Graphical classifier handles model uncertainty with Bayesian model averaging.
problem Model selection uncertainty in Bayesian classification.
method Particle Gibbs strategy for posterior sampling from decomposable graphical models.
result Proposed classifier outperforms standard Bayesian and other classifiers.
An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution. Several such rules have b…
Interactive graphics help diagnose forest classifiers in R.
problem Exploring and understanding complex ensemble models like random forests.
method Constructing and visualizing data interactively using R packages.
result Interactive graphics provide insights into model complexity and variable importance.
Proposes and evaluates three diagnostic graphics for probabilistic classifiers.
problem Evaluating and comparing probabilistic classifiers.
method Triptych of diagnostic graphics: reliability diagram, ROC curve, Murphy diagram.
result Visual diagnostics reveal distinct aspects of forecast performance.
AGM uses adversarial approach for robust prediction in structured prediction problems.
problem Structured prediction problems with complex relationships between variables.
method Adversarial Graphical Models (AGM) for distributionally robust prediction.
result AGM achieves Fisher consistency and flexibility in loss metrics.
Two new Hie-TAN and Hie-TAN-Lite algorithms improve TAN for hierarchical feature spaces.
problem Learning dependencies in hierarchical feature spaces.
method Exploits hierarchical parent-child relationships as constraints to learn a dependency tree.
result Hie-TAN-Lite outperforms Hie-TAN and other methods in predictive accuracy.
Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In any probabilistic setting, Bayesian averaging (BA) provides theoretically optimal…
New method improves inference for Bayesian graphical models.
problem Improving inference efficiency and accuracy for Bayesian graphical models.
method Proposes Heron inference, a deterministic method for Bayesian graphical models.
result Significantly outperforms baseline methods in inference for Bayesian graphical models.
This review classifies deep generative models from a graphical modeling perspective.
problem Learning with deep generative models from a graphical modeling perspective.
method Organized from graphical modeling perspective, differentiating model definitions from learning algorithms.
result Different learning algorithms can be applied to the same model.
New method controls latent variables in graphical models to improve causal inference and prediction.
problem Uncertainty in causal relationships due to unobserved confounders.
method Iteratively derives proxies for latent variables from model residuals.
result Improves structural inference and prediction performance of causal models.
Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of …
No multi-class labels needed for multi-class classification.
problem Multi-class classification without requiring class-specific labels.
method Meta classification learning using pairwise similarity prediction.
result The method learns a multi-class classifier from binary classifier for pairwise similarity.
New algorithm improves learning of latent-variable models.
problem Learning general latent-variable graphical models is challenging.
method Predictive Belief Propagation algorithm for general latent-variable graphical models.
result Significantly outperforms EM and spectral algorithms.
Proposes a novel classifier for probabilistic record linkage.
problem Probabilistic record linkage across databases.
method Graphical model based on mixture of Poisson distributions with latent variables, using gamma priors and supervised labels.
result Classifier works effectively with sparse and streaming data.
A new method combines Gaussian graphical models for better distributed Gaussian process predictions.
problem Poor results from traditional DGP due to violated conditional independence assumption.
method Proposes using Gaussian graphical models to aggregate local predictions from subsets of data.
result Our method outperforms other state-of-the-art DGP approaches on both synthetic and real datasets.
New method tests independence and learns graphs without manual choices.
problem Testing independence of multivariate random variables is hard.
method Link between independence testing and supervised learning.
result Predictive independence tests outperform current methods.
The study proves properties of translating solitons in 3D space.
problem Characterizing translating solitons in R3. method Analyzing mean curvature flow and mean convex translating solitons.
result Proves convexity of complete immersed translating solitons and classifies them.
In this paper, we present an algorithm for minimizing the difference between two submodular functions using a variational framework which is based on (an extension of) the concave-convex procedure [17]. Because several commonly used metrics in machine learning, like mutual information and conditional mutual information…
Let (X,Y) be a random variable consisting of an observed feature vector X∈X and an unobserved class label Y∈{1,2,...,L} with unknown joint distribution. In addition, let D be a training data set consisting of n completely observed independent copies of (X,Y). Usual classification…
The task of matching co-referent records is known among other names as rocord linkage. For large record-linkage problems, often there is little or no labeled data available, but unlabeled data shows a reasonable clear structure. For such problems, unsupervised or semi-supervised methods are preferable to supervised met…
Method quantifies uncertainties in complex MRF models.
problem Uncertainties in MRF predictions due to data, modeling, and approximations.
method Information-based uncertainty quantification using MRF graphical structure.
result Tight bounds on predictions for quantities of interest in MRFs.
Fragment-based autoencoder improves molecule screening with little data.
problem Limited experimental data for molecular optimization.
method Fragment-based graphical autoencoding to generate structural fingerprints.
result Fragment-based autoencoding reduces prediction error in small data.
NETpred uses graph models to predict multiple market indices.
problem Predicting multiple market indices with high accuracy.
method NETpred constructs a heterogeneous graph of related indices and stocks, selects representative nodes, and uses semi-supervised learning to predict index labels.
result NETpred outperforms state-of-the-art methods by 3%-5% in F-score on various datasets.
Machine learning predicts accurate cloning of printed graphical codes.
problem Clonability of anti-counterfeiting printable graphical codes.
method Simple system composed of fully connected neural network layers.
result Accurate estimation of digital codes from printed counterparts in certain cases.
Agents learn their states from interactions using Bayesian classifiers and ML estimators.
problem Learning unknown states from score graphs in social networks.
method Bayesian framework, local classifiers, centralized ML estimator, relaxed probabilistic model.
result Agents can learn their states through distributed computation.
Study complete space-like stationary surfaces with graphical Gauss image, estimating exceptional values and classifying degenerate surfaces.
problem Estimating exceptional values and classifying degenerate surfaces in Minkowski spacetime.
method Generalizing Fujimoto's theorem, estimating upper bounds, introducing conjugate similarity, and establishing structure theorems.
result Sharp contrast to Bernstein type results for minimal surfaces, estimating upper bounds of exceptional values.
GPU acceleration speeds up financial machine learning training time.
problem Time-intensive classifier training in financial machine learning.
method Deployed NVIDIA GPUs for parallel high-speed arithmetic operations.
result Significantly faster training time achieved.
Modeling complex systems with multi-resolution data and causal dependencies.
problem Accurate prediction of complex systems with varying causal dependencies and multi-resolution data.
method Score-based Variational Graphical Diffusion Model (Temporal-SVGDM) that constructs individual SDEs for each variable at its native resolution and couples them through a causal score mechanism.
result Improved prediction accuracy and causal understanding compared to existing methods, especially in temporal scenarios.
We consider the problem of learning Bayesian network classifiers that maximize the marginover a set of classification variables. We find that this problem is harder for Bayesian networks than for undirected graphical models like maximum margin Markov networks. The main difficulty is that the parameters in a Bayesian ne…
Unified framework for analyzing stable learning algorithms across different dataset shifts.
problem Analyzing and comparing stability of learning algorithms across various dataset shifts.
method Causal graphical representation to express dataset shifts and a hierarchy of operators to disable shift-causing edges.
result Established conditions for optimal performance and derived new algorithms for finding stable distributions.
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an…
A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.
problem Balancing computational efficiency and uncertainty quantification in Gaussian processes for big data.
method Using graphical models to select important experts and aggregate their predictions while ensuring uncertainty quantification.
result Substantially reduces computational cost of aggregating dependent experts while ensuring calibrated uncertainty quantification.
A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image distortions, particularly when the available training data are insufficient to learn accurate models, is a significant chall…
Automatic image annotation (AIA) raises tremendous challenges to machine learning as it requires modeling of data that are both ambiguous in input and output, e.g., images containing multiple objects and labeled with multiple semantic tags. Even more challenging is that the number of candidate tags is usually huge (as …
New framework for robustness guarantees in discrete domains.
problem Adversarial robustness in constrained discrete domains.
method Graphical framework for adversarial costs, accommodating complex cost functions.
result Provably minimal adversarial cost and robustness guarantees.
The paper proposes new models for short-term traffic flow forecasting.
problem Short-term traffic flow forecasting in network-scale ITS.
method Combining graphical lasso and neural networks with multi-link models and multi-task learning.
result The proposed models improve prediction accuracy and efficiency.
The paper shows cross-validation fails in learning Gaussian graphical model structures.
problem Cross-validation's failure in learning Gaussian graphical model structures.
method Finite-sample bounds on misidentification probability of Lasso estimator.
result Cross-validation is inconsistent for learning Gaussian graphical model structures.
Skein theory classifies UFCs with specific fusion rules.
problem Classifying unitary fusion categories with specific fusion rules.
method Graphical calculus and rotation operator action on a canonical basis.
result Explicit formulae for Fqqqq when k=2 and C is ribbon. Classifies and constructs translators for curvature flows.
problem Understanding translating solitons in curvature flows.
method Developed rotational theory, introduced signed-neck framework.
result Classified and constructed catenoidal-type translators.
The paper classifies and constructs translating graphs in 3D and higher dimensions.
problem Classifying and constructing translating graphs in various dimensions.
method Full classification and construction of translating graphs using mathematical analysis.
result Classification and construction of new examples of translating graphs.
Develops a new model for deep structured prediction with non-linear output transformations.
problem Limited neighborhood structure and inability to transform output space in deep structured models.
method Introduces a novel model that generalizes existing approaches and maintains applicability of inference techniques.
result Demonstrates improved flexibility and applicability of deep structured models through non-linear output transformations.
VARENN visualizes climate data in 2D images for analysis.
problem Lack of integrated spatiotemporal data in climate models.
method VARENN uses convolutional neural networks to summarize monthly climate data into 2D color images.
result VARENN models accurately classify temperature and precipitation changes.
New classifiers account for context-specific independences.
problem Restrictions in generative models for classification.
method Staged tree classifiers that account for context-specific independences.
result Staged tree classifiers achieve competitive classification accuracy.
SG-PALM learns interpretable tensor models for high-dimensional data.
problem Learning interpretable tensor models for high-dimensional data.
method SG-PALM combines Sylvester generative model and fast proximal alternating linearized minimization.
result SG-PALM converges linearly to global optimum and scales to high dimensions.
DOPE efficiently estimates ATE with complex covariates.
problem Efficient estimation of ATE from complex covariates.
method Proposed DOPE framework for efficient adjustment.
result DOPE retains efficiency even with highly predictive covariates.
New algorithms bound graph structure sampling and learning high-dimensional graphical models.
problem Learning high-dimensional graphical models and efficient graph structure sampling.
method Online learning framework with exponentially weighted average (EWA) or randomized weighted majority (RWM) forecasters using log loss function.
result New sample complexity bounds and efficient algorithms for learning Bayes nets, including trees and chordal skeletons.
NeuroNER simplifies ANN-based NER for non-experts.
problem Challenging use of ANNs for NER by non-experts.
method Graphical web-based user interface for easy annotation, training, and prediction of entities.
result NeuroNER streamlines NER process for non-expert users.
Bayesian method predicts runtime metrics for fog manufacturing.
problem Accurate prediction of runtime performance metrics in fog manufacturing.
method Bayesian sparse regression for multivariate mixed responses.
result Enhanced prediction and statistical inferences of runtime metrics.