A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Reduces false positives in classifying rare online platforms.
Paper reduces false positives in lung nodule detection using deep learning on point clouds.
Generating large quantities of quality labeled data in medical imaging is very time consuming and expensive. The performance of supervised algorithms for various tasks on imaging has improved drastically over the years, however the availability of data to train these algorithms have become one of the main bottlenecks f…
A new metric, Weighted Regret, unifies FDR and power evaluation in online multiple testing.
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adh…
FairCal improves face verification accuracy while making results fairer.
We present a powerful new loss function and training scheme for learning binary hash functions. In particular, we demonstrate our method by creating for the first time a neural network that outperforms state-of-the-art Haar wavelets and color layout descriptors at the task of automated scene matching. By accurately rel…
AutoYara generates effective Yara rules faster than humans.
Probabilistic graphical models are graphical representations of probability distributions. Graphical models have applications in many fields including biology, social sciences, linguistic, neuroscience. In this paper, we propose directed acyclic graphs (DAGs) learning via bootstrap aggregating. The proposed procedure i…
The paper reviews techniques for detecting errors in semantic segmentation models.
New method calibrates false detection rates in sequential change detection.
Study controls error rates of binary classifiers using hypothesis testing.
PatternLocal improves XAI for non-linear models by suppressing suppressor variables.
We consider the problem of estimating the set of all inputs that leads a system to some particular behavior. The system is modeled by an expensive-to-evaluate function, such as a computer experiment, and we are interested in its excursion set, i.e. the set of points where the function takes values above or below some p…
A statistical test controls false positives in anomaly localization using diffusion models.
New method improves feature selection by integrating stability paths.
Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents. It is particularly challenging to maintain low false reject rate in real world conditions where there is (a) ambient noise from external sources s…
Nonparametric IPSS selects features with false discovery control.
We address the problem of non-parametric multiple model comparison: given candidate models, decide whether each candidate is as good as the best one(s) or worse than it. We propose two statistical tests, each controlling a different notion of decision errors. The first test, building on the post selection inference…
Algorithm reconstructs triangle-free networks from data, certifying correctness.
Framework uses human feedback to safely set OOD detection thresholds, reducing false positives.
We propose an adaptive sampling approach for multiple testing which aims to maximize statistical power while ensuring anytime false discovery control. We consider distributions whose means are partitioned by whether they are below or equal to a baseline (nulls), versus above the baseline (actual positives). In addi…
New algorithm for adaptive experimental design in scientific settings.
In high dimensional settings where a small number of regressors are expected to be important, the Lasso estimator can be used to obtain a sparse solution vector with the expectation that most of the non-zero coefficients are associated with true signals. While several approaches have been developed to control the inclu…
Develops a new criterion for subgroup fairness in algorithmic decision support.
The commercialization of deep learning creates a compelling need for intellectual property (IP) protection. Deep neural network (DNN) watermarking has been proposed as a promising tool to help model owners prove ownership and fight piracy. A popular approach of watermarking is to train a DNN to recognize images with ce…
Proposes cost-sensitive feature selection for SVMs.
New algorithm balances user reward and statistical inference by mixing TS with UR based on difference size.
Benchmarking recursive collapse claims with a new framework under false-positive control.
Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohi…
New method improves false-/true-positive-rate estimation in fraud detection with noisy labels.
This work improved clustering methods by analyzing various datasets and dendrograms.
Paper estimates FPR of Bayes classifier using soft labels.
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain. In this work, we focus on weakly supervised localization (WSL) where a model is trained to classify an image and localize regi…
Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model. This work focuses on uni…
Transformer learns representations from time series data for money laundering detection.
Algorithm detects concept drift and adapts models in streaming data.
New findings control FDR for online testing methods under positive dependence.
Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. In this paper we show that the sample complexity of CNNs can be significantly improved by using 3D roto-translation group convolutions (G-Convs) instead of the more conv…
A new method detects changes in data sequences by comparing backward and forward confidence sequences.
The paper examines how machine learning tools in justice settings can unfairly affect different racial groups.
Bottlenecks of binary classification from positive and unlabeled data (PU classification) are the requirements that given unlabeled patterns are drawn from the test marginal distribution, and the penalty of the false positive error is identical to the false negative error. However, such requirements are often not fulfi…
In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the important variables with few errors, if any. This paper shows that in a regime of linear sparsity---meaning that the fraction of variables with a n…
Private online FDR control for adaptive testing under differential privacy.
Approach collects missing outcomes to improve fairness in classification.
Cheap permutation tests speed up distribution testing without sacrificing accuracy.
We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both po…
A genome-wide association study (GWAS) correlates marker variation with trait variation in a sample of individuals. Each study subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here we assume that subjects are unrelated and collected at random and that trait values are n…