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.
Proposes a new method for fiducial inference using autoencoders.
New method for DP inference on categorical data using fiducial matching.
EFI automates statistical inference for big data.
A new method uses deep neural networks for estimating individual treatment effects.
New tools connect CP to GF inference for better probabilistic prediction.
Due to their accuracies, methods based on ensembles of regression trees are a popular approach for making predictions. Some common examples include Bayesian additive regression trees, boosting and random forests. This paper focuses on honest random forests, which add honesty to the original form of random forests and a…
New method for PINNs uncertainty quantification without prior distribution.
The paper analyzes the observability of relative pose estimation using dual quaternions.
We introduce a new high dimensional algorithm for efficiency corrected, maximally Monte Carlo event generator independent fiducial measurements at the LHC and beyond. The approach is driven probabilistically using a Deep Neural Network on an event-by-event basis, trained using detector simulation and even only pure pha…
Quantum field theory connects Riemannian geometry to quantum fluctuations.
Study on rank 2 Higgs bundles on 5-punctured sphere, proving conjecture in lowest degree.
In this work, we have proposed several enhancements to improve the performance of any facial emotion recognition (FER) system. We believe that the changes in the positions of the fiducial points and the intensities capture the crucial information regarding the emotion of a face image. We propose the use of the gradient…
Levy processes, which have stationary independent increments, are ideal for modelling the various types of noise that can arise in communication channels. If a Levy process admits exponential moments, then there exists a parametric family of measure changes called Esscher transformations. If the parameter is replaced w…
Study asymptotics of hyperkähler geometry on singular fibers of Hitchin moduli space.
Clarifies challenges in machine learning uncertainty quantification.
Study shows genericity of singularities in spacetimes with weakly trapped submanifolds.
Nyström KPCA balances computational efficiency and statistical accuracy.
This Chapter, "Overview of Approximate Bayesian Computation", is to appear as the first chapter in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and concepts behind ABC methods with many examples and illustrations.
New method extracts cosmological information from dark matter halo catalogues using graph neural networks.
In this paper, we apply the method of approximate transformation groups proposed by Baikov, Gaziziv and Ibragimov, to compute the first-order approximate symmetry for the Gardner equations with the small parameters. We compute the optimal system and analyze some invariant solutions of These types of equations. Particul…
The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate computing focuses on balancing the efficiency-accuracy trade-off for existing pre-trained networks, which can lead to suboptimal solutions. In…
This work provides efficient algorithms for approximating ℓ_p sensitivities and related statistics.
VISA improves inference efficiency for complex models.
Accelerated RPCholesky speeds up kernel matrix approximations.
Neural network based approximate computing is a universal architecture promising to gain tremendous energy-efficiency for many error resilient applications. To guarantee the approximation quality, existing works deploy two neural networks (NNs), e.g., an approximator and a predictor. The approximator provides the appro…
Neural approximate computing gains enormous energy-efficiency at the cost of tolerable quality-loss. A neural approximator can map the input data to output while a classifier determines whether the input data are safe to approximate with quality guarantee. However, existing works cannot maximize the invocation of the a…
Develops a fast variational approximation for high-dimensional empirical Bayes posteriors.
New GP methods account for both data and computational uncertainty.
Hard to approximate critical points for simple nonconvex functions.
RCaGP improves robustness and computational efficiency in Gaussian processes.
We provide a fast approximation to eNTKs for neural networks.
Reduced modeling of a computationally demanding dynamical system aims at approximating its trajectories, while optimizing the trade-off between accuracy and computational complexity. In this work, we propose to achieve such an approximation by first embedding the trajectories in a reproducing kernel Hilbert space (RKHS…
The runtime for Kernel Partial Least Squares (KPLS) to compute the fit is quadratic in the number of examples. However, the necessity of obtaining sensitivity measures as degrees of freedom for model selection or confidence intervals for more detailed analysis requires cubic runtime, and thus constitutes a computationa…
The paper reduces xVA calculations by approximating sensitivities.
Survey on learning Boolean functions in computational theory.
The paper proposes using path signatures for better inference in time series data.
Paper speeds up GP inference by reducing precision matrix computation.
TopoFisher learns topological summaries by maximizing Fisher information, improving parameter efficiency and inference quality.
Computational method approximates homology groups of compact metric spaces.
Matrix multiplication is a fundamental building block for large scale computations arising in various applications, including machine learning. There has been significant recent interest in using coding to speed up distributed matrix multiplication, that are robust to stragglers (i.e., machines that may perform slower …
Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is to output high-accuracy posterior mean and uncertainty estimates. Classical Monte Carlo methods, particularly Markov Chain Monte Carlo, rem…
We construct algorithms via binomial approximations for computation of prices of game put options and obtain estimates of approximation errors.
The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several B…
Nystrom approximation speeds up kernel model training.
The paper examines how kernel approximations affect Gaussian process regression in large data applications.
Markov chains and diffusion processes are indispensable tools in machine learning and statistics that are used for inference, sampling, and modeling. With the growth of large-scale datasets, the computational cost associated with simulating these stochastic processes can be considerable, and many algorithms have been p…
Bayesian inference requires approximation methods to become computable, but for most of them it is impossible to quantify how close the approximation is to the true posterior. In this work, we present a theorem upper-bounding the KL divergence between a log-concave target density and its La…
Using classical Taylor series techniques, we develop a unified approach to pricing and implied volatility for European-style options in a general local-stochastic volatility setting. Our price approximations require only a normal CDF and our implied volatility approximations are fully explicit (ie, they require no spec…