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.
Unique solutions found for Plateau problems in smooth and continuous calibrations.
Proximal algorithms applied to current deformation into cycles.
We show that any semi-calibration of degree 2 is locally induced by a smooth almost complex structure. We provide some applications of this result in the regularity theory for semi-calibrated 2-currents
Recently the authors showed that there is a robust potential theory attached to any calibrated manifold (X,φ). In particular, on X there exist φ-plurisubharmonic functions, φ-convex domains, φ-convex boundaries, etc., all inter-related and having a number of good properties. In this paper we show that, in a strong sens…
Cone structures over minimal products can't be calibrated smoothly.
A new method for CT using graph-based regularization.
Given a transportation cost , optimal maps minimize the total cost of moving masses from to . We find a pseudo-metric and a calibration form on such that the graph of an optimal map is a calibrated maximal submanifold. We define the mass of space-like current…
Regime switching volatility models provide a tractable method of modelling stochastic volatility. Currently the most popular method of regime switching calibration is the Hamilton filter. We propose using the Baum-Welch algorithm, an established technique from Engineering, to calibrate regime switching models instead. …
Meta-Cal improves post-hoc calibration of neural networks.
Parity calibration aims to predict increase-decrease events, not values.
Optimizes calibration error estimators for better classifier trustworthiness.
In this paper we introduce and study the notion of plurisubharmonic functions in calibrated geometry. These functions generalize the classical plurisubharmonic functions from complex geometry and enjoy many of their important properties. Moreover, they exist in abundance whereas the corresponding pluriharmonics are gen…
Estimates proper calibration errors and refinement terms in probabilistic predictions.
New study on neural network calibration, linking it to generalization gap.
CP4SBI improves the calibration of credible sets in SBI models.
Bayesian inference calibrates Hall thruster model uncertainty at varying pressures.
Mathematical models of a cellular action potential in cardiac modelling have become increasingly complex, particularly in gating kinetics which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalised medicine to inform clinical decision-making, it is cri…
A new method calibrates scientific models by adding randomness to their predictions.
This paper calibrates Gaussian process predictive distributions for Bayesian optimization to improve sampling decisions.
Applications such as weather forecasting and personalized medicine demand models that output calibrated probability estimates---those representative of the true likelihood of a prediction. Most models are not calibrated out of the box but are recalibrated by post-processing model outputs. We find in this work that popu…
Binary classification is highly used in credit scoring in the estimation of probability of default. The validation of such predictive models is based both on rank ability, and also on calibration (i.e. how accurately the probabilities output by the model map to the observed probabilities). In this study we cover the cu…
Agent-based models, particularly those applied to financial markets, demonstrate the ability to produce realistic, simulated system dynamics, comparable to those observed in empirical investigations. Despite this, they remain fairly difficult to calibrate due to their tendency to be computationally expensive, even with…
Adaptive calibration improves model accuracy with fewer simulations.
Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning. However, predictive uncertainties---especially ones derived from modern deep learning systems---can be inaccurate and impose a bottleneck on performance. This paper explores which uncertainties are needed for…
CAP algorithm controls FCR in online selective prediction.
We present a scoring approach for speaker verification that mimics the standard PLDA-based backend process used in most current speaker verification systems. However, unlike the standard backends, all parameters of the model are jointly trained to optimize the binary cross-entropy for the speaker verification task. We …
Geometric method improves uncertainty estimation in real-time.
Paper develops a new algorithm to improve screening processes.
Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. However, while many current classification frameworks, in particular deep neural networks, achieve high accuracy, they tend to incorrectly estimate uncertainty. In this paper, we propose a …
New quantile methods improve uncertainty quantification across various models.
Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for the financial industry and mathematical finance. In this paper we …
In a recent work, we presented a discriminative backend for speaker verification that achieved good out-of-the-box calibration performance on most tested conditions containing varying levels of mismatch to the training conditions. This backend mimics the standard PLDA-based backend process used in most current speaker …
C-PP-COAD detects anomalies with limited real data, reducing dependency on real calibration data.
The Local Volatility model is a well-known extension of the Black-Scholes constant volatility model whereby the volatility is dependent on both time and the underlying asset. This model can be calibrated to provide a perfect fit to a wide range of implied volatility surfaces. The model is easy to calibrate and still ve…
The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions. Previous work on combining CNNs with Gaussian processes (GPs…
Interest in agent-based models of financial markets and the wider economy has increased consistently over the last few decades, in no small part due to their ability to reproduce a number of empirically-observed stylised facts that are not easily recovered by more traditional modelling approaches. Nevertheless, the age…
New examples show flat singular sets can be arbitrarily complex.
New federated learning methods improve model performance on non-IID data.
Develops PromptShift-CRC for drift-aware conformal risk control in foundation models under prompt and domain shift.
Calibrations help estimate volumes on odd spheres without gaps.
This thesis treats two main topics: calibrated symplectic foliations, and local Lie groupoids. Calibrated symplectic foliations are one possible generalization of taut foliations of 3-manifolds to higher dimensions. Their study has been popular in recent years, and we collect several interesting results. We then show h…
The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.
This study improves hyperparameter optimization for categorical and non-normal data.
Minimal networks minimize length and mass in certain configurations.
This paper concerns the problem of matrix completion, which is to estimate a matrix from observations in a small subset of indices. We propose a calibrated spectrum elastic net method with a sum of the nuclear and Frobenius penalties and develop an iterative algorithm to solve the convex minimization problem. The itera…
New research shows calibration error is flawed when dealing with model uncertainty.
Personalized medicine has become an important part of medicine, for instance predicting individual drug responses based on genomic information. However, many current statistical methods are not tailored to this task, because they overlook the individual heterogeneity of patients. In this paper, we look at personalized …
Proposes a new stochastic method to calibrate climate risks in financial models.