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.
Study controls error rates of binary classifiers using hypothesis testing.
A new metric, Weighted Regret, unifies FDR and power evaluation in online multiple testing.
Develops a new criterion for subgroup fairness in algorithmic decision support.
Bayesian model improves categorization of explosions from sparse data.
Proposes cost-sensitive feature selection for SVMs.
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…
AdaDetectGPT improves text authorship detection with statistical guarantees.
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. …
Paper introduces a statistical framework for watermarking LLM-generated text.
Paper estimates FPR of Bayes classifier using soft labels.
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…
In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discriminative voxels, thus improving interpretation of the discovered potential biomarkers. The main difficulty in doing this is an extremely high di…
MI attacks often mislabel non-training samples, making them impractical.
Environmental acoustic sensing involves the retrieval and processing of audio signals to better understand our surroundings. While large-scale acoustic data make manual analysis infeasible, they provide a suitable playground for machine learning approaches. Most existing machine learning techniques developed for enviro…
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…
We study the interplay between sequential decision making and avoiding discrimination against protected groups, when examples arrive online and do not follow distributional assumptions. We consider the most basic extension of classical online learning: "Given a class of predictors that are individually non-discriminato…
A new method for optimizing non-decomposable metrics with constraints.
Study benchmarks label noise detection methods, identifying best practices.
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…
Optimizes quickest detection of drift in Brownian motion with false negatives.
New indefinite false theta functions match homological blocks for a specific 3-manifold.
The paper shows how demographic data can lead to biased predictions, proposing 'Affirmative Information' as a solution.
New methods control false discoveries near the boundary in conformal novelty detection.
PAC-Wrap provides provable guarantees for semi-supervised anomaly detection.
New method controls false discoveries in financial asset pricing.
We introduce the State Classification Problem (SCP) for hybrid systems, and present Neural State Classification (NSC) as an efficient solution technique. SCP generalizes the model checking problem as it entails classifying each state of a hybrid automaton as either positive or negative, depending on whether or not …
We present a convolutional-recurrent neural network architecture with long short-term memory for real-time processing and classification of digital sensor data. The network implicitly performs typical signal processing tasks such as filtering and peak detection, and learns time-resolved embeddings of the input signal. …
New method controls false edge detections in Gaussian graphical models.
New algorithm balances user reward and statistical inference by mixing TS with UR based on difference size.
In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the desired behavior. However, as the trained policy learns to be more successful, the negative examples (the ones produced by the agent) become increasingly similar to expert ones. Desp…
New method identifies causes in time series with latent variables.
New method calibrates false detection rates in sequential change detection.
New bounds for Neyman-Pearson region using -divergences.
Low-rate application layer distributed denial of service (LDDoS) attacks are both powerful and stealthy. They force vulnerable webservers to open all available connections to the adversary, denying resources to real users. Mitigation advice focuses on solutions that potentially degrade quality of service for legitimate…
New models extrapolate false alarms in ASV without new data.
Paper proposes knockoff-based methods to simplify deep neural networks by controlling false discovery rates.
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…
Private online FDR control for adaptive testing under differential privacy.
New method controls FDR for sparse GLMs, identifying positive and negative relationships.
FairCal improves face verification accuracy while making results fairer.
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…
In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human d…
Paper tackles MIAs vulnerability by controlling FDR, providing guarantees on false discoveries.
Online anomaly detection in surveillance videos with false alarm rate bounds.
State-of-the-art approaches for Knowledge Base Completion (KBC) exploit deep neural networks trained with both false and true assertions: positive assertions are explicitly taken from the knowledge base, whereas negative ones are generated by random sampling of entities. In this paper, we argue that random sampling is …
We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive or negative. Our algorithm only observes the true label of an individual if they are given a positive classification. This setting captures …
New algorithm for adaptive experimental design in scientific settings.
The paper examines how machine learning tools in justice settings can unfairly affect different racial groups.