arXiv research

New algorithms minimize PAC-Bayesian C-Bound for majority voting, leading to scalable and accurate predictors.

Two approaches scale up DNN optimization for diverse edge devices.

The transport literature is dense regarding short-term traffic predictions, up to the scale of 1 hour, yet less dense for long-term traffic predictions. The transport literature is also sparse when it comes to city-scale traffic predictions, mainly because of low data availability. In this work, we report an effort to …

We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design. Predictive Correlation Screening (PCS) implements false positive control on the selected variables, is well suited to small sample sizes, and is scalable to high dimensions. We establish asymptotic bounds f…

We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors fo…

Bayesian Hierarchical Invariant Prediction refines ICP for better scalability and prior integration.

Bayesian $l_0$-regularized least squares is a variable selection technique for high dimensional predictors. The challenge is optimizing a non-convex objective function via search over model space consisting of all possible predictor combinations. Spike-and-slab (a.k.a. Bernoulli-Gaussian) priors are the gold standard f…

GIDS reduces high-dimensional response and predictor spaces, improving interpretability and computational efficiency.

New method selects sparse predictors in large LMMs.

New Krylov subspace methods speed up mixed-effects models with crossed random effects.

PEER tackles multi-response regression with incomplete outcomes efficiently.

BINAS improves neural architecture search with interpretable models.

Develops efficient time series prediction intervals.

New method tackles OOD robustness with a single additional variable.

Study evaluates scalability and real-world impact of disentangled representations.

Gaussian process (GP) models provide a powerful tool for prediction but are computationally prohibitive using large data sets. In such scenarios, one has to resort to approximate methods. We derive an approximation based on a composite likelihood approach using a general belief updating framework, which leads to a recu…

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…

A new method quantizes output space for multi-target regression.

DONNA rapidly finds optimal neural networks across diverse spaces.

A scalable GP model for online uncertainty quantification over graphs.

This paper proposes a general adaptive procedure for budget-limited predictor design in high dimensions called two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS). SPARCS can be applied to high dimensional prediction problems in experimental science, medicine, finance, and engineer…

Proposes bivariate DeepKriging for efficient wind field prediction.

Model predicts increased social unrest during COVID-19 using social media data.

Integrates neural encoders into GLMMs for multimodal data analysis.

Bayesian methods improve inference for cumulative probit models on large datasets.

Variable selection is central to high-dimensional data analysis, and various algorithms have been developed. Ideally, a variable selection algorithm shall be flexible, scalable, and with theoretical guarantee, yet most existing algorithms cannot attain these properties at the same time. In this article, a three-step va…

Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for …

New algorithm finds best subset in high-dimensional data models.

New estimators improve sparse semiparametric additive modeling.

We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features, {The number of features here refers to the number of non-zero entries in the data matrix.} billions of training examples and millions of parameters in an hour using a cluster …

DAG-FM discovers causal relationships from heterogeneous data.

Additive nonparametric regression models provide an attractive tool for variable selection in high dimensions when the relationship between the response and predictors is complex. They offer greater flexibility compared to parametric non-linear regression models and better interpretability and scalability than the non-…

We propose an algorithm, semismooth Newton coordinate descent (SNCD), for the elastic-net penalized Huber loss regression and quantile regression in high dimensional settings. Unlike existing coordinate descent type algorithms, the SNCD updates each regression coefficient and its corresponding subgradient simultaneousl…

The problem of forecasting conditional probabilities of the next event given the past is considered in a general probabilistic setting. Given an arbitrary (large, uncountable) set C of predictors, we would like to construct a single predictor that performs asymptotically as well as the best predictor in C, on any data.…

Artificial Neural Network predicts PM2.5 pollution with low-cost sensors.

Bayesian approach controls FDR in high-dimensional models.

The paper provides convergence guarantees for multicalibration gradient boosting.

New bounds explain deterministic non-smooth deep nets without large Lipschitz constants.

Optimizes predictions for specific tasks using parametrized decision analysis.

This paper proposes a method to reduce complexity in GLMs with categorical predictors.

AI detects heart disease from ECGs with improved interpretability and performance.

The article compares predictor importance in classification problems with categorical outcomes.

Paper proposes a sparse synthetic control method to select important predictors.

This paper presents Sparse Partitioning, a Bayesian method for identifying predictors that either individually or in combination with others affect a response variable. The method is designed for regression problems involving binary or tertiary predictors and allows the number of predictors to exceed the size of the sa…

WeakNAS uses a set of weaker predictors to find top architectures with fewer samples.

Proposes a method to create fair, robust predictors that remain consistent across different scenarios.

In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class. Our goal is to equip the reader with the conceptual foundations of this tool for building new, scalable agents that operate on broad domains.…

This paper continues study, both theoretical and empirical, of the method of Venn prediction, concentrating on binary prediction problems. Venn predictors produce probability-type predictions for the labels of test objects which are guaranteed to be well calibrated under the standard assumption that the observations ar…