arXiv research

SAFLe solves federated learning's trade-off between non-linearity and scalability.

Paper proposes scalable algorithm to estimate intervention targets in linear models.

Developing stable and scalable probabilistic ODE solvers for stiff and high-dimensional problems.

Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed. A standard solution is matrix factorization, which predicts unobserved entries as linear combinations of latent variables. We g…

We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting algorithm scales with the number of samples $n$ in th…

New classifier combines locally linear kernels for fast and accurate non-linear classification.

A scalable Bayesian linear regression framework for spatial data.

Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization. Typically, BO is powered by a Gaussian process (GP), whose algorithmic complexity is cubic in the number of evaluations. Hence, GP-based BO cannot leverage large amounts of past or related function evaluations, for e…

This dissertation advances scalable Gaussian processes using iterative methods and pathwise conditioning.

A scalable model for high-dimensional longitudinal data.

Change detection in multivariate time series has applications in many domains, including health care and network monitoring. A common approach to detect changes is to compare the divergence between the distributions of a reference window and a test window. When the number of dimensions is very large, however, the naive…

New method reduces SBL complexity from cubic to linear, improving scalability.

A scalable method for training prediction models in predict-then-optimize

Ensemble++ uses shared-factor ensembles to scale Thompson Sampling for linear and nonlinear bandits.

This paper tackles efficient and scalable estimation of a complex model involving stochastic linear combinations of non-linear regressions.

Ribbon: Scalable Approximation and Robust Uncertainty Quantification

New scalable algorithm for non-negative linear regression with entropy-regularized OT loss.

SASE improves attributed graph clustering for large graphs with linear time and space complexity.

Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing

Graph embedding learns low-dimensional representations for nodes in a graph and effectively preserves the graph structure. Recently, a significant amount of progress has been made toward this emerging research area. However, there are several fundamental problems that remain open. First, existing methods fail to preser…

As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization. The cubic complexity of standard GP however leads to poor scalability, which poses challenges in the era of big data. Hen…

TSRGA scales multivariate linear regression for feature-distributed data.

We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often mo…

Linear mixed models (LMMs) are used extensively to model dependecies of observations in linear regression and are used extensively in many application areas. Parameter estimation for LMMs can be computationally prohibitive on big data. State-of-the-art learning algorithms require computational complexity which depends …

A scalable method for accurate inference of low-dimensional parameters in high-dimensional linear regression.

A scalable method for Bayesian inference in large linear models.

PHP connects to ReLU neural networks for scalable Bayesian inference.

L0Learn solves sparse learning problems with millions of features.

Given observations of a physical system, identifying the underlying non-linear governing equation is a fundamental task, necessary both for gaining understanding and generating deterministic future predictions. Of most practical relevance are automated approaches to theory building that scale efficiently for complex sy…

Efficient Bayesian LMM framework for high-dimensional longitudinal data.

Approximate linear programming (ALP) represents one of the major algorithmic families to solve large-scale Markov decision processes (MDP). In this work, we study a primal-dual formulation of the ALP, and develop a scalable, model-free algorithm called bilinear $π$ learning for reinforcement learning when a sampling or…

In applications of Gaussian processes where quantification of uncertainty is of primary interest, it is necessary to accurately characterize the posterior distribution over covariance parameters. This paper proposes an adaptation of the Stochastic Gradient Langevin Dynamics algorithm to draw samples from the posterior …

We detail distributed algorithms for scalable, secure multiparty linear regression and feature selection at essentially the same speed as plaintext regression. While the core geometric ideas are simple, the recognition of their broad utility when combined is novel. Our scheme opens the door to efficient and secure geno…

Generalized additive models (GAMs) are a widely used class of models of interest to statisticians as they provide a flexible way to design interpretable models of data beyond linear models. We here propose a scalable and well-calibrated Bayesian treatment of GAMs using Gaussian processes (GPs) and leveraging recent adv…

Integrates neural encoders into GLMMs for multimodal data analysis.

Scalable method completes ill-conditioned matrices from few samples.

We show that fundamental learning tasks, such as finding an approximate linear separator or linear regression, require memory at least \emph{quadratic} in the dimension, in a natural streaming setting. This implies that such problems cannot be solved (at least in this setting) by scalable memory-efficient streaming alg…

A new method for efficient causal structure learning at scale.

Can we effectively learn a nonlinear representation in time comparable to linear learning? We describe a new algorithm that explicitly and adaptively expands higher-order interaction features over base linear representations. The algorithm is designed for extreme computational efficiency, and an extensive experimental …

New algorithm discovers causal graphs efficiently from observational data.

New method samples DPPs efficiently without downsampling or low-rank approximations.

This work extends VQR to non-linear cases and provides scalable solvers.

We report an exact likelihood computation for Linear Gaussian Markov processes that is more scalable than existing algorithms for complex models and sparsely sampled signals. Better scaling is achieved through elimination of repeated computations in the Kalman likelihood, and by using the diagonalized form of the state…

A scalable PyTorch framework for non-crossing quantile regression.

SG-PALM learns interpretable tensor models for high-dimensional data.

This paper speeds up iterative GP inference with warm starting.

Develops scalable Bayesian inference methods for neural networks.

We present a windowed technique to learn parsimonious time-varying autoregressive models from multivariate timeseries. This unsupervised method uncovers interpretable spatiotemporal structure in data via non-smooth and non-convex optimization. In each time window, we assume the data follow a linear model parameterized …