations to obtain an improved estimator for the Mallows kernel.

Multi-Object Tracking Recommendation Systems

Discovering Interpretable Representations for Both Deep Generative and Discriminative Models

no code implementations ICML 2018 Tameem Adel, Zoubin Ghahramani, Adrian Weller

We use a generative model which takes as input the representation in an existing (generative or discriminative) model, weakly supervised by limited side information.

Active Learning

Probabilistic Deep Learning using Random Sum-Product Networks

no code implementations5 Jun 2018 Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani

The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods.

Probabilistic Deep Learning

Gaussian Process Behaviour in Wide Deep Neural Networks

1 code implementation ICLR 2018 Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani

Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties.

Gaussian Processes

The Mirage of Action-Dependent Baselines in Reinforcement Learning

1 code implementation ICML 2018 George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine

Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance.

Policy Gradient Methods

Weakly supervised collective feature learning from curated media

no code implementations13 Feb 2018 Yusuke Mukuta, Akisato Kimura, David B Adrian, Zoubin Ghahramani

Through these insights, we can define human curated groups as weak labels from which our proposed framework can learn discriminative features as a representation in the space of semantic concepts the users intended when creating the groups.

Link Prediction

Variational Gaussian Dropout is not Bayesian

no code implementations8 Nov 2017 Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani

Gaussian multiplicative noise is commonly used as a stochastic regularisation technique in training of deterministic neural networks.

Bayesian Inference

Automatic Discovery of the Statistical Types of Variables in a Dataset

1 code implementation ICML 2017 Isabel Valera, Zoubin Ghahramani

A common practice in statistics and machine learning is to assume that the statistical data types (e. g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known.

A Birth-Death Process for Feature Allocation

no code implementations ICML 2017 Konstantina Palla, David Knowles, Zoubin Ghahramani

We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birth-death feature allocation process (BDFP).

General Latent Feature Modeling for Data Exploration Tasks

no code implementations26 Jul 2017 Isabel Valera, Melanie F. Pradier, Zoubin Ghahramani

This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables.

Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks

no code implementations8 Jul 2017 John Bradshaw, Alexander G. de G. Matthews, Zoubin Ghahramani

However, they often do not capture their own uncertainties well making them less robust in the real world as they overconfidently extrapolate and do not notice domain shift.

Gaussian Processes

Lost Relatives of the Gumbel Trick

1 code implementation ICML 2017 Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller

We show how a subfamily of our new methods adapts to this setting, proving new upper and lower bounds on the log partition function and deriving a family of sequential samplers for the Gibbs distribution.

General Latent Feature Models for Heterogeneous Datasets

1 code implementation12 Jun 2017 Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani

Second, its Bayesian nonparametric nature allows us to automatically infer the model complexity from the data, i. e., the number of features necessary to capture the latent structure in the data.

Deep Bayesian Active Learning with Image Data

3 code implementations ICML 2017 Yarin Gal, Riashat Islam, Zoubin Ghahramani

In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way.

Active Learning

Bayesian inference on random simple graphs with power law degree distributions

no code implementations ICML 2017 Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi

The BFRY random variables are well approximated by gamma random variables in a variational Bayesian inference routine, which we apply to several network datasets for which power law degree distributions are a natural assumption.

Bayesian Inference

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

2 code implementations7 Nov 2016 Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine

We analyze the connection between Q-Prop and existing model-free algorithms, and use control variate theory to derive two variants of Q-Prop with conservative and aggressive adaptation.

Continuous Control Policy Gradient Methods +1

A study of the effect of JPG compression on adversarial images

no code implementations2 Aug 2016 Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel M. Roy

For Fast-Gradient-Sign perturbations of small magnitude, we found that JPG compression often reverses the drop in classification accuracy to a large extent, but not always.

Classification General Classification +1

Magnetic Hamiltonian Monte Carlo

no code implementations ICML 2017 Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

We establish a theoretical basis for the use of non-canonical Hamiltonian dynamics in MCMC, and construct a symplectic, leapfrog-like integrator allowing for the implementation of magnetic HMC.

The Mondrian Kernel

no code implementations16 Jun 2016 Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh

We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel.

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

15 code implementations NeurIPS 2016 Yarin Gal, Zoubin Ghahramani

Recent results at the intersection of Bayesian modelling and deep learning offer a Bayesian interpretation of common deep learning techniques such as dropout.

Bayesian Inference Language Modelling +2

Particle Gibbs for Infinite Hidden Markov Models

no code implementations NeurIPS 2015 Nilesh Tripuraneni, Shixiang (Shane) Gu, Hong Ge, Zoubin Ghahramani

Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric generalization of the classical Hidden Markov Model which can automatically infer the number of hidden states in the system.

Statistical Model Criticism using Kernel Two Sample Tests

no code implementations NeurIPS 2015 James R. Lloyd, Zoubin Ghahramani

We propose an exploratory approach to statistical model criticism using maximum mean discrepancy (MMD) two sample tests.

A General Framework for Constrained Bayesian Optimization using Information-based Search

1 code implementation30 Nov 2015 José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani

Of particular interest to us is to efficiently solve problems with decoupled constraints, in which subsets of the objective and constraint functions may be evaluated independently.

Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions

no code implementations NeurIPS 2015 Amar Shah, Zoubin Ghahramani

We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions.

Global Optimization

Sandwiching the marginal likelihood using bidirectional Monte Carlo

no code implementations8 Nov 2015 Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams

Using the ground truth log-ML estimates obtained from our method, we quantitatively evaluate a wide variety of existing ML estimators on several latent variable models: clustering, a low rank approximation, and a binary attributes model.

Latent Variable Models

Dirichlet Fragmentation Processes

no code implementations16 Sep 2015 Hong Ge, Yarin Gal, Zoubin Ghahramani

In this paper, first we review the theory of random fragmentation processes [Bertoin, 2006], and a number of existing methods for modelling trees, including the popular nested Chinese restaurant process (nCRP).

Scalable Discrete Sampling as a Multi-Armed Bandit Problem

no code implementations30 Jun 2015 Yutian Chen, Zoubin Ghahramani

Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods.

Bayesian Inference Multi-Armed Bandits

An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process

no code implementations26 Jun 2015 Amar Shah, David A. Knowles, Zoubin Ghahramani

Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets.

Topic Models Variational Inference

MCMC for Variationally Sparse Gaussian Processes

no code implementations NeurIPS 2015 James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani

This paper simultaneously addresses these, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form.

Gaussian Processes

Neural Adaptive Sequential Monte Carlo

no code implementations NeurIPS 2015 Shixiang Gu, Zoubin Ghahramani, Richard E. Turner

Experiments indicate that NASMC significantly improves inference in a non-linear state space model outperforming adaptive proposal methods including the Extended Kalman and Unscented Particle Filters.

Variational Inference

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

23 code implementations6 Jun 2015 Yarin Gal, Zoubin Ghahramani

In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.

Bayesian Inference Gaussian Processes +1

Dropout as a Bayesian Approximation: Appendix

1 code implementation6 Jun 2015 Yarin Gal, Zoubin Ghahramani

We show that a neural network with arbitrary depth and non-linearities, with dropout applied before every weight layer, is mathematically equivalent to an approximation to a well known Bayesian model.

Training generative neural networks via Maximum Mean Discrepancy optimization

no code implementations14 May 2015 Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani

We frame learning as an optimization minimizing a two-sample test statistic---informally speaking, a good generator network produces samples that cause a two-sample test to fail to reject the null hypothesis.

A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models

no code implementations3 May 2015 Nilesh Tripuraneni, Shane Gu, Hong Ge, Zoubin Ghahramani

Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric generalization of the classical Hidden Markov Model which can automatically infer the number of hidden states in the system.

On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes

no code implementations27 Apr 2015 Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani

We then discuss augmented index sets and show that, contrary to previous works, marginal consistency of augmentation is not enough to guarantee consistency of variational inference with the original model.

Variational Inference

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data

1 code implementation7 Mar 2015 Yarin Gal, Yutian Chen, Zoubin Ghahramani

Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data.

Gaussian Processes Imputation +1

Slice Sampling for Probabilistic Programming

no code implementations20 Jan 2015 Razvan Ranca, Zoubin Ghahramani

We introduce the first, general purpose, slice sampling inference engine for probabilistic programs.

Probabilistic Programming

General Table Completion using a Bayesian Nonparametric Model

no code implementations NeurIPS 2014 Isabel Valera, Zoubin Ghahramani

Even though heterogeneous databases can be found in a broad variety of applications, there exists a lack of tools for estimating missing data in such databases.

Scalable Variational Gaussian Process Classification

1 code implementation7 Nov 2014 James Hensman, Alex Matthews, Zoubin Ghahramani

Gaussian process classification is a popular method with a number of appealing properties.

Classification General Classification

Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs

no code implementations6 Nov 2014 Yutian Chen, Vikash Mansinghka, Zoubin Ghahramani

Probabilistic programming languages can simplify the development of machine learning techniques, but only if inference is sufficiently scalable.

Probabilistic Programming

Beta diffusion trees and hierarchical feature allocations

no code implementations14 Aug 2014 Creighton Heaukulani, David A. Knowles, Zoubin Ghahramani

We define the beta diffusion tree, a random tree structure with a set of leaves that defines a collection of overlapping subsets of objects, known as a feature allocation.

Warped Mixtures for Nonparametric Cluster Shapes

1 code implementation9 Aug 2014 Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters.

Density Estimation

Linear Dimensionality Reduction: Survey, Insights, and Generalizations

1 code implementation3 Jun 2014 John P. Cunningham, Zoubin Ghahramani

Modern techniques for optimization over matrix manifolds enable a generic linear dimensionality reduction solver, which accepts as input data and an objective to be optimized, and returns, as output, an optimal low-dimensional projection of the data.

Dimensionality Reduction Metric Learning

Classification using log Gaussian Cox processes

no code implementations16 May 2014 Alexander G. de G. Matthews, Zoubin Ghahramani

McCullagh and Yang (2006) suggest a family of classification algorithms based on Cox processes.

Classification General Classification

Avoiding pathologies in very deep networks

2 code implementations24 Feb 2014 David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani

Choosing appropriate architectures and regularization strategies for deep networks is crucial to good predictive performance.

Gaussian Processes

Student-t Processes as Alternatives to Gaussian Processes

no code implementations18 Feb 2014 Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani

We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions.

Gaussian Processes Model Selection

The Random Forest Kernel and other kernels for big data from random partitions

no code implementations18 Feb 2014 Alex Davies, Zoubin Ghahramani

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects.

Gaussian Processes

Gaussian Process Volatility Model

no code implementations NeurIPS 2014 Yue Wu, Jose Miguel Hernandez Lobato, Zoubin Ghahramani

A Gaussian Process (GP) defines a distribution over functions, which allows us to capture highly flexible functional relationships for the variances.

Gaussian Processes

Randomized Nonlinear Component Analysis

no code implementations1 Feb 2014 David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Ghahramani, Bernhard Schölkopf

Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

no code implementations26 Sep 2013 Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data.

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

no code implementations26 Sep 2013 Amar Shah, Zoubin Ghahramani

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled.

Bayesian Structured Prediction Using Gaussian Processes

1 code implementation15 Jul 2013 Sebastien Bratieres, Novi Quadrianto, Zoubin Ghahramani

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design.

Gaussian Processes Structured Prediction

Dynamic Covariance Models for Multivariate Financial Time Series

no code implementations18 May 2013 Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data.

Time Series

Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data

no code implementations12 Apr 2013 Richard S. Savage, Zoubin Ghahramani, Jim E. Griffin, Paul Kirk, David L. Wild

We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data.

Feature Selection

Scaling the Indian Buffet Process via Submodular Maximization

1 code implementation11 Apr 2013 Colorado Reed, Zoubin Ghahramani

Inference for latent feature models is inherently difficult as the inference space grows exponentially with the size of the input data and number of latent features.

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

3 code implementations20 Feb 2013 David Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani

Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art.

Time Series

A nonparametric variable clustering model

no code implementations NeurIPS 2012 Konstantina Palla, Zoubin Ghahramani, David A. Knowles

Factor analysis models effectively summarise the covariance structure of high dimensional data, but the solutions are typically hard to interpret.

Continuous Relaxations for Discrete Hamiltonian Monte Carlo

no code implementations NeurIPS 2012 Yichuan Zhang, Zoubin Ghahramani, Amos J. Storkey, Charles A. Sutton

Continuous relaxations play an important role in discrete optimization, but have not seen much use in approximate probabilistic inference.

Variable noise and dimensionality reduction for sparse Gaussian processes

no code implementations27 Jun 2012 Edward Snelson, Zoubin Ghahramani

A projection of the input space to a low dimensional space is learned in a supervised manner, alongside the pseudo-inputs, which now live in this reduced space.

Dimensionality Reduction Gaussian Processes

Warped Mixtures for Nonparametric Cluster Shapes

1 code implementation8 Jun 2012 Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters.

Density Estimation

Gaussian Process Regression Networks

1 code implementation19 Oct 2011 Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes.

Gaussian Processes

Generalised Wishart Processes

no code implementations31 Dec 2010 Andrew Gordon Wilson, Zoubin Ghahramani

We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP).

Copula Processes

no code implementations NeurIPS 2010 Andrew G. Wilson, Zoubin Ghahramani

We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions.

Bayesian Inference

Ranking relations using analogies in biological and information networks

no code implementations28 Dec 2009 Ricardo Silva, Katherine Heller, Zoubin Ghahramani, Edoardo M. Airoldi

Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$?

Information Retrieval Relational Reasoning

Bayesian Exponential Family PCA

no code implementations NeurIPS 2008 Shakir Mohamed, Zoubin Ghahramani, Katherine A. Heller

Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data.

Bayesian Inference Dimensionality Reduction

The Infinite Factorial Hidden Markov Model

no code implementations NeurIPS 2008 Jurgen V. Gael, Yee W. Teh, Zoubin Ghahramani

We introduces a new probability distribution over a potentially infinite number of binary Markov chains which we call the Markov Indian buffet process.

Sparse Gaussian processes using pseudo-inputs

no code implementations NeurIPS 2005 Edward Snelson, Zoubin Ghahramani

We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudo-input points, which we learn by a gradient based optimization.

Gaussian Processes

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