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.
RePAD detects anomalies in streaming time series data in real-time.
Proceed adapts models proactively against concept drift in online time series forecasting.
This research improves capital efficiency and impermanent loss in cryptocurrency markets using multi-token trading pools.
Proposes proactive bed requests to reduce ED boarding and patient wait times.
This paper evaluates targeted data poisoning attacks by focusing on the hardest samples, improving evaluation and defense strategies.
ProEval efficiently estimates AI performance and discovers failures using pre-trained Gaussian Processes.
Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock ma…
HyPV-LEAD detects cryptocurrency anomalies proactively, improving financial security.
Paper analyzes liquidity for everlasting options in DeFi, offering strategies to reduce costs.
In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approa…
Predictive models can fail to generalize from training to deployment environments because of dataset shift, posing a threat to model reliability and the safety of downstream decisions made in practice. Instead of using samples from the target distribution to reactively correct dataset shift, we use graphical knowledge …
Prophet predicts device qualities for FL to reduce training latency.
Climate-contingent finance helps adapt to uncertain climate risks.
Robust policies improve ICU transfer outcomes by predicting patient deterioration.
RODMAN improves ML-based disk failure prediction accuracy in cloud environments.
Proactive DP optimizes privacy and utility in DP-SGD with a fixed privacy budget.
ExpertNet uses noisy labels to improve deep learning robustness.
To predict the employee attrition beforehand and to enable management to take individualized preventive action. Using Ensemble classification modeling techniques and Linear Regression. Model could predict over 91% accurate employee prediction, lead-time in separation and individual reasons causing attrition. Prior inti…
Unified approach detects traffic conflicts across various interactions.
Characterizing the appearance of real-world surfaces is a fundamental problem in multidimensional reflectometry, computer vision and computer graphics. For many applications, appearance is sufficiently well characterized by the bidirectional reflectance distribution function (BRDF). We treat BRDF measurements as sample…
Study uses machine learning to predict rain in Australia.
PAGER detects failures in deep regression models using a new framework.
Classical supervised learning produces unreliable models when training and target distributions differ, with most existing solutions requiring samples from the target domain. We propose a proactive approach which learns a relationship in the training domain that will generalize to the target domain by incorporating pri…
It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly confident model is more likely to be vulnerable to adversarial examples. This work is one of the most proactive approaches taken to date, as…
Enhances patient failure prediction using dynamic survival models.
Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challen…
Recent research has helped to cultivate growing awareness that machine learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively addres…
Analysis finds no evidence of banks managing deposit run risk prior to 2023 Regional Banking Crisis.
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochast…
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, wh…
The behaviors of patients with depression are usually difficult to predict because the patients demonstrate the symptoms of a depressive episode without a warning at unexpected times. The goal of this research is to build algorithms that detect signals of such unusual moments so that doctors can be proactive in approac…
China integrates ESG into corporate strategy for sustainable growth.
Abstraction and realization are bilateral processes that are key in deriving intelligence and creativity. In many domains, the two processes are approached through rules: high-level principles that reveal invariances within similar yet diverse examples. Under a probabilistic setting for discrete input spaces, we focus …
Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients a…
A Bayesian framework models adversarial uncertainty for robust machine learning.
AdaptHetero uses MLI to tailor EHR models for subgroup-specific predictions.
Opponent modeling improves auction market performance.
It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveil…
This article improves communication efficiency in distributed ML over wireless networks.
We describe two applications of machine learning in the context of IP/Optical networks. The first one allows agile management of resources at a core IP/Optical network by using machine learning for short-term and long-term prediction of traffic flows and joint global optimization of IP and optical layers using colorles…
An active learning approach reduces AoI violation in vehicular networks.
Emergent and unscheduled cardiology admissions from cardiac catheterization laboratory add complexity to the management of Cardiology and in-patient department. In this article, we sought to study the behavior of cardiology admissions from Catheterization laboratory using time series models. Our research involves retro…
Model predicts activist fund targets with 78.2% accuracy.
Prognosticator improves performance in non-stationary MDPs.
FAQ efficiently evaluates LLMs with statistical guarantees using adaptive query selection.
The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting …
Study creates open-access wildfire dataset for Russia.
Paper proposes a new method for predicting DER adoption with hierarchical guarantees.