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.
JANET improves time series prediction with adaptive uncertainty regions.
The paper forecasts joint electricity demand across 14 British regions using additive models.
Project aims to improve time series prediction intervals.
We study historical calibration of one- and two-factor models that are known to describe relatively well the dynamics of energy underlyings such as spot and index natural gas or oil prices at different physical locations or regional power prices. We take into account uneven frequency of data due to weekends, holidays, …
This paper focusses on "safe" screening techniques for the LASSO problem. Motivated by the need for low-complexity algorithms, we propose a new approach, dubbed "joint" screening test, allowing to screen a set of atoms by carrying out one single test. The approach is particularized to two different sets of atoms, respe…
A new method for joint noise removal and trend estimation from sparse signals.
Proposes a method to apply conformal prediction to probabilistic time series forecasting models.
Multivariate regular variation plays a role assessing tail risk in diverse applications such as finance, telecommunications, insurance and environmental science. The classical theory, being based on an asymptotic model, sometimes leads to inaccurate and useless estimates of probabilities of joint tail regions. This pro…
A new model predicts spatio-temporal data using adaptive decision trees and point processes.
Low-rank matrix regression refers to the instances of recovering a low-rank matrix based on specially designed measurements and the corresponding noisy outcomes. In the last decade, numerous statistical methodologies have been developed for efficiently recovering the unknown low-rank matrices. However, in some applicat…
Credit risk analysis improved with a joint model for spatial and temporal effects.
There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One approach to resolving this is a shared response factor model. This assumes a shared and time synchronized stimulus across subjects. Such a model…
A new computationally efficient dependence measure, and an adaptive statistical test of independence, are proposed. The dependence measure is the difference between analytic embeddings of the joint distribution and the product of the marginals, evaluated at a finite set of locations (features). These features are chose…
MA-COPP predicts multi-agent system outcomes using data from a different policy, with probabilistic guarantees.
The study improves prediction regions for hierarchical data using a projection step.
Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce. Recent developments in rainfall-runoff modeling, one of the prime challenges in hydrology, show the value of a joint model with shared repres…
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…
A novel ABC method for high-dimensional inverse problems using generative modeling and subset simulation.
Paper develops methods for statistical inference in SGD with infinite variance.
ViLT is a faster vision-and-language model without convolution or region supervision.
In this paper, we study the Edgeworth expansion for a pre-averaging estimator of quadratic variation in the framework of continuous diffusion models observed with noise. More specifically, we obtain a second order expansion for the joint density of the estimators of quadratic variation and its asymptotic variance. Our …
Develops a method for stress testing correlations of financial portfolios.
Study assesses drought and late-frost risks in Bavaria using vine copulas.
In this paper, the `Approximate Message Passing' (AMP) algorithm, initially developed for compressed sensing of signals under i.i.d. Gaussian measurement matrices, has been extended to a multi-terminal setting (MAMP algorithm). It has been shown that similar to its single terminal counterpart, the behavior of MAMP algo…
MIRA scores assess conditional distribution accuracy using joint samples.
An automatic machine learning (AutoML) task is to select the best algorithm and its hyper-parameters simultaneously. Previously, the hyper-parameters of all algorithms are joint as a single search space, which is not only huge but also redundant, because many dimensions of hyper-parameters are irrelevant with the selec…
A method to improve LLMs by automating the construction of a mixture of expert prompts.
Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction. In this work, we present a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for video-based person re-identification, which enables the feature extractor to be aware of …
Optimizes data acquisition in high-dimensional Bayesian optimization.
Study analyzes profitability and efficiency of Chinese banks, finding state-owned banks superior.
Fast method developed for pricing barrier options and joint Lévy process distributions.
New method tackles constrained optimization in multi-fidelity Bayesian optimization.
Neural optimal transport improves multivariate conformal prediction.
We suggest a general approach to quantification of different forms of aleatoric uncertainty in regression tasks performed by artificial neural networks. It is based on the simultaneous training of two neural networks with a joint loss function and a specific hyperparameter that allows for automatically detecting …
We discuss the concepts of energy and mass in relativity. On a finitely extended spatial region, they lead to the notion of quasilocal energy/mass for the boundary 2-surface in spacetime. A new definition was found in [27] that satisfies the positivity, rigidity, and asymptotics properties. The definition makes use of …
Automatically classifying the tissues types of Region of Interest (ROI) in medical imaging has been an important application in Computer-Aided Diagnosis (CAD), such as classification of breast parenchymal tissue in the mammogram, classify lung disease patterns in High-Resolution Computed Tomography (HRCT) etc. Recently…
A model-based optimal experiment design (OED) of nonlinear systems is studied. OED represents a methodology for optimizing the geometry of the parametric joint-confidence regions (CRs), which are obtained in an a posteriori analysis of the least-squares parameter estimates. The optimal design is achieved by using the a…
JAPAN uses flow-based models to create adaptive prediction areas with better coverage guarantees.
GANF uses normalizing flows to detect anomalies in multiple time series.
The paper models systemic risk in European and U.S. banks using factor copulas.
Improved VAE for heavy-tailed data using Student's t-distributions.
Probabilistic representations of movement primitives open important new possibilities for machine learning in robotics. These representations are able to capture the variability of the demonstrations from a teacher as a probability distribution over trajectories, providing a sensible region of exploration and the abili…
New approach simplifies proof of wave equations on black holes.
Unified model estimates landslide hazard combining susceptibility, intensity, and frequency.
This work is an endeavor to develop a deep learning methodology for automated anatomical labeling of a given region of interest (ROI) in brain computed tomography (CT) scans. We combine both local and global context to obtain a representation of the ROI. We then use Relation Networks (RNs) to predict the corresponding …
New scaling laws optimize model size, training, and inference for better performance.
Paper tackles rDR classification and lesion segmentation using self-supervised equivariant learning and attention-based MIL.
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among the sequences, and segments each time series into regions defined by a subset of these behaviors. Using a beta process prior, the size of the…