arXiv research

New insights link diverse statistical problems via secret leakage planted clique.

Develops methods to estimate high rank tensors from noisy data.

This article is the rejoinder for the paper "Probabilistic Integration: A Role in Statistical Computation?" to appear in Statistical Science with discussion. We would first like to thank the reviewers and many of our colleagues who helped shape this paper, the editor for selecting our paper for discussion, and of cours…

Statistical-computational gap found in aligning multiple Gaussian graphs.

Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent…

Estimation under missing data shows computational and statistical limits for Gaussian data.

Improved statistical computation through efficient matrix sampling.

Paper studies statistical-computational trade-offs in tensor PCA and related problems.

New methods solve tensor-on-tensor regression with unknown rank, revealing benefits of over-parameterization.

Estimates isotonic functions under unknown permutations, achieving optimal statistical and computational efficiency.

New computational lower bounds for clustering and related problems.

Noise Sensitivity Exponent controls statistical-computational gaps in learning.

New study shows limits of low-degree algorithms in finding large independent sets in sparse hypergraphs.

Greedy training of recursive partitioning estimators faces a computational barrier when the true function doesn't satisfy a specific property.

New method uses almost orthonormal bases to prove low-degree lower bounds in complex statistical models.

TSCI estimates treatment effects using machine learning and data-adaptive methods for invalid instruments.

Lower bounds show density estimation requires linear samples or query time.

A statistical framework for removing unwanted data domains in machine learning.

A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled. This raises several statistical challenges, including the design of statistical methods that enable the coherent propagation of probabilities through a (possibly dete…

New method explains computational barriers in high-dimensional statistical models.

Unified approach to tensor PCA and related problems using tensor cumulants.

Survey on using low-degree polynomials to assess statistical tasks complexity.

Random Fourier features (RFF) represent one of the most popular and wide-spread techniques in machine learning to scale up kernel algorithms. Despite the numerous successful applications of RFFs, unfortunately, quite little is understood theoretically on their optimality and limitations of their performance. Only recen…

New insights into statistical and computational limits for mixed sparse linear regression.

EFI automates statistical inference for big data.

Missing data imputation (MDI) is a fundamental problem in many scientific disciplines. Popular methods for MDI use global statistics computed from the entire data set (e.g., the feature-wise medians), or build predictive models operating independently on every instance. In this paper we propose a more general framework…

How should statistical procedures be designed so as to be scalable computationally to the massive datasets that are increasingly the norm? When coupled with the requirement that an answer to an inferential question be delivered within a certain time budget, this question has significant repercussions for the field of s…

Study wSAA for contextual decisions, improving uncertainty quantification under computational constraints.

This paper studies node embeddings of networks, revealing their geometric properties.

The paper tackles optimal level set estimation in crowdsourcing and tournaments.

Paper proposes efficient methods for high-order clustering in tensor block models.

New algorithm recovers sparse binary vectors from generalized linear measurements efficiently.

First DP algorithm for Wasserstein barycenters on private data.

We study the generalization properties of ridge regression with random features in the statistical learning framework. We show for the first time that $O(1/\sqrt{n})$ learning bounds can be achieved with only $O(\sqrt{n}\log n)$ random features rather than $O({n})$ as suggested by previous results. Further, we prove fa…

This paper develops several average-case reduction techniques to show new hardness results for three central high-dimensional statistics problems, implying a statistical-computational gap induced by robustness, a detection-recovery gap and a universality principle for these gaps. A main feature of our approach is to ma…

Methods to find correlation among variables are of interest to many disciplines, including statistics, machine learning, (big) data mining and neurosciences. Parameters that measure correlation between two variables are of limited utility when used with multiple variables. In this work, I propose a simple criterion to …

Deep learning identifies functional primitives in stroke rehabilitation.

Study potential computational gaps in symmetric binary perceptrons using fl-RDT.

Efficiently transforms Gaussian data to simulate various target distributions.

We consider two closely related problems: planted clustering and submatrix localization. The planted clustering problem assumes that a random graph is generated based on some underlying clusters of the nodes; the task is to recover these clusters given the graph. The submatrix localization problem concerns locating hid…

ParK efficiently solves kernel ridge regression for large datasets.

In the past decade, sparse principal component analysis has emerged as an archetypal problem for illustrating statistical-computational tradeoffs. This trend has largely been driven by a line of research aiming to characterize the average-case complexity of sparse PCA through reductions from the planted clique (PC) con…

New insights into binary perceptron reveal phase transitions and algorithmic thresholds.

Sobolev quantities (norms, inner products, and distances) of probability density functions are important in the theory of nonparametric statistics, but have rarely been used in practice, partly due to a lack of practical estimators. They also include, as special cases, $L^2$ quantities which are used in many applicatio…

This paper connects ultrametric overlap gap properties to parametric RDT for symmetric binary perceptrons.

New data structure identifies close match from multiple distributions.

Bayesian nonparametrics adapt model complexity to diverse datasets.

Paper develops inference methods for low-rank tensors without debiasing.