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.
IGNNK uses GNN for spatiotemporal kriging, improving scalability and transferability.
Random forests perform bootstrap-aggregation by sampling the training samples with replacement. This enables the evaluation of out-of-bag error which serves as a internal cross-validation mechanism. Our motivation lies in using the unsampled training samples to improve each decision tree in the ensemble. We study the e…
Improved LOO cross-validation for function approximation.
Proposes a model to handle mobile health data with irregular measurements.
Study calculates tail risk for various mixture distributions.
This paper describes the data collection effort that is part of the project Sprekend Nederland (The Netherlands Talking), and discusses its potential use in Automatic Accent Location. We define Automatic Accent Location as the task to describe the accent of a speaker in terms of the location of the speaker and its hist…
Exact 1-Wasserstein distance between location-scale distributions derived, with privacy effects studied.
Twitter is recently being used during crises to communicate with officials and provide rescue and relief operation in real time. The geographical location information of the event, as well as users, are vitally important in such scenarios. The identification of geographic location is one of the challenging tasks as the…
Study shows one-dimensional location-scale-shape models are flat in Wasserstein geometry.
Proposes a privacy framework for location traces under conditional priors.
We introduce an algorithm to locate contours of functions that are expensive to evaluate. The problem of locating contours arises in many applications, including classification, constrained optimization, and performance analysis of mechanical and dynamical systems (reliability, probability of failure, stability, etc.).…
Empirical median performs well in estimating location with varying scales.
A game theory study on optimal hiding and searching strategies in discrete locations.
Paper proposes a method to locate power grid recordings using ENF sequences.
We study the structure of locational marginal prices in day-ahead and real-time wholesale electricity markets. In particular, we consider the case of two North American markets and show that the price correlations contain information on the locational structure of the grid. We study various clustering methods and intro…
Optimizes black-box functions with varying costs across multiple sources.
New risk class defined based on loss location and deviation.
Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users' mov…
Paper proposes MWDE for estimating finite location-scale mixtures.
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…
MPE models traffic trajectory data to predict next locations.
New method makes neural networks more resilient to location-optimized adversarial patches.
Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to orchestrate traffic flows in cities. For optimal decision making and supervision, traffic centres…
ELU algorithm improves on EM for over-specified Gaussian mixtures.
Study shows accuracy of neural networks depends more on error location than percentage of error.
We consider the problem of locating a point-source heart arrhythmia using data from a standard diagnostic procedure, where a reference catheter is placed in the heart, and arrival times from a second diagnostic catheter are recorded as the diagnostic catheter moves around within the heart. We model this situation as a …
We propose a probabilistic generative model for unsupervised learning of structured, interpretable, object-based representations of visual scenes. We use amortized variational inference to train the generative model end-to-end. The learned representations of object location and appearance are fully disentangled, and ob…
This paper examines the problem of locating outlier columns in a large, otherwise low-rank matrix, in settings where {}{the data} are noisy, or where the overall matrix has missing elements. We propose a randomized two-step inference framework, and establish sufficient conditions on the required sample complexities und…
In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time-height cloud locations with a fully convolutional network (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with image-level annotati…
MLM models match or exceed RN in generating wind power time series without location info.
Predicts user next location using CDR data.
Identifying causal direction in location-scale noise models with hidden variables
Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.
We improve maximum likelihood for location estimation in finite samples.
Study predicts climate data at distant locations using machine learning.
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 …
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…
Transformer attention layers solve single-location regression tasks.
A new method trains deep networks by separating weight locations from values.
An important task in structural design is to quantify the structural performance of an object under the external forces it may experience during its use. The problem proves to be computationally very challenging as the external forces' contact locations and magnitudes may exhibit significant variations. We present an e…
Solves the film scheduling and staggered showtimes problem for movie theaters.
Improved average distance classifier for HDLSS settings with multiple population differences.
Proposes DR-ME test for interpretable distributional treatment effects.
Paper tackles online facility location with user requests and provides a quasi-linear time algorithm.
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light lev…
DRCD identifies causal direction between continuous and discrete variables using density ratio monotonicity.
Differentially private log-location-scale regression models improve privacy in statistical analysis.
Meta-data from photo-sharing websites such as Flickr can be used to obtain rich bag-of-words descriptions of geographic locations, which have proven valuable, among others, for modelling and predicting ecological features. One important insight from previous work is that the descriptions obtained from Flickr tend to be…