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.
Paper studies ensemble probabilistic regression trees for smooth approximations.
We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and …
Paper introduces methods to handle missing data in probabilistic regression trees.
Probabilistic proof of smooth boundaries in optimal stopping problems.
The paper provides a uniform convergence bound for smooth calibration error and its relationship with functional gradient.
Researchers propose better probabilistic models for deep learning.
Parallel-in-time solver reduces ODE simulation time from linear to logarithmic.
SQR Averaging improves probabilistic electricity price forecasting.
We review recent probabilistic results on covariant Schrödinger operators on vector bundles over (possibly locally infinite) weighted graphs, and explain applications like semiclassical limits. We also clarify the relationship between these results and their formal analogues on smooth (possibly noncompact) Riemannian m…
Paper improves probabilistic forecasts of electricity prices.
SPH-ParVI uses fluid dynamics to sample unknown densities efficiently.
Proposes PSCs for UQ in deep nets without retraining.
Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid for better integrating renewable energy sources such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form…
VIR model improves regression accuracy and uncertainty estimation for imbalanced data.
SPPL simplifies probabilistic programming for exact inference.
KalMamba improves RL efficiency with probabilistic SSMs.
Reciprocal processes are acausal generalizations of Markov processes introduced by Bernstein in 1932. In the literature, a significant amount of attention has been focused on developing dynamical models for reciprocal processes. In this paper, we provide a probabilistic graphical model for reciprocal processes. This le…
Entropic herding generates smooth distributions for probabilistic modeling.
Combines coarse learners for nonparametric probabilistic regression.
Point forecasting of univariate time series is a challenging problem with extensive work having been conducted. However, nonparametric probabilistic forecasting of time series, such as in the form of quantiles or prediction intervals is an even more challenging problem. In an effort to expand the possible forecasting p…
Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete late…
Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distri…
Probabilistic theory counts intersections in Riemannian spaces.
Bayesian Probabilistic Integration uses BART for high-dimensional, non-smooth functions.
We study a "div-grad type" sub-Laplacian with respect to a smooth measure and its associated heat semigroup on a compact equiregular sub-Riemannian manifold. We prove a short time asymptotic expansion of the heat trace up to any order. Our main result holds true for any smooth measure on the manifold, but it has a spec…
Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM)…
We give an asymptotic probabilistic real Riemann-Hurwitz formula computing the expected real ramification index of a random covering over the Riemann sphere. More generally, we study the asymptotic expected number and distribution of critical points of a random real Lefschetz pencil over a smooth real algebraic variety…
Paper proposes a probabilistic alignment method for domain adaptation.
A new probabilistic polygonal curve representation using Gaussian Mixture Models.
As inductive inference and machine learning methods in computer science see continued success, researchers are aiming to describe ever more complex probabilistic models and inference algorithms. It is natural to ask whether there is a universal computational procedure for probabilistic inference. We investigate the com…
The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaussian) on the latent space and penalizes the Kullback--Leibler (KL) divergence between the posterior and prior. In NLP, VAEs are extremely difficult to train due to the problem of KL collapsing to zero. One has to implement various heu…
In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts. They adapt to the incoming signal, quantify uncertainty, and measure correlation between the signal's amplitude and phase information, making time domain resynthesis straightforward. However, th…
New framework certifies robustness for regression models.
A novel GPUM constructs Gaussian Processes for unknown manifolds with probabilistic metrics.
We prove geometric and cohomological stabilization results for the universal smooth degree hypersurface section of a fixed smooth projective variety as goes to infinity. We show that relative configuration spaces of the universal smooth hypersurface section stabilize in the completed Grothendieck ring of variet…
Enhanced probabilistic sampling on manifolds using Double Diffusion Maps and Geometric Harmonics.
Model forecasts water demand with probabilistic multi-step-ahead approach.
New algorithm estimates complex probabilistic models efficiently.
Improves convergence speed in compressive sensing with a new probabilistic approach.
The task of calibration is to retrospectively adjust the outputs from a machine learning model to provide better probability estimates on the target variable. While calibration has been investigated thoroughly in classification, it has not yet been well-established for regression tasks. This paper considers the problem…
Unified approach to path planning using probabilistic inference on factor graphs.
Novel approach for estimating conditional expectations using Bayesian quadrature.
Derives VMP for LDA, simplifying inference for topic modeling.
New method improves Kalman filtering and smoothing for large state spaces.
Proposes DILATE and STRIPE++ for precise time series forecasting.
We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measurement functions. This is achieved by defining the measurement sequence to consist of the observations of the difference between the derivative …
New algorithm for contextual combinatorial bandits with probabilistic arm triggering.
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows…