A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New algorithm improves solving constraint satisfaction problems by avoiding contradictory estimates.
Paper introduces ASP, a variant of AMP for low-rank matrix estimation, showing improved performance under model mismatch.
As one of the most important types of (weaker) supervised information in machine learning and pattern recognition, pairwise constraint, which specifies whether a pair of data points occur together, has recently received significant attention, especially the problem of pairwise constraint propagation. At least two reaso…
We survey the correct definition of a generalized Dirac operator on a Space--Time and the classical result about propagation of singularities. This says that light travels along light--like geodesics. Finally we show this is also true for generalized Dirac operators.
We introduce an efficient message passing scheme for solving Constraint Satisfaction Problems (CSPs), which uses stochastic perturbation of Belief Propagation (BP) and Survey Propagation (SP) messages to bypass decimation and directly produce a single satisfying assignment. Our first CSP solver, called Perturbed Blief …
This is mainly a survey article on the recent development of the theory of graph-like Legendrian unfoldings and its applications. The notion of big Legendrian submanifolds was introduced by Zakalyukin for describing the wave front propagations. Graph-like Legendrian unfoldings belong to a special class of big Legendria…
This thesis investigates belief propagation's performance in graphical models with loops.
Variational inference is a powerful concept that underlies many iterative approximation algorithms; expectation propagation, mean-field methods and belief propagations were all central themes at the school that can be perceived from this unifying framework. The lectures of Manfred Opper introduce the archetypal example…
New algorithms improve binary neural network configurations.
A new method for target propagation using iterative approximations converges fast and is more biologically plausible.
CEP improves inference efficiency and accuracy by conditional moment matching.
Truncated back-propagation improves hyperparameter tuning and meta learning efficiency.
Survey examines deep neural networks' ability to approximate functions.
The paper proposes a scalable framework for uncertainty quantification and propagation in surrogate-based Bayesian inference.
Loss-calibrated EP improves Bayesian decision-making by focusing on utility-sensitive posterior approximations.
Several numerical approximation strategies for the expectation-propagation algorithm are studied in the context of large-scale learning: the Laplace method, a faster variant of it, Gaussian quadrature, and a deterministic version of variational sampling (i.e., combining quadrature with variational approximation). Exper…
A susceptibility propagation that is constructed by combining a belief propagation and a linear response method is used for approximate computation for Markov random fields. Herein, we formulate a new, improved susceptibility propagation by using the concept of a diagonal matching method that is based on mean-field app…
DP-SEP privatizes EP by refining a single factor per data point.
New -BP algorithm improves belief propagation for graphs with loops.
Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems. This paper proposes and analyzes a generalization of Opper and Winther's expectation consistent (EC) approximate inference method. Th…
We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approximation method proposed by Yedidia et al. We show that the message passing fixed-point equations obtained with this combination correspond to station…
We discuss positivity properties of `distinguished propagators', i.e. distinguished inverses of operators that frequently occur in scattering theory and wave propagation. We relate this to the work of Duistermaat and Hörmander on distinguished parametrices (approximate inverses), which has played a major role in quantu…
Factor graphs are important models for succinctly representing probability distributions in machine learning, coding theory, and statistical physics. Several computational problems, such as computing marginals and partition functions, arise naturally when working with factor graphs. Belief propagation is a widely deplo…
TD learning with neural networks can lead to worse solutions than Monte-Carlo methods, especially in discontinuous value functions.
Survey on learning Boolean functions in computational theory.
Develops an efficient approximation for collapsed Gibbs sampling in complex models.
Symmetries in shrinking Ricci solitons spread outward.
Innovative PGMs match neural networks, revealing precise approximations during forward propagation.
Following Feynman's prescription for constructing a path integral representation of the propagator of a quantum theory, a short-time approximation to the propagator for imaginary time, N=1 supersymmetric quantum mechanics on a compact, even-dimensional Riemannian manifold is constructed. The path integral is interprete…
A new stochastic algorithm approximates optimal distributions without requiring propagation of chaos.
This is an extensive (published) survey on CR geometry, whose major themes are: formal analytic reflection principle; generic properties of Systems of (CR) vector fields; pairs of foliations and conjugate reflection identities; Sussmann's orbit theorem; local and global aspects of holomorphic extension of CR functions;…
QP improves Gaussian process inference by minimizing Wasserstein distance.
Stochastic gradient descent (SGD) has achieved great success in training deep neural network, where the gradient is computed through back-propagation. However, the back-propagated values of different layers vary dramatically. This inconsistence of gradient magnitude across different layers renders optimization of deep …
Paper proposes a new MIMO detection algorithm using Gaussian Mixture Expectation Propagation.
Improved BP algorithm outperforms loopy BP in MAP inference.
Expectation propagation (EP) is a deterministic approximation algorithm that is often used to perform approximate Bayesian parameter learning. EP approximates the full intractable posterior distribution through a set of local approximations that are iteratively refined for each datapoint. EP can offer analytic and comp…
Efficient EP algorithm improves smoothing distribution inference in financial models.
Dynamic trees are mixtures of tree structured belief networks. They solve some of the problems of fixed tree networks at the cost of making exact inference intractable. For this reason approximate methods such as sampling or mean field approaches have been used. However, mean field approximations assume a factorized di…
Digital personas improve survey results for stable attributes but fail for subjective responses.
Many inference problems involving questions of optimality ask for the maximum or the minimum of a finite set of unknown quantities. This technical report derives the first two posterior moments of the maximum of two correlated Gaussian variables and the first two posterior moments of the two generating variables (corre…
BBPL uses block updates to learn Markov random fields without full inference.
A new backprop method reduces memory usage for deep neural networks.
Paper combines deterministic and stochastic inference methods for PGMs.
A decentralized MARL algorithm for value propagation in non-linear settings.
The paper proposes a new framework for accurate uncertainty representation and propagation.
Proposes a deep learning method for uncertainty propagation in complex systems.
This survey covers in our opinion the most important results in the theory of continuous selections of multivalued mappings (approximately) from 2002 through 2012. It extends and continues our previous such survey which appeared in Recent Progress in General Topology, II, which was published in 2002. In comparison, our…
pRSL combines probabilistic rules to improve multi-label classification.