You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

no code implementations • 16 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.

no code implementations • 10 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.

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.

no code implementations • 21 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.

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.

1 code implementation • 7 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.

no code implementations • 7 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.

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.

no code implementations • ICLR 2019 • Tameem Adel, Cuong V. Nguyen, Richard E. Turner, Zoubin Ghahramani, Adrian Weller

We present a framework for interpretable continual learning (ICL).

no code implementations • 5 Dec 2018 • Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer, Lukas Pfeifenberger, Guenther Schindler, Holger Froening, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani

In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.

no code implementations • NeurIPS 2018 • Ruixiang Zhang, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song

In this paper, we propose a conceptually simple and general framework called MetaGAN for few-shot learning problems.

no code implementations • 1 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.

no code implementations • 24 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.

2 code implementations • 10 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.

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.

no code implementations • 1 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.

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.

no code implementations • 5 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.

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.

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.

no code implementations • 13 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.

no code implementations • 8 Feb 2018 • Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda

In this paper, we propose a simple but effective method for training neural networks with a limited amount of training data.

no code implementations • 8 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.

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.

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).

no code implementations • 26 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.

no code implementations • 19 Jul 2017 • Tomoharu Iwata, Zoubin Ghahramani

We propose a simple method that combines neural networks and Gaussian processes.

no code implementations • 18 Jul 2017 • Jordan Burgess, James Robert Lloyd, Zoubin Ghahramani

We consider the task of one-shot learning of visual categories.

no code implementations • 8 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.

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.

1 code implementation • 12 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.

no code implementations • NeurIPS 2017 • Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine

Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques.

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.

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.

2 code implementations • 7 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.

1 code implementation • 27 Oct 2016 • Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman

GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end.

no code implementations • 2 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.

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.

no code implementations • 16 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.

no code implementations • NeurIPS 2016 • Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-Chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani

Tensor factorization is a powerful tool to analyse multi-way data.

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.

Ranked #34 on Language Modelling on Penn Treebank (Word Level)

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.

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.

1 code implementation • 30 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.

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.

no code implementations • 8 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.

no code implementations • 16 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).

no code implementations • 30 Jun 2015 • Yutian Chen, Zoubin Ghahramani

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

no code implementations • 26 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.

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.

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.

23 code implementations • 6 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.

1 code implementation • 6 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.

2 code implementations • 6 Jun 2015 • Yarin Gal, Zoubin Ghahramani

Convolutional neural networks (CNNs) work well on large datasets.

no code implementations • 14 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.

no code implementations • 3 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.

no code implementations • 27 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.

1 code implementation • 7 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.

1 code implementation • 18 Feb 2015 • José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani

Unknown constraints arise in many types of expensive black-box optimization problems.

no code implementations • 20 Jan 2015 • Razvan Ranca, Zoubin Ghahramani

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

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.

1 code implementation • 7 Nov 2014 • James Hensman, Alex Matthews, Zoubin Ghahramani

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

no code implementations • 6 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.

no code implementations • 14 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.

1 code implementation • 9 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.

1 code implementation • NeurIPS 2014 • José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES).

1 code implementation • 3 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.

no code implementations • 16 May 2014 • Alexander G. de G. Matthews, Zoubin Ghahramani

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

no code implementations • 17 Mar 2014 • Konstantina Palla, David A. Knowles, Zoubin Ghahramani

We present a nonparametric prior over reversible Markov chains.

2 code implementations • 24 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.

no code implementations • 18 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.

2 code implementations • 18 Feb 2014 • James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani

This paper presents the beginnings of an automatic statistician, focusing on regression problems.

no code implementations • 18 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.

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.

no code implementations • 1 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.

no code implementations • 26 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.

no code implementations • 26 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.

1 code implementation • 15 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.

no code implementations • 18 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.

no code implementations • 12 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.

1 code implementation • 11 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.

no code implementations • 13 Mar 2013 • Konstantina Palla, David A. Knowles, Zoubin Ghahramani

The fundamental aim of clustering algorithms is to partition data points.

3 code implementations • 20 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.

no code implementations • NeurIPS 2012 • Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, Jose M. Hernández-Lobato

We present a new model based on Gaussian processes (GPs) for learning pairwise preferences expressed by multiple users.

no code implementations • NeurIPS 2012 • Michael Osborne, Roman Garnett, Zoubin Ghahramani, David K. Duvenaud, Stephen J. Roberts, Carl E. Rasmussen

Numerical integration is an key component of many problems in scientific computing, statistical modelling, and machine learning.

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.

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.

no code implementations • 27 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.

1 code implementation • 8 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.

1 code implementation • 24 Dec 2011 • Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, Máté Lengyel

Information theoretic active learning has been widely studied for probabilistic models.

no code implementations • NeurIPS 2011 • Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths

How do people determine which elements of a set are most representative of that set?

1 code implementation • 19 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.

no code implementations • 31 Dec 2010 • Andrew Gordon Wilson, Zoubin Ghahramani

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

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.

no code implementations • NeurIPS 2010 • Zoubin Ghahramani, Michael. I. Jordan, Ryan P. Adams

Many data are naturally modeled by an unobserved hierarchical structure.

no code implementations • 28 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 $\mathbf{S}$?

no code implementations • NeurIPS 2009 • Finale Doshi-Velez, Shakir Mohamed, Zoubin Ghahramani, David A. Knowles

Nonparametric Bayesian models provide a framework for flexible probabilistic modelling of complex datasets.

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.

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.

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.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.