Zobrazeno 1 - 10
of 16 202
pro vyhledávání: '"Evans, David A."'
Autor:
Knauer, Ricardo, Koddenbrock, Mario, Wallsberger, Raphael, Brisson, Nicholas M., Duda, Georg N., Falla, Deborah, Evans, David W., Rodner, Erik
Large language models (LLMs) provide powerful means to leverage prior knowledge for predictive modeling when data is limited. In this work, we demonstrate how LLMs can use their compressed world knowledge to generate intrinsically interpretable machi
Externí odkaz:
http://arxiv.org/abs/2409.18594
Non-equilibrium clustering and percolation are investigated in an archetypal model of two-dimensional active matter using dynamic simulations of self-propelled Brownian repulsive particles. We concentrate on the single-phase region up to moderate lev
Externí odkaz:
http://arxiv.org/abs/2409.04141
Large language models (LLMs) are now being considered and even deployed for applications that support high-stakes decision-making, such as recruitment and clinical decisions. While several methods have been proposed for measuring bias, there remains
Externí odkaz:
http://arxiv.org/abs/2408.01285
Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at
Externí odkaz:
http://arxiv.org/abs/2407.04730
Membership inference attacks aim to infer whether an individual record was used to train a model, serving as a key tool for disclosure auditing. While such evaluations are useful to demonstrate risk, they are computationally expensive and often make
Externí odkaz:
http://arxiv.org/abs/2406.11544
Serious privacy concerns arise with the use of patient data in rule-based clinical decision support systems (CDSS). The goal of a privacy-preserving CDSS is to learn a population ruleset from individual clients' local rulesets, while protecting the p
Externí odkaz:
http://arxiv.org/abs/2405.09721
Autor:
Long, Minjun, Evans, David
Publikováno v:
PoPETS 2024
While third-party cookies have been a key component of the digital marketing ecosystem for years, they allow users to be tracked across web sites in ways that raise serious privacy concerns. Google has proposed the Privacy Sandbox initiative to enabl
Externí odkaz:
http://arxiv.org/abs/2405.08102
Autor:
Evans, David E., Jones, Corey
We consider the problem of building non-invertible quantum symmetries (as characterized by actions of unitary fusion categories) on noncommutative tori. We introduce a general method to construct actions of fusion categories on inductive limit C*-alg
Externí odkaz:
http://arxiv.org/abs/2404.14466
Statistical fairness stipulates equivalent outcomes for every protected group, whereas causal fairness prescribes that a model makes the same prediction for an individual regardless of their protected characteristics. Counterfactual data augmentation
Externí odkaz:
http://arxiv.org/abs/2404.00463
Autor:
Duan, Michael, Suri, Anshuman, Mireshghallah, Niloofar, Min, Sewon, Shi, Weijia, Zettlemoyer, Luke, Tsvetkov, Yulia, Choi, Yejin, Evans, David, Hajishirzi, Hannaneh
Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model's training data. Despite extensive research on traditional machine learning models, there has been limited work studying MIA on the pr
Externí odkaz:
http://arxiv.org/abs/2402.07841