Novel graphical models for time series with latent confounders improve causal inference.
problem Causal relationships and independencies in multivariate time series with unobserved confounders.
method Introduced a novel class of graphical models and characterized their properties.
result Novel graphs provide stronger causal inferences without additional assumptions.
TensorFlow Probability introduces JointDistributions for probabilistic programming.
problem Specifying models in probabilistic programming languages.
method Declarative representations of directed graphical models.
result JointDistributions for TensorFlow Probability.
NeurISE uses neural nets to learn graphical models efficiently.
problem Efficiently learning graphical models with high-order terms.
method NeurISE, a neural net-based algorithm that approximates interaction screening objective function.
result NeurISE can find parsimonious representations without prior information.
Consider a mean curvature flow of hypersurfaces in Euclidean space, that is initially graphical inside a cylinder. There exists a period of time during which the flow is graphical inside the cylinder of half the radius. Here we prove a lower bound on this period depending on the Lipschitz-constant of the initial graphi…
VFG model embeds flow-based models with hierarchical structures using variational inference.
problem Flow-based models struggle with high-dimensional latent spaces and lack of tractable inference for graphical structures.
method Integrates flow-based functions through variational inference with aggregation nodes for hierarchical information integration.
result VFG models achieve improved ELBO and likelihood values on multiple datasets.
Graphical notation simplifies complex polynomial constraints in linear models.
problem Complex polynomial constraints in linear structural equation models are impractical.
method Developed a graphical notation to represent these constraints.
result The graphical notation simplifies the representation of many polynomial constraints.
New method learns dependencies in high-dimensional data without graph assumptions.
problem Learning dependencies in nonparametric and high-dimensional settings.
method Neighbourhood lattice decomposition for nonparametric CI learning.
result Compact, non-graphical representation of CI exists in any graphical model.
We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths. Our model family augments graphical structure in latent variables with neural network observation models. For inference, we extend variational autoencode…
We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are linear exponential families (LEFs), directed acyclic graphical models and chain graphs with no hidden variables, including Bayesian networks …
Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world. In spite of such advances, a higher level understanding of vision and imagery does not arise from exhaustively modeling an object, but instead identifying hi…
Bayesian nonparametric approach for clustering non-exchangeable groups.
problem Clustering grouped data with dependencies among groups.
method Graphical Dirichlet process modeling with Markov property.
result Efficient posterior inference algorithm developed.
Novel SVAE learns interpretable discrete data representations from deep learning.
problem Learning interpretable discrete data representations from deep learning.
method Structured variational autoencoder (SVAE) with novel optimization algorithms.
result First competitive comparisons with state-of-the-art time series models.
We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, w…
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference. This allows us to both perform reasoning (e.g. classification) under the structural constraints…
GmGM models multi-axis data for faster analysis.
problem Efficiently modeling multi-axis data across multiple tensors.
method Generalizes Gaussian Graphical Model to learn sparse graph representations across shared axes.
result Achieves significant speedup (order of magnitude) for large multi-modal datasets.
BEGIN network models binary data without parametric assumptions.
problem Conditional independence in non-parametric families of binary data.
method BEGIN network models binary data using sparse linear representations and block factorizations.
result BEGIN network captures conditional independence for arbitrary binary and multinomial variables.
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.
Perfect adaptation in systems is identified and tested using graphical tools.
problem Identifying perfect adaptation in dynamical systems.
method Causal ordering algorithm and graphical representations of dynamical systems.
result Sufficient graphical and testing conditions for perfect adaptation.
Graphical normalizing flows use Bayesian networks to improve normalizing flows' interpretability and performance.
problem Improving the interpretability and performance of normalizing flows.
method Revisiting normalizing flows as probabilistic graphical models, proposing graphical normalizing flows with either prescribed or learnable graph structures.
result Graphical conditioners lead to competitive white box density estimators.
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…
The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple …
Graphical models improve actuarial judgment in insurance claims analysis.
problem Improving actuarial judgment in insurance claims analysis.
method Using graphical models to represent complex inter-dependencies and incorporate qualitative knowledge.
result Graphical models can be used to express and analyze non-life insurance claims data.
We introduce block-tree graphs as a framework for deriving efficient algorithms on graphical models. We define block-tree graphs as a tree-structured graph where each node is a cluster of nodes such that the clusters in the graph are disjoint. This differs from junction-trees, where two clusters connected by an edge al…
In a voice-controlled smart-home, a controller must respond not only to user's requests but also according to the interaction context. This paper describes Arcades, a system which uses deep reinforcement learning to extract context from a graphical representation of home automation system and to update continuously its…
Deep networks can approximate score functions in high-dimensional graphical models efficiently.
problem Approximation efficiency of score functions by deep neural networks in high-dimensional graphical models like Markov random fields.
method Variational inference denoising algorithms and efficient neural network representation.
result Efficient sample complexity bound for diffusion-based generative modeling when score functions are learned by deep neural networks.
This paper studies graphical model selection, i.e., the problem of estimating a graph of statistical relationships among a collection of random variables. Conventional graphical model selection algorithms are passive, i.e., they require all the measurements to have been collected before processing begins. We propose an…
AdaRL adapts quickly to new environments with minimal data.
problem Quickly adapting to new environments in reinforcement learning.
method AdaRL uses a parsimonious graphical representation to encode changes across domains.
result AdaRL can efficiently adapt policies to target domains with few samples.
AutoBayes automates Bayesian graph exploration for robust machine learning.
problem Learning representations invariant to nuisance variations in machine learning.
method Automated Bayesian inference framework exploring different graphical models.
result Significant performance improvement with nuisance-invariant machine learning pipelines.
AGMs outperform EGMs in generalizing to unseen inference tasks.
problem Training graphical models for inference tasks not seen during training.
method Adversarial training of an ensemble of discrete graphical models.
result AGMs significantly outperform EGMs in generalization to unseen tasks.
Hybrid model combines graphical and learned inference for better data estimation.
problem Suboptimal estimation due to poor graphical model approximation of complex data generating process.
method Combines graphical inference with a learned inverse model structured as a graph neural network and formulated as a recurrent neural network.
result Hybrid model estimates chaotic trajectory more accurately than graphical or learned inference alone.
Introduces CStrees for modeling context-specific causal models from observational and interventional data.
problem Modeling context-specific causal relationships from mixed data types.
method Introduces CStrees with a novel factorization criterion and graphical characterization for context-specific conditional independence models.
result Derives a graphical characterization of model equivalence for observational CStrees and extends it to CStree models under context-specific interventions.
Profile graphical models represent multivariate dependence under varying risk factors.
problem Capturing varying conditional independence structures across different levels of a risk factor.
method Introducing a novel class of graphical models (profile graphical models) that represent multivariate dependence under varying risk factors, and developing a Bayesian approach for learning shared sparsity structures.
result Demonstrated enhanced ability to capture subject-specific differences in protein network data from acute myeloid leukemia.
Gaussian graphical model is a graphical representation of the dependence structure for a Gaussian random vector. It is recognized as a powerful tool in different applied fields such as bioinformatics, error-control codes, speech language, information retrieval and others. Gaussian graphical model selection is a statist…
Graphical models help infer domain adaptation across unknown distributions.
problem Unknown changes in joint distribution across domains.
method Use graphical models to encode and infer changes in data distribution.
result Automated domain adaptation framework improves posterior inference of target variable.
This work abstracts deep neural networks into concept graphs for better interpretability in medical tasks.
problem Lack of interpretability in deep learning models, especially in medical domains.
method Developed a graphical representation of medical image processing models to understand concept-based reasoning.
result Extracted a concept-level graph that reveals the decision-making process of deep learning models.
New graph types help identify complex relationships.
problem Understanding complex relationships in data.
method Introducing separable and essentially separable graphs to characterize and identify graphical models.
result Developed algorithms to identify equivalence classes of essentially separable graphs.
The representation of the approximate posterior is a critical aspect of effective variational autoencoders (VAEs). Poor choices for the approximate posterior have a detrimental impact on the generative performance of VAEs due to the mismatch with the true posterior. We extend the class of posterior models that may be l…
Enhances autoencoders to represent transformations explicitly.
problem Lack of explicit representation of transformations in autoencoders.
method Extended variational autoencoders to include latent transformations, using hierarchical graphical models.
result Inferred latent transformations reflect interpretable properties in the observation space.
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.
OpenGM is a C++ template library for defining discrete graphical models and performing inference on these models, using a wide range of state-of-the-art algorithms. No restrictions are imposed on the factor graph to allow for higher-order factors and arbitrary neighborhood structures. Large models with repetitive struc…
Paper characterizes causal graphs from hard interventions and proposes a learning algorithm.
problem Discovering causal structure from hard interventions and observational data.
method Proposes graphical constraints and a learning algorithm based on do-calculus.
result Characterizes interventional equivalence classes of causal graphs with latent variables.
P3I learns holistic scene representations from a single image.
problem Inferring camera poses, object locations, and global scene structures from a single image.
method Combines search-based and gradient-based algorithms.
result P3I outperforms baselines on various image manipulation tasks.
CRL uses causality to build interpretable AI models from complex data.
problem Interpreting deep neural networks' implicit representations.
method Causal representation learning (CRL) synthesizing latent variable models, causal graphical models, and nonparametric statistics.
result CRL can improve interpretability of generative AI models.
A new model learns latent spaces for graph data.
problem Scalability and expressivity limitations in graph generative models.
method Sequential Graph Variational Autoencoder (SGVAE) that learns latent spaces directly from graph data.
result Promising results on a cycle dataset, but need for permutation relaxation.
We show that the A2 clasps in the Karoubi envelope of A2 spider satisfy the recursive formula of the two-variable Chebyshev polynomials of the second kind associated with a root system of type A2. The A2 spider is a diagrammatic description of the representation category for Uq(sl3) and the $…
Hybrid framework combines PGMs and TNs for complex probabilistic modeling.
problem Combining quantum-like correlations into PGM models.
method Introducing decoherence to convert probabilistic TN models into PGMs.
result Hybrid models can represent and combine strengths of both PGMs and TNs.
Chart descriptions are a graphic method to describe monodromy representations of various topological objects. Here we introduce a chart description for hyperelliptic Lefschetz fibrations, and show that any hyperelliptic Lefschetz fibration can be stabilized by fiber-sum with certain basic Lefschetz fibrations.
This paper shows how to perform likelihood inference for complex graphical models efficiently.
problem Intractable normalizing constants in fully and partially observed exponential family graphical models.
method Using a technique from Geyer (1991), the paper estimates the normalizing constant and its gradient.
result Full likelihood-based analysis is feasible and computationally efficient for these models.