Proposes a new algorithm for efficient probabilistic inference.
problem Efficient probabilistic inference in deep models with graphical structures.
method Structured inference networks and variational message-passing algorithm.
result Enables fast and efficient natural-gradient inference for deep structured models.
New distribution simplifies covariance matrix inference.
problem Efficient inference for covariance matrices in large models.
method Incorporates Inverse G-Wishart distribution for variational message passing.
result Elegant and succinct expression of variational message passing fragments.
Agent uses message passing to optimize robot navigation, balancing exploration and exploitation.
problem Optimizing robot navigation in continuous-valued spaces with uncertainty.
method Autoregressive active inference agent using message passing on a factor graph.
result Agent modulates action based on predictive uncertainty, leading to better model of dynamics.
The paper analyzes the convergence rates of smooth message passing algorithms in entropy-regularized MAP inference.
problem Finding the most likely configuration in graphical models with combinatorial optimization.
method Entropy-regularized linear programming relaxations and smooth message passing algorithms.
result The number of iterations sufficient to recover the true integral MAP solution is determined.
Tree-AMP simplifies inference in complex tree-structured models.
problem Inference in high-dimensional tree-structured models.
method Approximate Message Passing algorithms for various machine learning tasks.
result Theoretical performance predictions and automated entropy estimation.
Graph Neural Networks improve probabilistic inference in complex graphs.
problem Performing accurate inference in probabilistic graphical models with loops.
method Using Graph Neural Networks to learn and solve message-passing algorithms.
result GNNs outperform traditional belief propagation on loopy graphs.
New algorithms accelerate MAP inference in Markov fields with faster convergence.
problem Finding the most likely configuration in discrete-valued Markov random fields.
method Entropy-regularized linear programming with accelerated gradient methods.
result Accelerated algorithms find optimal solutions faster, especially when the LP is tight.
Efficient method for resampling problems using vector approximate message passing.
problem Computational demand in resampling techniques for statistical inference and ensemble learning.
method Combination of replica method from statistical physics and vector approximate message passing from information theory.
result Fast convergence and high approximation accuracy for variable selection problems.
New algorithm improves graph inference tasks.
problem Complex graph reasoning and prediction tasks.
method Policy Message Passing algorithm reformulates graph inference as stochastic sequential processes.
result Consistently outperforms state-of-the-art models.
Topo-MLP learns network representations without message passing.
problem Lack of efficient higher-order network modeling methods.
method Proposes Topo-MLP, a simplicial neural network algorithm using MLP and HONC loss.
result Demonstrates improved robustness and efficiency in representation learning.
AIF improves physical AI agents' performance in dynamic environments.
problem Physical AI agents are less capable than biological agents in open-ended real-world environments.
method Developed from probability theory, Bayesian machine learning, variational inference, and Active Inference (AIF), grounded in the Free Energy Principle.
result AIF minimizes variational free energy and is well-suited to physical constraints.
Automates Bayesian signal processing algorithm design using factor graphs.
problem Designing efficient Bayesian signal processing algorithms.
method Factor graph approach and ForneyLab tool for automated inference.
result ForneyLab outperforms competitors in automated inference for state-space models.
This work analyzes mini-batch inference limits and introduces Mini-AMP for clustering.
problem Analyzing information-theoretic limits of mini-batch inference in statistical learning.
method Detailed analysis of Mini-AMP for generalized linear models and low-rank matrix factorization.
result Characterization of optimal performance and phase transitions for mini-batch inference.
Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos or neural recordings, are now a common feature of modern data analysis. Explaining the phenomena underlying these diverse data sets requires flexible and accurate models. In this paper, we promote Gaussian pro…
Bayesian method improves online NARMAX model identification.
problem Online identification of nonlinear systems with small sample sizes and low noise.
method Variational Bayesian inference using message passing algorithm for polynomial NARMAX models.
result Variational Bayesian estimator outperforms recursive and offline least-squares methods.
Active sampling algorithm improves accuracy of inferred scores from pairwise comparisons.
problem Inference of accurate scores from time-consuming pairwise comparisons.
method Approximate message passing and expected information gain maximization.
result ASAP offers the highest accuracy of inferred scores compared to existing methods.
New algorithm minimizes FE objectives for synthetic AIF agents.
problem Minimizing Free Energy for synthetic active inference agents.
method Variational message passing on Constrained FFGs.
result Message passing algorithms induce epistemic behavior in AIF agents.
Causal inference improves heterophilic graph learning.
problem Capturing asymmetric node dependencies in graph learning.
method Intervention-based causal inference for graph structure learning.
result CausalMP achieves superior link prediction performance.
New method infers viral load from pooled tests.
problem Inefficient viral load inference in pooled testing.
method Message passing algorithm with PCR noise function.
result Accurate viral load inference possible.
Chance-constrained ActInf allows for small violations of constraints to drive goal-directed behavior.
problem Goal-directed behavior constrained by prior beliefs.
method Introducing chance constraints to ActInf, allowing for small violations of constraints.
result Chance-constrained ActInf allows for a trade-off between robust control and chance constraint violation.
Improved variational inference by enforcing consistency with linear response.
problem Inconsistent variational approximations in inference methods.
method Introducing constraints on covariance to ensure consistency with linear response.
result Improvement in marginal probability distribution inference.
The Duffing oscillator's parameters are identified online using variational message passing.
problem Estimating parameters of a nonlinear Duffing oscillator in real-time.
method Variational message passing on a factor graph of the Duffing oscillator's generative model.
result The online inference procedure performs as well as offline methods.
Analyze message passing algorithms using free probability theory.
problem Dynamics of message passing algorithms for probabilistic models.
method Use freeness assumptions of random matrix theory.
result Recover and analyze properties of message passing algorithms.
Automates model comparison in probabilistic programming.
problem Manual derivations for model comparison are error-prone and time-consuming.
method Message passing on a Forney-style factor graph with a custom mixture node.
result Automates Bayesian model averaging, selection, and combination.
Paper proposes efficient methods for forecasting with large datasets.
problem Forecasting with large, high-dimensional economic data sets.
method Bayesian hierarchical priors, factor graphs, message passing algorithms, Generalized Approximate Message Passing (GAMP).
result The proposed methods outperform traditional approaches in forecasting U.S. price inflation.
Novel approach tightens MAP inference for complex Markov fields.
problem Intractable message passing in MAP inference for large state spaces.
method Benders decomposition applied to local polytope relaxation.
result Upper envelope of messages tightens true message at minimizers.
Derives VMP for LDA, simplifying inference for topic modeling.
problem Manual derivation of VMP equations for LDA is challenging and time-consuming.
method Detailed derivation of VMP update equations for LDA.
result Enables easier implementation of VMP for LDA models.
These are notes from the lecture of Devavrat Shah given at the autumn school "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", that took place in Les Houches, France from Monday September 30th, 2013, till Friday October 11th, 2013. The school was organized by Florent Krzakala from UPMC & E…
In this work, we consider compressed sensing reconstruction from M measurements of K-sparse structured signals which do not possess a writable correlation model. Assuming that a generative statistical model, such as a Boltzmann machine, can be trained in an unsupervised manner on example signals, we demonstrate how…
Paper tackles structure learning with trade-off between accuracy and communication cost.
problem Recovering graph of data dependency among nodes with message-passing cost.
method Formalizes trade-off in optimization problem, proposes ASYNC-MAP and SYNC-MAP algorithms.
result Learning algorithms balance accuracy and cost, with theoretical and empirical validation.
New algorithms improve binary neural network configurations.
problem Training binary neural networks efficiently.
method Stochastic message passing algorithms (BP and SP) for discrete inference.
result Stochastic BP and SP find better BNN configurations.
We design iterative receiver schemes for a generic wireless communication system by treating channel estimation and information decoding as an inference problem in graphical models. We introduce a recently proposed inference framework that combines belief propagation (BP) and the mean field (MF) approximation and inclu…
Adversarial GANs improve Bayesian inference on complex models.
problem Hard inference in implicit models and complex posterior distributions.
method Local learning rules based on message passing, minimizing global divergence criterion.
result Unified inference and learning framework for adversarial learning.
New method provides exact performance guarantees for MAP inference in deep networks.
problem Analyzing MAP inference in deep networks with complex data.
method Multi-layer vector approximate message passing (ML-VAMP) method.
result Mean squared error of ML-VAMP estimate can be exactly characterized in high-dimensional random limit.
Boolean matrix factorization and Boolean matrix completion from noisy observations are desirable unsupervised data-analysis methods due to their interpretability, but hard to perform due to their NP-hardness. We treat these problems as maximum a posteriori inference problems in a graphical model and present a message p…
We study the community detection and recovery problem in partially-labeled stochastic block models (SBM). We develop a fast linearized message-passing algorithm to reconstruct labels for SBM (with n nodes, k blocks, p,q intra and inter block connectivity) when δ proportion of node labels are revealed. The signa…
Graph neural networks improve few-shot learning performance.
problem Few-shot learning with limited labeled data.
method Graphical model inference with neural networks.
result Framework extends to semi-supervised and active learning.
Convex message passing algorithms converge to a fixed point.
problem Understanding convergence properties of convex message passing methods.
method Proving convergence of coordinate descent applied to piecewise-affine convex objectives, and showing this applies to various message passing methods.
result The iterates converge to a fixed point of the method, and the algorithm terminates in a known number of iterations.
A new algorithm combines SVGD with local kernels for efficient inference in continuous graph models.
problem Efficient inference in high-dimensional continuous graphical models.
method Stein variational gradient descent extended with local kernels.
result Local kernels improve approximation and enable distributed inference.
This paper introduces a new approach to active inference using constrained Bethe Free Energy.
problem Tackling the limitations of existing epistemic behavior models in active inference.
method Introducing a constrained Bethe Free Energy (CBFE) perspective to optimize epistemic behavior in generative models.
result CBFE optimization leads to more robust and flexible epistemic behavior compared to existing methods.
ES-MLP combines Graph-MLP with edge splitting for node classification on both homophilic and heterophilic graphs.
problem Node classification on graphs with mixed homophilic and heterophilic properties.
method Combines Graph-MLP with edge splitting mechanism from ES-GNN to learn two adjacency matrices based on relevant and irrelevant feature pairs.
result ES-MLP achieves performance comparable to homophilic and heterophilic models without using edges during inference.
New methods for distributed CP improve reliability in healthcare.
problem Post-hoc calibration of models in safety-critical domains.
method Quantile-based and histogram-based distributed conformal prediction via message passing.
result Improved reliability and coverage guarantees in decentralized settings.
A new optimization scheme tackles convex objectives with non-separable penalties.
problem Optimizing convex objectives with non-separable penalties.
method Expectation-consistent approximation and vector approximate message-passing (VAMP) algorithm.
result Faster convergence compared to state-of-the-art approaches in tasks like classification and reconstruction.
While loopy belief propagation (LBP) performs reasonably well for inference in some Gaussian graphical models with cycles, its performance is unsatisfactory for many others. In particular for some models LBP does not converge, and in general when it does converge, the computed variances are incorrect (except for cycle-…
MP-SVGD improves SVGD's performance in high-dimensional Bayesian inference.
problem Particles tend to collapse to modes in SVGD, especially in high dimensions.
method MP-SVGD converts high-dimensional inference into local problems over Markov blankets.
result MP-SVGD prevents vanishing repulsive force in high-dimensional space.
Review of mean-field methods for neural network inference.
problem Understanding neural network learning from a theoretical perspective.
method Mean-field methods, high-temperature expansions, replica method, message passing algorithms.
result Equivalences and complementarities of mean-field methods.
Adapts BP-based algorithms for deep learning, improving performance and accuracy.
problem Training deep neural networks with discrete weights and activations.
method Message-passing algorithms based on Belief Propagation, with reinforcement field.
result Comparable performance to SGD-inspired heuristics (BinaryNet) and higher accuracy in predictions.
BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct model…