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.
A new method learns meaningful distances between samples using optimal transport.
Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations. Their relevance for real-life applications strongly hinges on whether that ground metric parameter is suitably chosen. Selecting it adaptively and algorithmically from prior knowledge…
Transportation distances have been used for more than a decade now in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, transportation distances can only prove useful in…
The paper identifies magnetic ground states and their role in determining the conformal class of a surface.
Paper introduces metrics to evaluate missing data imputation without ground truth.
Unsupervised clustering can reproduce categorization systems if features and metrics are correctly selected.
Generative Adversial Networks (GANs) have made a major impact in computer vision and machine learning as generative models. Wasserstein GANs (WGANs) brought Optimal Transport (OT) theory into GANs, by minimizing the -Wasserstein distance between model and data distributions as their objective function. Since then, W…
Enhances trading metrics with financially grounded loss functions.
This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges. The core challenge is that people usually cannot obtain ground-truth explanations of the neural network. To …
The accurate measurement of security metrics is a critical research problem because an improper or inaccurate measurement process can ruin the usefulness of the metrics, no matter how well they are defined. This is a highly challenging problem particularly when the ground truth is unknown or noisy. In contrast to the w…
RealCause provides a realistic benchmark for causal inference.
New method estimates model performance bounds without ground truth labels.
Study validates metrics for offline MBO using diffusion models.
New method accounts for uncertainty in medical AI evaluations.
Interferometric Synthetic Aperture Radar (InSAR) imagery for estimating ground movement, based on microwaves reflected off ground targets is gaining increasing importance in remote sensing. However, noise corrupts microwave reflections received at satellite and contaminates the signal's wrapped phase. We introduce Conv…
The study categorizes reward errors in reinforcement learning, finding some can be beneficial.
Proper holomorphic isometries between Bergman domains are biholomorphisms.
Ranking a set of objects involves establishing an order allowing for comparisons between any pair of objects in the set. Oftentimes, due to the unavailability of a ground truth of ranked orders, researchers resort to obtaining judgments from multiple annotators followed by inferring the ground truth based on the collec…
New metric improves latent dynamics inference from neural data.
Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by assessing the relationship between the learnt and the ground truth graph. However, there is no agreed scoring metric to determine this relationsh…
Proposes a method to choose thresholds for LLM evaluation metrics.
Polymarket-v1 Database tracks 1.2B trades across 1.3M markets with 100% ground-truth direction.
Proposes MCC-F1 curve for better binary classification evaluation.
We define a novel class of distances between statistical multivariate distributions by modeling an optimal transport problem on their marginals with respect to a ground distance defined on their conditionals. These new distances are metrics whenever the ground distance between the marginals is a metric, generalize both…
Proposes a method to align language and image data.
Evaluates change point detection algorithms on real-world data.
Generative model synthesizes earthquake acceleration data.
On the ground of origins of the theory of Lie groups and Lie algebras, their (co)adjoint representations, and the Pontryagin maximum principle for the time-optimal problem are given an independent foundation for methods of geodesic vector field to search for normal geodesics of left-invariant (sub-)Finsler metrics on L…
Statistical analysis of Diffusion Tensor Imaging (DTI) data requires a computational framework that is both numerically tractable (to account for the high dimensional nature of the data) and geometric (to account for the nonlinear nature of diffusion tensors). Building upon earlier studies that have shown that a Rieman…
New framework to test neural network representation similarity measures.
TemperatureGAN generates hourly atmospheric temperature data with high fidelity.
We present a novel algorithm that predicts the probability that the time derivative of the horizontal component of the ground magnetic field exceeds a specified threshold at a given location. This quantity provides important information that is physically relevant to Geomagnetically Induced Currents (GIC), whic…
Consider a sample of points taken i.i.d from a submanifold of Euclidean space. We show that there is a way to estimate the Ricci curvature of with respect to the induced metric from the sample. Our method is grounded in the notions of Carré du Champ for diffusion semi-groups, the theory of Empirical process…
A nonnegative number d_infinity, called asymptotic dimension, is associated with any metric space. Such number detects the asymptotic properties of the space (being zero on bounded metric spaces), fulfills the properties of a dimension, and is invariant under rough isometries. It is then shown that for a class of open …
Study on ground states of semilinear elliptic equations with various potential wells.
Ground-A-Video edits videos without training, preserving intended changes.
A new method matches measures across different spaces using cost-regularized optimal transport.
We present a method that "meta" classifies whether seg-ments predicted by a semantic segmentation neural networkintersect with the ground truth. For this purpose, we employ measures of dispersion for predicted pixel-wise class probability distributions, like classification entropy, that yield heat maps of the input sce…
Metrics assess uncertainty structure and distribution for regression models.
Nonlinear dimensionality reduction methods are a popular tool for data scientists and researchers to visualize complex, high dimensional data. However, while these methods continue to improve and grow in number, it is often difficult to evaluate the quality of a visualization due to a variety of factors such as lack of…
Recent work in distance metric learning has focused on learning transformations of data that best align with provided sets of pairwise similarity and dissimilarity constraints. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similar…
New algorithms sample from complex path measures using neural networks.
Paper formalizes anti-discrimination law in automated systems.
We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation. First, we characterize the concept "disentangled representations" used in supervised and unsupervised methods along three dimensions-in…
This research detects and identifies human-made objects in 3D point clouds using novel methods.
CREAM models enable concept-grounded predictions and interpretability.
Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial…
Classification of ground state solutions to critical Dirac equation on spheres.