arXiv research

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

Develops RES metrics for stable rare-event forecasting evaluation.

Study robust estimation under varying corruption probabilities in data.

Deepfake detection is formulated as a hypothesis testing problem to classify an image as genuine or GAN-generated. A robust statistics view of GANs is considered to bound the error probability for various GAN implementations in terms of their performance. The bounds are further simplified using a Euclidean approximatio…

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

New insights on robust learning under strong noise models.

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

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

This paper focuses on the problem of determining as large a region as possible where a function exceeds a given threshold with high probability. We assume that we only have access to a noise-corrupted version of the function and that function evaluations are costly. To select the next query point, we propose maximizing…

Method selects the best deep learner for time-series prediction using Bayesian networks.

We win EVA2025 by estimating extreme precipitation events using Peaks Over Thresholds and martingale testing.

We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence. We do not ass…

Investigates probability of error in structured thresholding bandit problems.

SyncRank recovers global ranking from noisy comparisons with theoretical guarantees.

Robust learning mixtures of linear regressions improve robustness.

We investigate the impact of available information on the estimation of the default probability within a generalized structural model for credit risk. The traditional structural model where default is triggered when the value of the firm's asset falls below a constant threshold is extended by relaxing the assumption of…

Paper proposes robust methods for estimating optimal treatment rules with censored survival data.

The fundamental group of the $2$-dimensional Linial-Meshulam random simplicial complex $Y_2(n,p)$ was first studied by Babson, Hoffman and Kahle. They proved that the threshold probability for simple connectivity of $Y_2(n,p)$ is about $p\approx n^{-1/2}$. In this paper, we show that this threshold probability is at mo…

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.

Deep networks can handle noisy labels up to a certain threshold.

We consider a variant of the classic Ski Rental online algorithm with applications to machine learning. In our variant, we allow the skier access to a black-box machine-learning algorithm that provides an estimate of the probability that there will be at most a threshold number of ski-days. We derive a class of optimal…

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

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

Sampling is a fundamental problem in computer science and statistics. However, for a given task and stream, it is often not possible to choose good sampling probabilities in advance. We derive a general framework for adaptively changing the sampling probabilities via a collection of thresholds.In general, adaptive samp…

We consider estimating the edge-probability matrix of a network generated from a graphon model when the full network is not observed---only some overlapping subgraphs are. We extend the neighbourhood smoothing (NBS) algorithm of Zhang et al. (2017) to this missing-data set-up and show experimentally that, for a wide ra…

We investigate the probability distributions of the recurrence intervals $τ$ between consecutive 1-min returns above a positive threshold $q>0$ or below a negative threshold $q<0$ of two indices and 20 individual stocks in China's stock market. The distributions of recurrence intervals for positive and negative thresho…

We define the information threshold in Bayesian decision-making.

New method calculates sensitivity of system failure probability.

Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorpora…

We investigate adversarial robustness of Gaussian Process Classification (GPC) models. Given a compact subset of the input space $T\subseteq \mathbb{R}^d$ enclosing a test point $x^*$ and a GPC trained on a dataset $\mathcal{D}$, we aim to compute the minimum and the maximum classification probability for the GPC over …

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…

We present a framework and analysis of consistent binary classification for complex and non-decomposable performance metrics such as the F-measure and the Jaccard measure. The proposed framework is general, as it applies to both batch and online learning, and to both linear and non-linear models. Our work follows recen…

This paper improves conformal prediction to be robust to perturbations.

Bayesian framework proves thresholds for multi-graph alignment feasibility.

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…

Square percolation determines threshold for group divergence in random graphs.

New algorithm robustly optimizes data streams with heavy-tailed or infinite variance samples.

Threshold found for hyperbolicity in random Coxeter groups.

The binary symmetric stochastic block model deals with a random graph of $n$ vertices partitioned into two equal-sized clusters, such that each pair of vertices is connected independently with probability $p$ within clusters and $q$ across clusters. In the asymptotic regime of $p=a \log n/n$ and $q=b \log n/n$ for fixe…

The interplay between computational efficiency and statistical accuracy in high-dimensional inference has drawn increasing attention in the literature. In this paper, we study computational and statistical boundaries for submatrix localization. Given one observation of (one or multiple non-overlapping) signal submatrix…

Bayesian method for estimating inputs leading to specific probability outputs.

Study minimax rates for density estimation under Huber contamination and Besov IPM losses.

We provide high probability finite sample complexity guarantees for hidden non-parametric structure learning of tree-shaped graphical models, whose hidden and observable nodes are discrete random variables with either finite or countable alphabets. We study a fundamental quantity called the (noisy) information threshol…

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

Paper compares credit portfolio risks using robust Bernoulli mixture models.

The paper develops methods to predict the probability of achieving a user goal in a task, ensuring the system alerts when the probability falls below a threshold.

Majority bit estimation in noisy random recursive DAGs.