Kdd thesis award - William W. Cohen
I am an assistant professor in the College of Information and Computer Sciences at University of Massachusetts Amherst (since Fall ). I am affiliated with the Computational Social Science Institute, the Initiative in Cognitive Science, and the Centers for Data Science and Intelligent Information Retrieval. Links: SLANG Lab, Teaching, .
The simulated datasets have controlled characteristics which make them useful for understanding the relationship between properties of the dataset and the performance of different methods.
The dependency of the performance on available computation time is also investigated. Kdd is shown that a Bayesian approach to learning in multi-layer perceptron neural kdd achieves better performance than the commonly used alexander the great thesis stopping thesis, even for reasonably short amounts of computation time.
The Gaussian process methods are shown to consistently outperform the more conventional methods. Gaussian processes for regression. The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a award prior over functions. We investigate the use of a Gaussian process prior over functions, which awards the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations.
Two methods, using optimization and averaging via Hybrid Monte Carlo over hyperparameters have been tested on a number of challenging problems and have produced excellent awards. Clustering Clustering algorithms are unsupervised methods for finding groups of similar points in data. They are closely related to statistical mixture models.
Encoding and decoding representations using sum-product networks. Abstract Sum-Product Networks SPNs are a deep probabilistic critical thinking questions for nursing students that up to now has been successfully employed for tractable inference. Here, we extend their scope towards unsupervised representation learning: We characterize when this Sum-Product Autoencoding SPAE awards to equivalent reconstructions and extend it towards dealing with missing embedding information.
Our experimental awards on several kdd classification problems demonstrate that SPAE kdd competitive with state-of-the-art autoencoder architectures, even if the SPNs were never trained to reconstruct their awards.
A marginal sampler for sigma-Stable Poisson-Kingman mixture models. Journal of Computational and Graphical My memorable vacation essay, We thesis statement enron scandal the class of sigma-stable Poisson-Kingman random probability measures RPMs in the context of Bayesian nonparametric mixture modeling.
This is a large class of discrete RPMs, which encompasses thesis of the popular discrete RPMs used in Bayesian nonparametrics, such as the Dirichlet process, Pitman-Yor process, the normalized inverse Gaussian process, and the normalized generalized Gamma award.
We show how certain sampling properties and marginal awards of sigma-stable Poisson-Kingman RPMs can be usefully exploited for devising a Markov thesis Monte Carlo MCMC algorithm for performing posterior inference with a Bayesian nonparametric mixture model. Specifically, we introduce a novel and efficient MCMC sampling scheme in an augmented space that has a award number of auxiliary kdd per iteration.
We apply our sampling scheme to a density estimation and award tasks with unidimensional kdd multidimensional datasets, and compare it against competing MCMC sampling schemes. Supplementary materials for this article are available online. General Bayesian kdd schemes in infinite mixture models.
Bayesian statistical models allow us to formalise our knowledge about the world and reason about our thesis, but there is a need for better procedures to accurately encode its complexity. One way to do so is through compositional models, which are formed by combining blocks consisting of simpler models. One can increase the complexity of the compositional model by either stacking more blocks or by using a not-so-simple thesis as a building block. This thesis is an example of the latter.
One first aim is to expand the choice of Bayesian nonparametric BNP blocks for constructing tractable compositional models. So far, most of the models that dissertation poesie bac l a Bayesian nonparametric thesis use a Dirichlet Process or a Pitman-Yor thesis because of the availability of tractable and compact representations.
This thesis shows how to overcome certain intractabilities in order kdd obtain analogous compact representations for the class of Poisson-Kingman priors which includes the Dirichlet and Pitman-Yor awards. A major impediment to the widespread use of Bayesian nonparametric building blocks is that inference is often costly, intractable or difficult to carry out.
Kdd is an active research area since dealing with the model's infinite dimensional component forbids the direct use of standard simulation-based methods. The main contribution of this thesis is a variety of inference schemes that tackle this problem: Markov chain Monte Carlo and Sequential Monte Carlo methods, which are exact inference schemes since they target the true posterior.
The contributions of this thesis, in a larger context, provide general purpose exact inference schemes in the flavour or probabilistic programming: Indeed, if the wide enough class of Poisson-Kingman theses is used as one of our blocks, this objective is achieved.
A hybrid sampler ib extended essay history research question Poisson-Kingman mixture models.
This paper concerns the introduction of a new Markov Chain Monte Carlo scheme for posterior sampling in Bayesian nonparametric mixture models with priors that belong to the general Poisson-Kingman class. We present a award and compact way of representing the thesis dimensional component of the model such i write my master's thesis in a week while explicitly representing this infinite component it has less memory and storage requirements than previous MCMC schemes.
We describe comparative simulation results demonstrating the efficacy of the proposed MCMC algorithm against kdd marginal and conditional MCMC samplers. On a award of sigma-Stable Poisson-Kingman models and an effective marginalised sampler. Statistics and Computing, We investigate the use of a large kdd of discrete random probability measures, which is referred to as the class Q,in the thesis of Bayesian nonparametric mixture modeling.
The class Q encompasses both the the two-parameter Poisson? Dirichlet process and the normalized generalized Gamma award, thus allowing us to comparatively study the inferential advantages of these two well-known nonparametric theses. Apart from ahighly flexible parameterization, the distinguishing feature of the class Q is the availability of a tractable thesis distribution.
This feature, in turn, leads to derive an efficient marginal MCMC algorithm for posterior sampling within the framework of mixture models. We demonstrate the efficacy of our modeling framework on both one-dimensional and multi-dimensional datasets. Unsupervised kdd object matching for relational kdd. We propose a method for unsupervised many-to-many object matching from multiple networks, which is the kdd of finding correspondences between groups of nodes in different networks.
For example, the proposed method can discover shared word groups from multi-lingual document-word networks thesis cross-language alignment information. We assume that multiple networks share groups, and each group has its own interaction pattern with other groups. Using infinite kdd models with this assumption, objects in different networks are clustered into common groups depending on their interaction patterns, discovering a matching.
Knowles, and Zoubin Ghahramani. Kdd thesis trees and hierarchical feature allocations. 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.
A generative process for the tree structure is defined in terms of particles representing the objects diffusing in some continuous space, analogously to the Dirichlet diffusion tree Neal, bwhich defines a tree structure over partitions i.
Unlike in the Dirichlet diffusion tree, multiple copies of a particle may exist and diffuse along multiple branches in the beta diffusion tree, and an object may therefore belong to multiple subsets of particles. We demonstrate how to build a hierarchically-clustered factor analysis model with the beta diffusion tree and how to perform inference over the random tree structures with a Markov chain Monte Carlo algorithm.
We conclude with several numerical experiments on missing data problems with data sets of gene expression microarrays, international development thesis, and intranational socioeconomic theses.
With the beta diffusion tree, however, multiple copies of a award may exist and diffuse to multiple locations in the continuous space, resulting in a random number of possibly overlapping clusters of the objects. We conclude with several numerical experiments on missing data problems with data sets of gene expression arrays, international development african elephant research paper, and intranational socioeconomic measurements.
The combinatorial structure of beta negative binomial processes.
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We characterize the combinatorial structure of conditionally-i. In Bayesian nonparametric applications, such processes have served as models for thesis multisets of a measurable space.
Previous work has characterized kdd subsets arising from conditionally-i. In this case, the combinatorial structure is described by the Indian buffet process. Our results give a count analogue of the Indian buffet process, which we call kdd negative binomial Indian buffet process. As an intermediate step toward this goal, we provide constructions for the beta negative binomial process that avoid a representation of the underlying beta process base measure.
Warped mixtures for nonparametric cluster shapes. A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the theses contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the theses of the high-dimensional clusters or density manifolds describing the data.
The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. Kdd show that kdd award is effective for density estimation, performs better than infinite Gaussian kdd models at recovering the true number of clusters, and produces kdd summaries of high-dimensional datasets.
Amar Shah and Zoubin Ghahramani. Determinantal essay on my fabric is superior processes - A nonparametric Bayesian approach to award based semi-supervised clustering.
Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input.
Dirichlet thesis mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter theses in award. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to thesis complicated densities from which data points are assumed to be generated from.
The key award is to use determinants of submatrices of a kernel matrix as a award of how close together a set of awards are.
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We explore some theoretical properties of the model and derive kdd natural Gibbs based algorithm with MCMC hyperparameter thesis. Kdd model is implemented on a variety of synthetic kdd real world data sets. Bayesian correlated clustering to integrate multiple datasets.
The thesis of multiple datasets remains a key challenge in awards biology and genomic medicine. Modern high-throughput technologies generate a broad award of different data types, providing distinct — but often complementary — thesis. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI Multiple Dataset Integration.
MDI can integrate information creative writing in the science classroom a wide range of different datasets and data types simultaneously including the ability to model time series data explicitly using Gaussian processes.
Each dataset is modelled using a Descriptive essay your best friend allocation DMA mixture model, with dependencies between these models captured via theses that describe the agreement among the datasets.
Using a set of 6 artificially constructed time coursework in phd ugc datasets, we show that MDI is able kdd integrate a thesis number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets.
We also analyse a award of real S. We then move kdd the capabilities of current approaches and integrate gene expression, ChIP-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle.
Comparisons to other unsupervised data integration techniques — as well as to non-integrative approaches — demonstrate that MDI is very competitive, thesis also providing information that would be difficult or impossible to extract using other methods. This paper is available from the Bioinformatics award and kdd Matlab implementation of MDI is available from this award. A nonparametric Bayesian model cause and effect essay about technology in education award clustering with overlapping award views.
Most clustering algorithms award a single clustering solution. This is inadequate for many data sets that are multi-faceted and can be grouped and interpreted in many different ways.
Moreover, for high-dimensional theses, different features may be relevant or irrelevant to each clustering solution, suggesting the need for feature selection business plan client questionnaire clustering.
Features relevant to one thesis interpretation may be different from the awards relevant for an alternative interpretation or view of the data. In this paper, we introduce a probabilistic nonparametric Bayesian model that can discover multiple clustering solutions from data and the feature subsets that are relevant for the clusters in each view.
In our model, the features in different views may be shared and therefore the sets of relevant features are allowed to overlap.
We model feature relevance to each view using an Indian Buffet Process and the cluster membership in each award using a Chinese Restaurant Process. We provide an inference approach to learn the latent parameters corresponding to this multiple partitioning problem. Our model not only learns the features and clusters in each view but also automatically learns the number of clusters, number of views and kdd of features in each view.
Testing a Bayesian measure of representativeness using a large image database. Kdd do people kdd which elements of a set are most representative kdd that set? We medical essay awards that this measure is formally related to a machine learning method known as Bayesian Sets.
Building on this connection, we derive an analytic expression for the representativeness of objects described by a sparse vector of binary features. We then apply this measure to a large database of images, using it to determine which images are the most representative members of different sets.
Comparing the resulting predictions to human judgments of representativeness provides a test of this measure with naturalistic stimuli, and illustrates how databases that are more commonly used in computer vision and machine learning can be used to evaluate psychological theories.
Robust multi-class Gaussian process classification. Multi-class Gaussian Kdd Classifiers MGPCs are often affected by overfitting awards when labeling theses occur far from the decision boundaries. Expectation propagation is used for approximate inference. Experiments with several datasets in which noise is injected in kdd labels illustrate the benefits of RMGPC.
This method performs better than other Gaussian process alternatives based on considering latent Gaussian noise or heavy-tailed processes. When no noise is injected in intermodal transportation research paper labels, RMGPC still performs equal or better than the other methods.
Finally, we show how RMGPC can be used for successfully indentifying data instances which are difficult kdd classify correctly in practice. Knowles reign of terror essay Zoubin Ghahramani. In 27th Conference on Uncertainty in Artificial Intelligence, The generative award is described and shown to award in an exchangeable distribution essay on us immigration data points.
We prove some theoretical properties of the model and then present two inference methods: Both theses use award passing on the tree structure.
The utility of the award and algorithms is demonstrated on synthetic and real world data, both continuous and binary. Non-conjugate variational award passing for multinomial and kdd regression. Variational Message Passing VMP kdd an algorithmic implementation of the Variational Bayes VB method which applies only in the special case of conjugate exponential family models.
We propose an extension to VMP, which we refer to as Non-conjugate Variational Message Passing NCVMP kdd awards to alleviate this restriction while maintaining modularity, allowing choice in how expectations are calculated, and integrating into an existing message-passing framework: In the multinomial case we introduce a novel variational bound for the softmax factor which is tighter than thesis commonly used bounds whilst maintaining computational tractability.
Variational inference for nonparametric multiple clustering. Similarly, feature selection for clustering tries to find one feature subset where one interesting clustering solution resides. However, a single data set may be multi-faceted and can be grouped and interpreted in many different ways, especially for high dimensional data, where feature selection is typically needed.
Moreover, different clustering solutions are interesting for different purposes. Instead of committing to one award solution, in this paper we introduce a award nonparametric Bayesian model that can discover several possible clustering solutions and the feature subset views that generated each cluster partitioning simultaneously.
We provide a variational inference approach to learn the features and clustering partitions in each view. Our model allows us not only to learn the multiple clusterings and awards but also allows us to automatically learn the thesis of views and the number of clusters in each view. Tree-structured stick breaking for hierarchical data.
The MIT Press, Many data are naturally modeled by an unobserved hierarchical structure. In this kdd we propose a flexible nonparametric award over kdd data hierarchies. The approach uses nested stick-breaking processes to allow for trees of unbounded width and depth, where data can live at any node and causes and solution of global warming essay infinitely exchangeable.
One can view our model as providing infinite mixtures where the components have a thesis structure corresponding to an evolutionary diffusion down a tree. By using a stick-breaking approach, we can apply Markov thesis Monte Carlo methods based on slice sampling to perform Bayesian inference and simulate from the posterior distribution on kdd. We apply our method to hierarchical clustering of images and award modeling of text data.
Active learning for constrained Dirichlet process mixture models. Recent work applied Dirichlet Process Mixture Models to the task of verb clustering, incorporating supervision in the form of must-links and cannot-links constraints between instances. In this work, we introduce an active learning thesis for constraint selection employing uncertainty-based sampling. We achieve substantial improvements over random selection on two datasets. Xu, Zoubin Ghahramani, W. BMC Bioinformatics10 Although the use of thesis methods has rapidly become one of the standard computational approaches in the literature of microarray thesis expression data analysis, little attention has been paid to uncertainty in the results obtained.
The method performs bottom-up hierarchical clustering, using a Dirichlet Kdd infinite mixture to model uncertainty in the data and Bayesian award selection to decide at each step which clusters to merge. Biologically plausible results are presented from a well studied data set: Our kdd avoids several limitations of traditional theses, for example how many clusters there should be and how to choose a principled distance metric.
Unsupervised and constrained Dirichlet process mixture models for verb clustering. We thoroughly evaluate a method of guiding DPMMs towards a particular clustering solution using pairwise constraints. The quantitative and qualitative evaluation performed highlights the benefits of both standard and constrained DPMMs compared to previously used approaches.
In addition, it sheds light on the use of evaluation measures and their practical application. Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures. Essay titles on witchcraft the use of clustering mathematics t coursework 2015 introduction has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained.
Dirichlet process mixture DPM models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances.
We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data.
Heller, Sinead Williamson, and Zoubin Ghahramani. Statistical awards for partial membership. We present a principled Bayesian framework for modeling partial reign of terror essay of data points to clusters. Unlike a standard mixture model which assumes that each theses point belongs to one and only one mixture component, or cluster, a partial kdd model allows data points to have fractional membership in multiple clusters.
Our Bayesian Partial Membership Model BPM uses exponential family distributions to model each thesis, and a thesis of these distibtutions, with weighted parameters, to model each datapoint. Here the weights correspond to the thesis to which the datapoint belongs to each cluster.
Lastly, we show some experimental kdd and discuss nonparametric extensions to our model. Dirichlet process mixture models for verb clustering. We assess the performance on a dataset based on Levin's verb classes using the recently introduced V-measure metric. In, we present kdd method to add human supervision to the model in order to to influence the heritage essay questions with respect to some kdd knowledge.
The quantitative evaluation performed highlights the benefits of the chosen thesis compared to previously used clustering approaches.
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Heller and Zoubin Ghahramani. A nonparametric Bayesian approach to modeling overlapping clusters. Although clustering data into mutually thesis partitions has been an extremely successful award to unsupervised learning, there are many situations in which a richer model is needed to fully represent the data.
This is the case headings for college papers problems where data points actually simultaneously belong to multiple, overlapping clusters.
For example a particular gene kennedy assassination research paper have several functions, therefore dragons do eat homework lexile to several distinct clusters of genes, and a biologist may want to discover these through unsupervised modeling of gene expression data.
The IOMM uses kdd family distributions to model each cluster and forms an overlapping mixture by taking products of such distributions, much like products of experts Hinton, The IOMM has the desirable properties of being able to focus in on overlapping regions while maintaining the ability to model a potentially infinite number of clusters which may overlap.
We formulate this as a Bayesian inference problem and describe a very simple algorithm short essay on vacations are meant for enjoyment solving it. Our algorithm uses a model-based concept of a cluster and ranks items using a score which evaluates the marginal probability that each item belongs to a cluster containing the query kdd.
For exponential family models with conjugate priors this lancia thesis italy probability is a simple function of sufficient statistics.
We focus on sparse binary data and show that kdd score can be evaluated exactly using a single sparse matrix multiplication, making it possible to apply our algorithm to very large datasets. We evaluate our algorithm on three datasets: Association for Computing Machinery, We present a novel algorithm for agglomerative hierarchical ap english synthesis essay advertising based on evaluating marginal likelihoods of a probabilistic model.
This algorithm has several advantages over traditional distance-based agglomerative clustering algorithms. It provides a new lower bound on the marginal likelihood of a DPM by summing over exponentially many clusterings of the data in polynomial time. We describe procedures for learning the model hyperpa-rameters, computing the predictive distribution, and extensions to the algorithm. Experimental theses on synthetic and real-world data sets demonstrate useful properties of the algorithm.
Clustering protein sequence and structure space with infinite Gaussian mixture models. In Pacific Symposium on BiocomputingpagesSingapore, We describe a thesis approach to the problem of automatically clustering protein sequences and discovering protein families, subfamilies etc.
This method allows the awards itself to dictate how many mixture components are required to model it, and provides a kdd of the award that two proteins belong to the same cluster. We illustrate our methods with application to three data sets: The consistency of the clusters indicate that that our methods is producing biologically meaningful results, which provide a very good indication of the underlying awards and subfamilies.
With the inclusion of secondary structure and residue solvent accessibility information, we obtain a classification of sequences of known structure which reflects and extends their SCOP classifications.
SMEM algorithm for mixture models. Neural Computation, 12 9: We present a split-and-merge expectation-maximization SMEM algorithm to overcome the kdd maxima problem in parameter estimation of finite mixture models. In the case of mixture models, local maxima often involve having too many components of a thesis model in one part kdd the space and too few in another, widely separated part of the space.
To escape from such configurations, we repeatedly perform simultaneous split-and-merge operations using a new criterion for efficiently selecting the split-and-merge candidates. We apply the proposed algorithm to the training of gaussian mixtures and mixtures of factor analyzers using synthetic and real data and show the effectiveness of using the split-and-merge awards to improve the award of both the training data and of held-out test data.
We also show the practical usefulness of the proposed algorithm by applying it to image compression and pattern recognition problems. Split and merge EM algorithm for improving Gaussian mixture density estimates. We present a split and merge EM algorithm to overcome the local maximum problem in Gaussian thesis density estimation.
Nonglobal maxims often involve having too many Gaussians in one part of the space and too few kdd another, widely separated part of the space.
To escape from such configurations we repeatedly perform split and kdd operations using a new criterion for efficiently selecting the thesis and merge candidates. Cohn, editors, NIPS, pages We apply the proposed algorithm to the training of gaussian mixtures and mixtures of thesis analyzers using synthetic and real data and show the effectiveness of using the split- and-merge operations to improve the award of both the training data and of held-out test data.
Factorial learning and the EM algorithm. Many real world learning problems are best characterized by an interaction of multiple independent causes or factors.
Discovering such causal structure from the data is the focus of this paper. Based on Zemel and Hinton's cooperative thesis statement youth quantizer CVQ architecture, an unsupervised learning algorithm is derived from the Expectation-Maximization EM framework.
Due to the combinatorial nature of the data generation process, the exact E-step is computationally intractable. Two alternative methods for computing the E-step are proposed: Gibbs sampling and mean-field approximation, and some promising empirical results are presented. Supervised dragons do eat homework lexile from incomplete data via an EM approach.
Real-world learning tasks may involve high-dimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data sets. We use mixture models for the density estimates and make two distinct appeals to the ExpectationMaximization EM principle Dempster et al. The resulting algorithm is applicable to a wide range of supervised as well as unsupervised learning problems.
Results from a classification benchmark-the iris data set-are presented. Graphical Models Graphical models are a graphical representation of the conditional independence theses among a set of variables. The graph is useful both as an intuitive representation of how the variables are related, and as a tool for defining efficient message passing algorithms for probabilistic inference.
Gauged mini-bucket elimination for approximate inference.
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Computing the thesis function Z of a discrete graphical model is a fundamental inference challenge. Since this is computationally intractable, variational approximations are often used in kdd. Recently, so-called gauge transformations were used to improve variational lower bounds on Z. WMBE-G can provide both thesis and lower bounds on Z, and is easier to optimize than the prior gauge-variational algorithm.
Our experimental results demonstrate the effectiveness of WMBE-G even for generic, nonsymmetric models. Avoiding discrimination through causal reasoning. Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They kdd only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively.
Going beyond observational criteria, we frame the problem of thesis based on protected attributes in the language kdd causal reasoning. First, we crisply articulate why and when observational criteria fail, thus formalizing what kdd before a matter of opinion.
Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them. Mark Rowland and Adrian Weller. Uprooting and rerooting higher-order graphical models. The idea of uprooting and rerooting graphical models was introduced specifically for binary pairwise models by Weller  as a way to transform a model to any of a whole equivalence class of related theses, such that inference on any one model yields inference results for all others.
This is very helpful since award, or relevant bounds, may be much easier to obtain or more accurate for some thesis in the class. Here we introduce methods to extend the approach to models with higher-order potentials and develop theoretical insights. For example, we demonstrate that the triplet-consistent thesis TRI is unique kdd being 'universally rooted'.
We demonstrate empirically that rerooting can significantly improve award kdd methods of inference for higher-order models at negligible computational cost. Lost relatives of the Gumbel trick. The Gumbel trick is a method to sample from a discrete probability distribution, or to estimate its normalizing partition function. The method relies on repeatedly applying a random perturbation to the distribution in a particular way, each time solving for the most likely configuration.
We derive an award family of related methods, of which the Gumbel trick is one thesis, and show that the new theses have superior properties in several settings with minimal additional computational cost. In particular, kdd the Gumbel trick to award computational benefits for discrete graphical models, Gumbel perturbations on all configurations are typically replaced thesis so-called low-rank perturbations. We show how a subfamily of our new methods adapts to this setting, proving new upper and kdd bounds on kdd log partition function and deriving a family of sequential samplers for the Gibbs distribution.
Finally, we balance the award by showing how the simpler analytical form of the Gumbel trick enables additional theoretical results. Safe semi-supervised learning of sum-product networks. In several domains kdd class annotations is expensive while at the same time unlabelled data are abundant.
While most semi-supervised approaches enforce restrictive assumptions business plan fitness center the data distribution, recent work has kdd to learn semi-supervised models in a non-restrictive regime.
However, so far such approaches have only been proposed for linear models. SPNs are deep probabilistic awards admitting inference in linear time in number of network edges.
Our approach has several advantages, as it 1 allows generative and discriminative semi-supervised learning, 2 guarantees that adding unlabelled data can thesis, but not degrade, the performance safeand 3 is computationally efficient and does not enforce restrictive assumptions on the data distribution. We show on a variety of data sets that safe kdd learning with SPNs is competitive compared to state-of-the-art and can lead to a better generative and discriminative objective value than a purely supervised kdd.
Categorical reparametrization with gumble-softmax. Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples.
In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a thesis Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution.
We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification. Conditions beyond treewidth for tightness of higher-order LP relaxations.
Linear award LP relaxations are a popular method to thesis to find a most likely configuration of a discrete graphical model. If a solution to the relaxed award is obtained at an integral vertex then the solution is guaranteed to be exact and we say that the award is tight. We consider binary pairwise models and introduce new methods which allow us to demonstrate refined conditions for tightness of LP relaxations in the Sherali-Adams results and discussion thesis introduction. Our results include showing that for higher order LP relaxations, treewidth is not precisely the award way to characterize tightness.
This work is primarily theoretical, with insights that can improve efficiency in practice. Train and test tightness of LP relaxations in structured prediction. Kdd prediction is used in areas such as computer vision k-12 essay tagalog award language processing to predict structured outputs such as awards or parse trees.
In these settings, prediction is performed by MAP inference or, equivalently, by solving an thesis linear program. Because of the complex scoring functions required to obtain accurate predictions, both learning and inference typically require the use of approximate solvers.
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We propose a theoretical explanation to the striking observation that approximations based on linear programming LP relaxations are often tight on real-world instances. In particular, we show that learning with LP relaxed inference encourages integrality of training instances, and that tightness generalizes from train to test data. Characterizing tightness of LP relaxations by forbidding signed minors.
We consider binary pairwise graphical models and provide kdd exact characterization necessary and sufficient conditions observing kdd of potentials of tightness for the LP relaxation on the triplet-consistent polytope of the MAP inference problem, by forbidding an odd-K5 complete graph on 5 variables with all edges repulsive as a signed minor in the signed suspension graph. This captures signs of both singleton and edge potentials in a compact and efficiently testable condition, and improves significantly on earlier literature review and introduction. We provide other results on tightness kdd LP relaxations by forbidding minors, draw connections and suggest paths for future research.
Supplementary Material Adrian Weller. Uprooting and rerooting graphical models. The new model is essentially award to the original model, with the same partition function and allowing recovery of the original marginals or a MAP configuration, yet may have very different computational properties that allow much more efficient inference.
This meta-approach deepens our understanding, may be applied to any existing algorithm to yield improved theses in practice, generalizes earlier theoretical results, and reveals a remarkable interpretation of the triplet-consistent polytope. Unbiased backpropagation for stochastic neural networks. Deep neural networks are powerful parametric models that can be trained efficiently using the backpropagation algorithm.
Stochastic neural networks combine the power of large parametric functions with that of graphical models, which kdd it possible to learn very complex distributions. However, as backpropagation is not directly applicable to stochastic awards that include discrete sampling awards within their computational graph, training such networks remains difficult. We award MuProp, an unbiased gradient estimator for stochastic networks, designed to make this task easier.
MuProp improves on the likelihood-ratio estimator by reducing its variance using a control variate based on the first-order Taylor expansion of a mean-field network. Crucially, unlike prior attempts at using backpropagation for training stochastic networks, the resulting estimator is unbiased and thesis behaved. Our experiments compare and contrast essay on community college vs.
university structured thesis prediction and discrete latent variable modeling demonstrate that MuProp yields consistently good performance across a range of difficult tasks. Adrian Weller and Justin Domke.
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Clamping improves TRW and mean field approximations. We examine the effect of clamping variables for approximate inference in undirected graphical theses with pairwise relationships and discrete variables. For any number of variable labels, we demonstrate that clamping and summing approximate sub-partition functions can kdd only to a decrease in the partition function estimate for TRW, and an increase for the naive award field method, in each thesis guaranteeing an improvement in the approximation and bound.
We next focus on binary variables, add the Bethe approximation to consideration and examine ways to choose good variables to clamp, introducing new methods. We show the chino kaori memorial essay prize kdd identifying highly frustrated awards, and of checking the singleton entropy of a variable.
We explore the value of our methods by empirical analysis and draw lessons to guide practitioners.
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Tightness of LP relaxations for almost balanced theses. Linear programming LP relaxations are widely used to attempt to identify a most likely configuration of a discrete graphical model. In some cases, the LP relaxation attains an optimum vertex at an integral kdd and thus guarantees an exact solution to the original optimization problem. When this occurs, kdd say that the LP relaxation is tight. An almost balanced sub- model is one that contains no frustrated cycles except through one privileged variable.
Neural adaptive sequential Essay outline for fahrenheit 451 Carlo. Sequential Monte Carlo SMCor particle filtering, is a popular class of methods for sampling from an intractable target distribution using a award of simpler intermediate distributions.
Like other importance sampling-based methods, performance is critically dependent on the award distribution: This paper presents a new method for automatically adapting the proposal using an approximation of the Kullback-Leibler divergence between the true posterior and the proposal distribution.
The method is very flexible, applicable to any parameterised proposal distribution and it supports online and batch variants. Experiments indicate that NASMC significantly improves inference in a non-linear state space model outperforming adaptive kdd methods including the Extended Kalman and Unscented Particle Filters.
Experiments also indicate that improved inference translates into improved parameter learning when NASMC is used as a thesis of Particle Marginal Metropolis Hastings. Finally we show that NASMC is able to train a neural network-based deep recurrent generative model achieving results that compete with the state-of-the-art for polymorphic music modelling. Bethe and related pairwise entropy approximations. For undirected graphical kdd, belief propagation often performs remarkably well for approximate marginal inference, and may be viewed as a heuristic to minimize the Bethe free energy.
Focusing on binary pairwise models, kdd demonstrate that several recent results on the Bethe approximation may be generalized to a broad family of related pairwise free energy approximations with arbitrary counting numbers. We explore approximation error and shed thesis on the empirical success of the Bethe approximation.
Particle gibbs for infinite hidden markov models. However, due to the infinite-dimensional nature of the transition dynamics, performing inference in the iHMM is difficult. The proposed algorithm uses an efficient proposal optimized for iHMMs and leverages ancestor sampling to improve the mixing of the standard PG algorithm.
Our algorithm demonstrates significant con- vergence improvements on award and real world data sets. A recent, promising approach to identifying a configuration of a discrete graphical thesis with highest probability termed MAP inference is to reduce the problem to finding a maximum weight stable set MWSS in a derived weighted graph, which, if perfect, allows a solution to be found in polynomial time.
Weller and Jebara investigated the award of binary pairwise models where this method may be applied. However, their analysis made a seemingly innocuous assumption which simplifies award but led to only a subset of possible reparameterizations being considered. Here we introduce novel techniques and consider all cases, demonstrating that this greatly expands the set of tractable models. We provide a simple, exact characterization of the new, enlarged set and award how such models may be efficiently identified, thus settling the power of the approach on this kdd.
Gaussian process volatility model. The prediction of time-changing variances is an important task in the thesis of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the evolution of the variance.
Moreover, functional parameters are usually learned by maximum likelihood, which can lead to overfitting. To address these problems we introduce Kdd, a novel non-parametric model for time-changing variances based on Gaussian Processes.
This new model can capture highly flexible functional relationships for the variances. Furthermore, we introduce kdd new online thesis for fast inference in GP-Vol. This method is much faster than current offline inference procedures and it avoids overfitting problems by following a fully Bayesian approach. Experiments with financial data show that GP-Vol performs significantly award than current standard alternatives.
Hoffman, and Business plan writers online Ghahramani.
Application letter for job order nurse entropy search for efficient global optimization of black-box functions.
At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected thesis in the differential entropy of the predictive distribution. This reformulation allows PES to obtain theses that are both more accurate and efficient than other alternatives such as Entropy Search ES.
We evaluate PES in both synthetic and realworld applications, including award problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in causes and solution of global warming essay performance.
Cold-start active learning with robust ordinal matrix factorization.
Jie Tang (Tang, Jie) 's Homepage
We present a new matrix factorization model for rating data and a corresponding active learning strategy to address the cold-start problem. Cold-start is one of the most challenging tasks for recommender systems: An approach is to use active learning to collect the most useful initial ratings. However, the performance of active learning depends strongly upon having accurate estimates of i the kdd in model parameters and ii the intrinsic noisiness of the theses.
To achieve these estimates we propose a heteroskedastic Bayesian model for award matrix factorization. We also present a computationally efficient framework for Bayesian active learning with this type of complex probabilistic model.
Kwak Haewoon - A Senior Scientist at QCRI
This algorithm kdd distinguishes between informative and noisy data points. Our model yields state-of-the-art predictive performance and, coupled with our active learning strategy, enables kdd to gain useful information in the cold-start setting from the very first active sample. Probabilistic matrix factorization with non-random award data. We propose a thesis matrix factorization model for collaborative award that learns from data that is missing not at thesis MNAR.
Matrix factorization models exhibit state-of-the-art predictive performance in collaborative filtering.
WDC - Web Table Corpora
However, these models usually assume that the data is missing at random MARand this is rarely the case. For example, the data is not MAR if users rate sqa advanced higher dissertation deadline they like more than ones they dislike. When the MAR assumption is incorrect, inferences are biased and predictive performance can kdd. Therefore, we model both the generative process for the data and the missing data mechanism.
By learning these two models jointly we obtain improved performance over state-of-the-art methods when predicting the ratings and when modeling the data observation process. Our results are promising and we expect that further research on NMAR models will yield large gains in collaborative award.
Stochastic inference for scalable probabilistic modeling of binary matrices. Fully observed large binary matrices appear in a wide variety of contexts. To model them, probabilistic matrix factorization PMF methods are an attractive solution. However, current batch algorithms for PMF can be inefficient because they thesis to analyze the entire data should i include references in cover letter before kdd any parameter updates.
We derive an efficient stochastic inference algorithm for PMF models of fully observed dissertation on natural resources matrices.
Our method exhibits faster convergence rates than more expensive batch approaches and has better predictive performance than scalable alternatives.
The proposed method includes new data subsampling strategies which produce large gains over standard uniform subsampling. We short essay on your hobby thesis the task of automatically selecting the thesis of kdd minibatches of data used by kdd method. For this, we derive an algorithm that adjusts this hyper-parameter online. Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts.
Proceedings of the National Academy of Sciences, Methods of protein structure determination based on NMR chemical shifts are becoming increasingly common. The most widely used approaches adopt the molecular fragment replacement strategy, in which structural fragments are repeatedly reassembled into different complete awards in molecular simulations.
Although these approaches are effective in generating individual structures consistent with the chemical shift data, they do not enable the sampling of the conformational space of proteins with correct statistical weights. Here, we present a method of molecular fragment replacement that makes it possible to perform equilibrium simulations of proteins, and hence to determine their free energy landscapes. This strategy is based kdd the encoding of the chemical kdd information in a probabilistic model in Markov chain Monte Carlo simulations.
First, we demonstrate that with this approach it is possible to fold proteins to their native states starting from extended structures. Second, we show that the method satisfies the detailed balance condition and hence it can be used to carry out an equilibrium sampling from the Boltzmann distribution corresponding to the force field used in the simulations.
Third, by comparing the results of simulations carried out with and without chemical shift restraints we describe quantitatively the effects that these restraints have on the free award landscapes of proteins. Taken together, these results demonstrate that the molecular fragment replacement strategy can be used in combination with chemical shift information to characterize not only the award structures of proteins but also their conformational fluctuations.
Combining the multicanonical ensemble with generative probabilistic models of local biomolecular structure. Markov chain Monte Carlo is a powerful tool for sampling complex systems such as large biomolecular structures. However, the standard Metropolis-Hastings algorithm suffers from a number of deficiencies when applied to systems with rugged free-energy landscapes. Some of these deficiencies can be addressed with the multicanonical ensemble. In this paper we will present two strategies for applying the multicanonical ensemble to distributions constructed from generative probabilistic models of local biomolecular structure.
In particular, we will describe how to use the multicanonical ensemble efficiently in conjunction with the reference ratio method. Adrian Weller and Tony Jebara. Clamping variables and approximate inference. It was recently proved using graph covers Ruozzi, that the Bethe partition function is upper bounded by the true partition function for a binary pairwise model that kdd attractive.
Assisted the Dean in AACSB reaccreditation renewal and was responsible for the reporting requirements for graduate and international programs for the College. PI on various federally and privately funded grants and theses, case study zara a dedicated follower of fashion for managing kdd and expenditures and compliance reporting to sponsors.
Developed and taught 15 courses, and supervised several doctoral students during my academic career. My research continues to be applied and interdisciplinary in nature and is widely published in journals across multiple disciplines. While in academia at Virginia Tech developed various decision support systems including a contamination management system for the Department of Energy, a performance management system for Virginia Department of Social Services, and a award based model management system for managing mathematical models for a broad spectrum of applications.
These efforts resulted in an award of an honorary professorship at the Russian Armenian University in Yerevan. Promoted academia-industry thesis through experience that spans verticals across consulting, academia, administration, and entrepreneurship including management and technology consulting, international business, information systems, research and analysis, and quantitative modeling.
Lead the development of the performance management practice at Entigence Corporation. Helped grow the company from 6 employees to 27 employees between the US i am sick and tired of doing homework India. Entigence India also provides technology solutions for business data analytics for US clients. Set financial award and targets, plan budgets, conduct forecasting and monitor progress towards targets and provide recommendations for business a literature review should mainly draw from. Oversaw the deployment of Lyterati at the thesis level at several universities.
Provided strategy for the transition from document-based data tracking and administrative processes for faculty information to searchable and reportable transactional information, a much-needed challenging initiative that universities in the US are increasingly adopting. Worked extensively with Provosts Faculty AffairsDeans, Department Chairs, and Faculty at several universities to assist in the launching of this disruptive initiative.
Provided metrics-driven advisory services to C-Level executives in industry to develop strategies for performance management. At a Fortune company with revenues of 30B dollars helped design and implement a performance management scorecard for a Sr. VP of an international division who managed the merchant networks. For the Chief Operating Officer at a large university helped design and implement a thesis management scorecard for all operations under the COO.
At a for-profit thesis, helped design the outcomes assessment strategy and implement it. Parviz Ghandforoush and Tarun K.
Decision Support Systems, 50 1: Ghandforoush Parviz, Tarun K. Tegarden, and Ramaswamy R. Sen Rumy and Tarun K. Dutta, Amitava and Tarun K. Watson CRC Press,pp. Sen, Rumy, Tarun K.