arXiv research

Paper proposes efficient AL algorithms for optimizing product performance under environmental variability.

This paper improves conformal prediction to be robust to perturbations.

New method provides calibrated feature importance explanations for regression models.

Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.

Robust learning mixtures of linear regressions improve robustness.

Paper presents a probabilistic model to improve LLM cascade performance.

Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicabili…

This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employe…

New robust method for high-dimensional data analysis in imaging studies.

Paper analyzes robust matrix completion with efficient nonconvex method and leave-one-out analysis.

New proof shows neural networks can memorize training data with high accuracy.

A new SSL method uses instance-dependent thresholds to improve accuracy.

New algorithm robust to probabilistic unbounded adversarial attacks in bandit problems.

New algorithm detects communities even with corrupted data, reaching Kesten-Stigum threshold.

This research focuses on invariant probabilistic predictions, showing they are not robust under distribution shifts.

Geometric approach improves probabilistic robustness in neural networks.

We study the problem of robust linear regression with response variable corruptions. We consider the oblivious adversary model, where the adversary corrupts a fraction of the responses in complete ignorance of the data. We provide a nearly linear time estimator which consistently estimates the true regression vector, e…

FedGVI improves FL robustness to model misspecification.

The stochastic block model is one of the oldest and most ubiquitous models for studying clustering and community detection. In an exciting sequence of developments, motivated by deep but non-rigorous ideas from statistical physics, Decelle et al. conjectured a sharp threshold for when community detection is possible in…

New algorithm detects communities near KS threshold with optimal rate, even in noisy conditions.

Efficiently certifies global robustness of large neural networks with probabilistic guarantees.

Neural networks are becoming increasingly prevalent in software, and it is therefore important to be able to verify their behavior. Because verifying the correctness of neural networks is extremely challenging, it is common to focus on the verification of other properties of these systems. One important property, in pa…

We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach…

We consider the problem of clustering noisy high-dimensional data points into a union of low-dimensional subspaces and a set of outliers. The number of subspaces, their dimensions, and their orientations are unknown. A probabilistic performance analysis of the thresholding-based subspace clustering (TSC) algorithm intr…

With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important area of research. However, most of the research literature merely focuses on the \textit{worst-case} setting where the input of the neural …

New algorithm resists contamination in high-dimensional regression with optimal performance.

Study proposes active learning method for estimating robust regions in uncertain function evaluations.

Probabilistic STNs improve image classification and robustness.

PRoA assesses deep learning robustness against practical functional perturbations.

We consider the problem of sparsity-constrained $M$-estimation when both explanatory and response variables have heavy tails (bounded 4-th moments), or a fraction of arbitrary corruptions. We focus on the $k$-sparse, high-dimensional regime where the number of variables $d$ and the sample size $n$ are related through $…

Study robust estimation under varying corruption probabilities in data.

The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings such as compressive sensing, and subset selection in regression, and group testing. In this paper, we take a unified approach to supp…

Develops RES metrics for stable rare-event forecasting evaluation.

This work analyzes CVaR under heavy-tailed data, providing generalization and robustness bounds.

Detects crypto pump-and-dump schemes with a thresholding-based model.

Proposes Likelihood Regret for VAEs to improve OOD detection.

Identifies arms with rewards significantly different from the majority.

LinearAPT optimizes decision-making under resource constraints for a linear threshold problem.

Extends probabilistic approach for Kahler-Einstein metrics on Fano manifolds.

Paper introduces Prob-SSI for robust OMA in noisy data.

The existence of adversarial examples has led to considerable uncertainty regarding the trust one can justifiably put in predictions produced by automated systems. This uncertainty has, in turn, lead to considerable research effort in understanding adversarial robustness. In this work, we take first steps towards separ…

New metrics improve probabilistic forecasting, especially for rare events.

The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process. Such an approximation has not yet been rigorously examined, and standard dictionaries often lead to non-optimal performance when used for computing thresholde…

COLEP improves robustness of conformal prediction via probabilistic circuits.

Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies. These methods are often tailored to target specific data sets with "known" number of clusters. The paper provides a streaming clustering and anomaly detection algorithm that does not require strict arbitrary…

Thresholded Lasso bandit minimizes regret in sparse linear bandits.

Paper shows equivalence between two dividend preference models.

High-dimensional models become unstable when sample size falls below a critical level, leading to a phase transition.