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.
PatternLocal improves XAI for non-linear models by suppressing suppressor variables.
Knowledge bases (KBs) are the backbone of many ubiquitous applications and are thus required to exhibit high precision. However, for KBs that store subjective attributes of entities, e.g., whether a movie is "kid friendly", simply estimating precision is complicated by the inherent ambiguity in measuring subjective phe…
Framework for fair classification with noisy protected attributes and provable guarantees.
Interpretability is an important area of research for safe deployment of machine learning systems. One particular type of interpretability method attributes model decisions to input features. Despite active development, quantitative evaluation of feature attribution methods remains difficult due to the lack of ground t…
New method shows data-driven causal studies can be misleading.
Adversarial sample attacks perturb benign inputs to induce DNN misbehaviors. Recent research has demonstrated the widespread presence and the devastating consequences of such attacks. Existing defense techniques either assume prior knowledge of specific attacks or may not work well on complex models due to their underl…
In this paper, we present a novel attack against authorship attribution of source code. We exploit that recent attribution methods rest on machine learning and thus can be deceived by adversarial examples of source code. Our attack performs a series of semantics-preserving code transformations that mislead learning-bas…
Develops a new criterion for subgroup fairness in algorithmic decision support.
FairCal improves face verification accuracy while making results fairer.
A new method assesses algorithmic fairness using game theory.
New method improves feature selection by integrating stability paths.
A method to control false membership rate in unsupervised mixture models.
DeXposure-Claw supervises decentralized finance risks by grounding LLM decisions in evidence.
Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on …
AXE evaluates explanations to avoid misleading Rashomon set model selection.
A system for supervising decentralized finance risks using LLMs and structured evidence.
Study examines flaws in probing LLMs' knowledge and introduces a new method.
A new method enhances signal recovery with FDR control.
Study explores rumor spread on Twitter using supervised learning.
The paper develops a method to identify LLM-generated text without training.
We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e.g., an occluder in the case of a visual recording. Within this framework, we propose i) a novel algorithm to efficiently separate, i.e., detect and localiz…
The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data. Observational data often contain noisy or missing entries. Moreover, causal inference studies may require unobserved high-level information which needs to be …
Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. We design two lear…
New method controls false discoveries in financial asset pricing.
We decompose the squared price-of-risk premium into three components: intervention-stable premium, confounding wedge, and information loss.
New methods control false discoveries near the boundary in conformal novelty detection.
There are two big unsolved mathematical questions in artificial intelligence (AI): (1) Why is deep learning so successful in classification problems and (2) why are neural nets based on deep learning at the same time universally unstable, where the instabilities make the networks vulnerable to adversarial attacks. We p…
Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or benignware. Parameters of this classifier are typically optimized such that outputs from the model over a set of input samples most closely…
Job transitions and upskilling are common actions taken by many industry working professionals throughout their career. With the current rapidly changing job landscape where requirements are constantly changing and industry sectors are emerging, it is especially difficult to plan and navigate a predetermined career pat…
New method controls false edge detections in Gaussian graphical models.
New indefinite false theta functions match homological blocks for a specific 3-manifold.
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…
Optimizes latency and false alarm probability in change detection problems.
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse. A company that can only observe the interactions between its own customers will generally not be able to accurately estimate its …
Statistical test detects model degradations in optimized language models.
New models extrapolate false alarms in ASV without new data.
A new metric, Weighted Regret, unifies FDR and power evaluation in online multiple testing.
Private online FDR control for adaptive testing under differential privacy.
New method calibrates false detection rates in sequential change detection.
Nonparametric IPSS selects features with false discovery control.
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…
Paper tackles MIAs vulnerability by controlling FDR, providing guarantees on false discoveries.
Knockoffs method selects financial factors, controlling false discoveries.
Paper proposes knockoff-based methods to simplify deep neural networks by controlling false discovery rates.
Nowadays, advanced intrusion detection systems (IDSs) rely on a combination of anomaly detection and signature-based methods. An IDS gathers observations, analyzes behavioral patterns, and reports suspicious events for further investigation. A notorious issue anomaly detection systems (ADSs) and IDSs face is the possib…
Study controls error rates of binary classifiers using hypothesis testing.
New method controls false discoveries in online testing with deadlines.
Efron et al. (2001) proposed empirical Bayes formulation of the frequentist Benjamini and Hochbergs False Discovery Rate method (Benjamini and Hochberg,1995). This article attempts to unify the `two cultures' using concepts of comparison density and distribution function. We have also shown how almost all of the existi…