arXiv research

Time-aware neural models improve system identification from unevenly sampled data.

This paper tackles robust control of noisy systems with uncertain distributions.

A new classifier improves one-class predictions on unevenly sampled data.

New method compresses large sample data for faster discriminant analysis.

Circadian rhythms influence multiple essential biological activities including sleep, performance, and mood. The dim light melatonin onset (DLMO) is the gold standard for measuring human circadian phase (i.e., timing). The collection of DLMO is expensive and time-consuming since multiple saliva or blood samples are req…

q-VAE extracts disentangled latent spaces for robot control and dynamic systems.

Proposes a real-time DDoS detection framework using machine learning.

Control Contraction Metrics (CCMs) provide a nonlinear controller design involving an offline search for a Riemannian metric and an online search for a shortest path between the current and desired trajectories. In this paper, we generalize CCMs to Finsler geometry, allowing the use of non-Riemannian metrics. We provid…

Flow Matching models help generative models stay within the subspace of real data.

The paper uses neural networks to learn system dynamics from data with Lipschitz regularization.

Two simulation-based methods improve optimal sampling design in systems biology.

In this paper, we exploit minimal sensing information gathered from biologically inspired sensor networks to perform exploration and mapping in an unknown environment. A probabilistic motion model of mobile sensing nodes, inspired by motion characteristics of cockroaches, is utilized to extract weak encounter informati…

We compare and contrast two approaches to validating a trained classifier while using all in-sample data for training. One is simultaneous validation over an organized set of hypotheses (SVOOSH), the well-known method that began with VC theory. The other is withhold and gap (WAG). WAG withholds a validation set, trains…

We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data. Our neural network produces sample embeddings that are motivated by--and are at least as expressive as--spectral clustering. Our training objective, based on the Hilbert Schmidt Information Criterion, can be o…

For large matrix factorisation problems, we develop a distributed Markov Chain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics (SGLD) that we call Parallel SGLD (PSGLD). PSGLD has very favourable scaling properties with increasing data size and is comparable in terms of computational requiremen…

Generative adversarial networks reconstruct MRI images without full data.

Actively sampled data can have very different characteristics than passively sampled data. Therefore, it's promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for im…

SympNets identify Hamiltonian systems from data using linear, activation, and gradient modules.

This paper tackles Bayesian system identification with probabilistic numerical methods.

Paper presents a new way to analyze machine learning generalization without probabilistic assumptions.

This work learns models for population dynamics using variational methods and higher-order quadrature.

Method recovers causal diffusion mechanisms from steady-state data without parametric assumptions.

Neural GDEs improve graph prediction by blending discrete structures and differential equations.

New active learning method uses combinatorial coverage to improve data transfer and reduce bias.

The paper identifies potential adversarial samples near decision boundaries of neural networks.

The paper examines how sampling data affects the performance of submodular maximization.

DSAC improves robustness of imitation learning.

Dimensionality reduction methods, also known as projections, are frequently used for exploring multidimensional data in machine learning, data science, and information visualization. Among these, t-SNE and its variants have become very popular for their ability to visually separate distinct data clusters. However, such…

GINNs combine deep learning with PGMs for physics-based multiscale systems.

Estimates set overlap and similarity using random samples.

Modeling complex systems with multi-resolution data and causal dependencies.

Proposes a method to detect anomalies in multi-subgroup normal data.

Networks are a natural representation of complex systems across the sciences, and higher-order dependencies are central to the understanding and modeling of these systems. However, in many practical applications such as online social networks, networks are massive, dynamic, and naturally streaming, where pairwise inter…

The paper analyzes the generalization performance of spectral clustering algorithms and proposes new methods to improve their effectiveness.

This study compares three portfolio design approaches for stock selection.

Paper proposes $S^2$ConvSCN for robust subspace clustering and classification.

Proposes new attribution methods for trees with regularization.

We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biased towards increasing the importance of features with more potential splits. We show that by appropriately incorporating split-improvement as…

Optimizes experimental designs for intractable models using mutual information bounds.

We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier work proposed prescriptive formulations based on supervised machine learning methods. These prescriptive methods can factor in contextual in…

A comprehensive methodology is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen a priori from physical reasoning. We also show how to allow for non-Gaussian noise and outliers which are typical…

Microbes are everywhere, including in and on our bodies, and have been shown to play key roles in a variety of prevalent human diseases. Consequently, there has been intense interest in the design of bacteriotherapies or "bugs as drugs," which are communities of bacteria administered to patients for specific therapeuti…

For manifold learning, it is assumed that high-dimensional sample/data points are embedded on a low-dimensional manifold. Usually, distances among samples are computed to capture an underlying data structure. Here we propose a metric according to angular changes along a geodesic line, thereby reflecting the underlying …

Jeffreys Flow improves robustness of Boltzmann generators for rare event sampling.

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

We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well as performance for finite-sample data. We present a computationally efficient algorithm, with provable numerical convergence properties, fo…

A significant hurdle for analyzing large sample data is the lack of effective statistical computing and inference methods. An emerging powerful approach for analyzing large sample data is subsampling, by which one takes a random subsample from the original full sample and uses it as a surrogate for subsequent computati…

Randomized algorithm solves vector-valued regression problems with low-rank operators.