arXiv research

New method for valid and exact statistical inference of multi-dimensional change-points.

The study examines conditions for achieving a simple lower bound in estimating mean from samples.

Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a Rényi differentially private framework for boundi…

Active learning reduces SP calculations by 90%.

A new method for spotting symbols in CAD images reduces annotation costs and improves accuracy.

Method locates equilibria on unknown Riemannian manifolds using iterative sampling and parallel transport.

Sparse Gaussian Processes improve scalability by learning inducing points from data.

Study reveals properties of local minima in ReLU networks.

Study predicts climate data at distant locations using machine learning.

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…

Adaptive selection of IPs improves online GP performance.

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 …

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…

Study shows surfaces sound the same everywhere if they have a transitive isometry group.

We present a private learner for halfspaces over an arbitrary finite domain $X\subset \mathbb{R}^d$ with sample complexity $mathrm{poly}(d,2^{\log^*|X|})$. The building block for this learner is a differentially private algorithm for locating an approximate center point of $m>\mathrm{poly}(d,2^{\log^*|X|})$ points -- a…

New algorithm learns halfspaces almost optimally with fewer queries.

We introduce a novel geometry-oriented methodology, based on the emerging tools of topological data analysis, into the change point detection framework. The key rationale is that change points are likely to be associated with changes in geometry behind the data generating process. While the applications of topological …

In this paper, we are interested in the location of conjugate points along a geodesic in the volumorphism group of a compact three-dimensional manifold without boundary (the configuration space of an ideal fluid). As shown in the author's previous work, these are typically pathological, i.e., they can occur in clusters…

Study identifies change points in piecewise constant reward functions with fixed exploration budget.

The paper studies singularities of pedal curves of hyperbolic frontals.

The paper develops methods to accurately locate change points in high-dimensional mean shift models.

Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations. When Bayesian methods are considered, the standard practice is to infer the posterior distribution of the change-point locations. However, for complex models (high-dimensional or heterogeneous), it is n…

New AMP algorithm detects change points in high-dimensional GLMs.

A new method detects change points in time series with conceptors.

We discuss several kinds of Willmore surfaces of flat normal bundle in this paper. First we show that every S-Willmore surface with flat normal bundle in $S^n$ must locate in some $S^3\subset S^n$, from which we characterize Clifford torus as the only non-equatorial homogeneous minimal surface in $S^n$ with flat normal…

In this paper we propose a method of obtaining points of extreme overfitting - parameters of modern neural networks, at which they demonstrate close to 100 % training accuracy, simultaneously with almost zero accuracy on the test sample. Despite the widespread opinion that the overwhelming majority of critical points o…

Bayesian Context Trees improve change-point detection in discrete data.

The paper develops a neural network-based method for detecting change points in large-scale time-evolving data.

SGD trains ReLU networks to implement piecewise linear maps with at most 3 knot points.

Attempts to build a discrete theory for rational maps on the sphere via circle packing have foundered on discretization effects in locating branch points. The authors remove this impediment by introducing generalized branch points. A generalized branch point need no longer be attached to an individual circle, but with …

Ghost points affect stability in finite difference schemes for diffusion equations.

Study calculates tail risk for various mixture distributions.

New tiles allow efficient knot mosaics for small knots.

In this paper we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behaviour. Then, we explain how we model main traffic flow patterns by a mixture of 2d Gaussian distribution…

New method infers centromere locations in yeast using Hi-C data.

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…

A new TwinGP framework for efficient large-scale GP modeling.

Improved Gaussian process inference for spatio-temporal data.

This article is concerned with Gaussian process quadratures, which are numerical integration methods based on Gaussian process regression methods, and sigma-point methods, which are used in advanced non-linear Kalman filtering and smoothing algorithms. We show that many sigma-point methods can be interpreted as Gaussia…

Two derivations of PCA for distributional data.

Assessing the predictive accuracy of black box classifiers is challenging in the absence of labeled test datasets. In these scenarios we may need to rely on a human oracle to evaluate individual predictions; presenting the challenge to create query algorithms to guide the search for points that provide the most informa…

New method for estimating lead-lag times between non-synchronously observed point processes.

Using transfer entropy, we observed the strength and direction of information flow between stock indices. We uncovered that the biggest source of information flow is America. In contrast, the Asia/Pacific region the biggest is receives the most information. According to the minimum spanning tree, the GSPC is located at…

New method improves spatial prediction validation accuracy.

Post-detection analysis identifies responsible coordinates for multivariate change-points.

The problem of change-point estimation is considered under a general framework where the data are generated by unknown stationary ergodic process distributions. In this context, the consistent estimation of the number of change-points is provably impossible. However, it is shown that a consistent clustering method may …

Deviance Voronoi residuals improve earthquake insurance risk assessment.

This work studies the location estimation problem for a mixture of two rotation invariant log-concave densities. We demonstrate that Least Squares EM, a variant of the EM algorithm, converges to the true location parameter from a randomly initialized point. We establish the explicit convergence rates and sample complex…