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.
Methods for prediction and tolerance intervals in non-normal models.
A network of independently trained Gaussian processes (StackedGP) is introduced to obtain predictions of quantities of interest with quantified uncertainties. The main applications of the StackedGP framework are to integrate different datasets through model composition, enhance predictions of quantities of interest thr…
We propose a dynamical model for business cycle based on an optimal DI model. In the model there exists a conserved quantity, which corresponds to the total energy in a dynamical system. We found that the business cycle with the period 6 or 7 years is nicely reproduced, since the model predicts a periodic motion in the…
This work connects symmetries and conserved quantities in machine learning.
Enhanced visibility forecasts using CAMS data improve accuracy.
Two models predict net loan losses using Bayesian and frequentist regression.
A new method for experimental design focuses on predicting downstream quantities of interest.
Recent decades have seen an interest in prediction problems for which Bayesian methodology has been used ubiquitously. Sampling from or approximating the posterior predictive distribution in a Bayesian model allows one to make inferential statements about potentially observable random quantities given observed data. Th…
A new approach to newsvendor problems reduces loss by up to 40%.
The paper critiques existing uncertainty concepts and proposes a new decision-theoretic approach.
Stable long-term predictions for fluid flows using neural networks.
The paper introduces recklessness to improve recommendation quality and quantity.
GO-OED maximizes predictive information gain on nonlinear QoIs.
The paper solves the problem of optimal portfolio choice when the parameters of the asset returns distribution, like the mean vector and the covariance matrix are unknown and have to be estimated by using historical data of the asset returns. The new approach employs the Bayesian posterior predictive distribution which…
Valid inference from data and predictions.
Theory explains how AI models can predict unseen tasks without labeled data.
In this work, sequence-to-sequence (seq2seq) models, originally developed for language translation, are used to predict the temporal evolution of complex, multi-physics computer simulations. The predictive performance of seq2seq models is compared to state transition models for datasets generated with multi-physics cod…
This paper describes a time-series-based classification approach to identify similarities between bio-medical-based situations. The proposed approach allows classifying collections of time-series representing bio-medical measurements, i.e., situations, regardless of the type, the length and the quantity of the time-ser…
ROM-net framework applies to industrial design uncertainty quantification.
Convolutional networks predict turbulence from wall quantities.
A regression-based BNN model is proposed to predict spatiotemporal quantities like hourly rider demand with calibrated uncertainties. The main contributions of this paper are (i) A feed-forward deterministic neural network (DetNN) architecture that predicts cyclical time series data with sensitivity to anomalous foreca…
Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest. But the space of all testable correlates is far larger than scientific resources can currently address, so the ability to accurately predict correlations would be useful to plan research and a…
Framework improves CATE estimation by aligning active learning with causal objectives.
Study finds conserved quantities for two types of curves on conformal sphere.
Ecker's and Huisken's quantities agree for ancient mean curvature flows.
LUQ learns QoI from dynamical systems for consistent observation inversion.
We consider a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. We describe a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades off expected return, risk, transaction cost and h…
Proposes a framework for automated radiation therapy treatment planning with uncertainty quantification.
The paper compares Bayesian uncertainty to MAP estimator in random features regression.
Datasets in engineering applications are often limited and contaminated, mainly due to unavoidable measurement noise and signal distortion. Thus, using conventional data-driven approaches to build a reliable discriminative model, and further applying this identified surrogate to uncertainty analysis remains to be very …
Study uses neural networks to predict wall quantities in turbulent flows.
The study evaluates how well local explanations align with model predictions.
The paper develops bounds for predictive values in binary classification.
We present a sampling-free approach for computing the epistemic uncertainty of a neural network. Epistemic uncertainty is an important quantity for the deployment of deep neural networks in safety-critical applications, since it represents how much one can trust predictions on new data. Recently promising works were pr…
Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier-Stokes (RANS) simulations have gained significant interest in the computational fluid dynamics community. Modern machine learning algorithms have opened up a new area of black-box turbulence models allowing for the tuning of RANS simulati…
The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied. In this work, we cast the problem of predicting clearing prices into a learning framework and use the resulting models to perform revenue optimization in auctions and markets with contextual information. T…
Deep neural networks have outperformed existing machine learning models in various molecular applications. In practical applications, it is still difficult to make confident decisions because of the uncertainty in predictions arisen from insufficient quality and quantity of training data. Here, we show that Bayesian ne…
We quantify predictive uncertainty using the posterior predictive variance.
In this paper, we consider two different monotone quantities defined for the Ricci flow and show that their asymptotic limits coincide for any ancient solutions. One of the quantities we consider here is Perelman's reduced volume, while the other is the local quantity discovered by Ecker, Knopf, Ni and Topping. This es…
An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution. Several such rules have b…
Given a vector field on a manifold M, we define a globally conserved quantity to be a differential form whose Lie derivative is exact. Integrals of conserved quantities over suitable submanifolds are constant under time evolution, the Kelvin circulation theorem being a well-known special case. More generally, conserved…
We study analytically and numerically Minsky instability as a combination of top-down, bottom-up and peer-to-peer positive feedback loops. The peer-to-peer interactions are represented by the links of a network formed by the connections between firms, contagion leading to avalanches and percolation phase transitions pr…
Derive monotone quantities for harmonic functions on asymptotically flat 3-manifolds with nonnegative scalar curvature.
Method quantifies uncertainties in complex MRF models.
The paper introduces a method to incorporate expert opinion on observable quantities into statistical models.
This paper analyzes the difficulty of unsupervised domain adaptation using information theory.
Equivariant networks improve geometric prediction without scalar approximations.
Develops a multilevel Monte Carlo framework with dropout for efficient uncertainty quantification.