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.
Improved average distance classifier for HDLSS settings with multiple population differences.
Exact 1-Wasserstein distance between location-scale distributions derived, with privacy effects studied.
Paper proposes MWDE for estimating finite location-scale mixtures.
Study calculates tail risk for various mixture distributions.
Empirical median performs well in estimating location with varying scales.
In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of th…
Study shows one-dimensional location-scale-shape models are flat in Wasserstein geometry.
Space2Vec learns multi-scale spatial representations from grid cell insights.
Novel framework for unbiased confidence estimates in object detection.
This paper argues that a class of Riemannian metrics, called warped metrics, plays a fundamental role in statistical problems involving location-scale models. The paper reports three new results : i) the Rao-Fisher metric of any location-scale model is a warped metric, provided that this model satisfies a natural invar…
Paper analyzes VI for location-scale families, proving robustness guarantees for mean and correlation recovery.
Deep networks adapt to function regularity and data distribution.
Identifying causal direction in location-scale noise models with hidden variables
Differentially private log-location-scale regression models improve privacy in statistical analysis.
ELU algorithm improves on EM for over-specified Gaussian mixtures.
Paper proposes a k-NN classifier for detecting spike-and-wave seizures in EEG.
Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a significant challenge to decentralized learning because their different contexts result in significant data distribution skew across devices/locatio…
A main goal of regression is to derive statistical conclusions on the conditional distribution of the output variable Y given the input values x. Two of the most important characteristics of a single distribution are location and scale. Support vector machines (SVMs) are well established to estimate location functions …
Behaviors of several laboratory animals can be modeled as sequences of stereotyped behaviors, or behavioral motifs. However, identifying such motifs is a challenging problem. Behaviors have a multi-scale structure: the animal can be simultaneously performing a small-scale motif and a large-scale one (e.g. \textit{chewi…
We introduce a wavelet-domain functional analysis of variance (fANOVA) method based on a Bayesian hierarchical model. The factor effects are modeled through a spike-and-slab mixture at each location-scale combination along with a normal-inverse-Gamma (NIG) conjugate setup for the coefficients and errors. A graphical mo…
The nearest neighbor classifier fails in high dimensions, leading to this study.
A new framework uses multi-agent reinforcement learning for evaluating policies in two-sided markets.
A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations by taking as input the streamwise and spanwise wall-shear-stress planes in a turbulent open channel flow. The training data are generated by performing a direct numerical simulation (DNS) at a f…
In the brain, learning signals change over time and synaptic location, and are applied based on the learning history at the synapse, in the complex process of neuromodulation. Learning in artificial neural networks, on the other hand, is shaped by hyper-parameters set before learning starts, which remain static through…
Study identifies and estimates causal LSNM models, proving feature maps are consistent.
In this article we extend a euclidean result of David and Semmes to the Heisenberg group by giving a sufficient condition for a -Ahlfors-regular subset to have big pieces of bilipschitz images of subsets of . This Carleson type condition measures how well the set can be approximated by the Heisenberg -plane…
SE-KGE embeds spatial data into KGs for better spatial reasoning.
We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on i.i.d. samples are known to have lower accuracy than the classical error. We investigate whether the Expectation-Maximization (EM) algorithm also converges slowly for these m…
Meta-SAGE improves deep RL scalability for CO tasks by adapting pre-trained models to larger-scale problems.
We are interested in learning customers' video preferences from their historic viewing patterns and geographical location. We consider a Bayesian latent factor modeling approach for this task. In order to tune the complexity of the model to best represent the data, we make use of Bayesian nonparameteric techniques. We …
NAMLSS models provide interpretable neural regression for location, scale, and shape.
Guarantees convergence for black-box variational inference without modifications.
Multidimensional data have become ubiquitous and are frequently encountered in situations where the information is aggregated over multiple data atoms. The aggregation can be over time or other features, such as geographical location. We often have access to multiple aggregated views of the same data, each aggregated i…
A new TwinGP framework for efficient large-scale GP modeling.
This paper tackles denoising of complex measures using optimal transport and curvature analysis.
Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identification do not capitalize on this separation of 'what' and 'where'. Learning deep convolutional feature spaces that are shared among many neuro…
Mirror flow in shallow neural networks shows similar implicit bias to gradient flow, with key differences in curvature penalties.
Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.
Detecting a specific horizon in seismic images is a valuable tool for geological interpretation. Because hand-picking the locations of the horizon is a time-consuming process, automated computational methods were developed starting three decades ago. Older techniques for such picking include interpolation of control po…
A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a significant role in enhancing the performance of location-based forecasting models. In this work, we investigate two widely used tessellation s…
Optimizes black-box functions with varying costs across multiple sources.
Interpretable ML models predict recidivism as well as non-interpretable methods and are more fair.
This paper studies identifiability and convergence behaviors for parameters of multiple types in finite mixtures, and the effects of model fitting with extra mixing components. First, we present a general theory for strong identifiability, which extends from the previous work of Nguyen [2013] and Chen [1995] to address…
Unified framework detects shifts in climate boundaries using GP regression and MAD test.
The paper develops a neural network-based method for detecting change points in large-scale time-evolving data.
SkewD robustly discovers causal relationships in skewed noise models.
Study uniform rates for estimating Gaussian mixtures without separation assumption.
We propose to model the fixation locations of the human eye when observing a still image by a Markovian point process in R 2 . Our approach is data driven using k-means clustering of the fixation locations to identify distinct salient regions of the image, which in turn correspond to the states of our Markov chain. Bay…