Proceed adapts models proactively against concept drift in online time series forecasting.
problem Concept drift causes forecast models to adapt to outdated concepts, reducing performance.
method Proceed estimates and translates concept drift into parameter adjustments, enhancing model resilience.
result Proceed brings more performance improvements than state-of-the-art online learning methods.
Proposes proactive bed requests to reduce ED boarding and patient wait times.
problem Reduces ED boarding and patient wait times by proactively requesting inpatient beds.
method Formulates as a Markov decision process, uses predictions of admission probability and time to disposition to guide bed requests.
result Proactive aggregate bed requests can reduce boarding times by 30-70% and length of stay by 6-15%.
Approach detects illegal insider trading proactively from diverse data sources.
problem Detecting illegal insider trading in the stock market.
method Deep-learning and discrete signal processing on time series data, combined with tree-based visualization.
result Approach has a good success rate in detecting illegal insider trading patterns.
RePAD detects anomalies in streaming time series data in real-time.
problem Real-time anomaly detection for time series data without human intervention.
method RePAD uses LSTM to predict anomalies based on short-term historic data.
result RePAD detects anomalies proactively and provides early warnings.
ProEval efficiently estimates AI performance and discovers failures using pre-trained Gaussian Processes.
problem Resource-intensive evaluation of generative AI models.
method ProEval uses pre-trained Gaussian Processes and Bayesian quadrature to estimate performance and discover failures.
result ProEval requires significantly fewer samples to achieve accurate performance estimates and reveals more diverse failure cases.
The paper develops efficient sampling strategies for BRDF data manifolds.
problem Efficiently sampling and measuring BRDF data from high-dimensional manifolds.
method Statistical design of experiments and generalized proactive learning.
result Established more efficient sampling and measurement strategies for BRDF data manifolds.
This research improves capital efficiency and impermanent loss in cryptocurrency markets using multi-token trading pools.
problem Poor impermanent loss and capital efficiency in automated market makers.
method Analysis and construction of a multi-token token proactive market maker (MPMM).
result MPMM shows better impermanent loss and capital efficiency than comparable market makers.
This paper evaluates targeted data poisoning attacks by focusing on the hardest samples, improving evaluation and defense strategies.
problem The effectiveness of targeted data poisoning attacks is often overestimated due to average evaluation methods.
method The paper introduces metrics to identify the hardest and easiest to poison samples based on clean model information.
result The proposed metrics reliably stratify samples by poisoning vulnerability, enabling rigorous worst-case evaluation and proactive defense.
CANARI detects near-anomalies to predict future anomalies proactively.
problem Uncertainty in anomaly detection near distribution boundaries.
method Christoffel-based ANomaly Anticipation for eaRly dIscovery (CANARI) method.
result CANARI outperforms baseline methods in detecting near-anomalies and predicting future anomalies.
Prophet predicts device qualities for FL to reduce training latency.
problem Bad candidate-selection leads to large training and reporting latency in FL.
method Each device predicts its own training and reporting phases using LSTM. The algorithm is implemented with DRL.
result The proposed approach outperforms reactive algorithms in real-world experiments.
The paper proposes a proactive method to improve model reliability by removing unstable relationships in the training data.
problem Improper generalization of predictive models due to dataset shift.
method Proactively removing unstable relationships using causal mechanisms and latent counterfactual variables.
result Models that remove vulnerable variables and use estimates of latent variables transfer better, often outperforming in the target domain.
Models predict patients at risk of uncontrolled hypertension.
problem Identifying patients at risk of uncontrolled hypertension.
method Developed machine learning models (logistic regression and recurrent neural networks) using EHR data.
result Best model achieved AUROC of 0.719, outperforming baseline.
RODMAN improves ML-based disk failure prediction accuracy in cloud environments.
problem Imperfect data quality in real-world cloud environments degrades ML-based disk failure prediction accuracy.
method RODMAN uses three data preprocessing techniques: failure-type filtering, spline-based data filling, and automated pre-failure backtracking.
result RODMAN significantly improves prediction accuracy compared to no preprocessing.
Robust policies improve ICU transfer outcomes by predicting patient deterioration.
problem Higher mortality rates for unplanned ICU transfers.
method Markov Decision Process model to predict patient severity and optimize transfer policies.
result Robust policies are more aggressive in transferring patients than nominal policies, improving overall patient care.
GANs visualize malware behavior for proactive protection.
problem Malware authors' advantage in testing and augmenting malicious code.
method GAN trained on distributed image representation of malware behaviors.
result Generated synthetic malware for adversarial training of anti-malware models.
ExpertNet uses noisy labels to improve deep learning robustness.
problem Improving deep learning robustness against noisy labels.
method ExpertNet framework combining Amateur and Expert models, iteratively learning from noisy labels and images.
result ExpertNet achieves robust classification with as little as 20-50% training data, outperforming state-of-the-art models.
HyPV-LEAD detects cryptocurrency anomalies proactively, improving financial security.
problem Cryptocurrency anomalies like mixing, fraud, and pump-and-dump operations are hard to detect due to class imbalance and temporal volatility.
method HyPV-LEAD integrates lead time into anomaly detection through window-horizon modeling, Peak-Valley sampling, and hyperbolic embedding.
result HyPV-LEAD achieves a PR-AUC of 0.9624 on Bitcoin transaction data, significantly outperforming state-of-the-art methods.
Paper analyzes liquidity for everlasting options in DeFi, offering strategies to reduce costs.
problem Challenges of perpetual derivatives in decentralized finance markets.
method Dynamic proactive market maker model, simulations, hedging strategies.
result Liquidity providers can achieve net positive PnL with effective strategies.
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…
Proposes a method to predict content preferences for mobile users in decentralized caching networks.
problem Determining caching schemes for decentralized caching networks with mobile traffic.
method Formulates content preference learning as a DRMTL problem, integrates mobility prediction, and uses ADMM for optimization.
result Mobility-aware content preference learning provides more accurate predictions and improved hit ratios.
Climate-contingent finance helps adapt to uncertain climate risks.
problem Uncertainty in future climate scenarios makes proactive adaptation less feasible.
method Underwrite climate adaptation projects with repayment based on future climate scenarios.
result Optimal financing reduces over- and under-preparation risks.
Unified approach detects traffic conflicts across various interactions.
problem Inconsistent detection of traffic conflicts across different interactions.
method Unified probabilistic approach decomposes conflicts into statistical learning tasks.
result Effective collision warnings across diverse datasets and environments.
Study uses machine learning to predict rain in Australia.
problem Challenging task of predicting rainfall with uncertain outcomes.
method Machine learning techniques, including modeling inputs, methods, and pre-processing.
result Comparison of various machine learning techniques' reliability in predicting rainfall.
Proactive DP optimizes privacy and utility in DP-SGD with a fixed privacy budget.
problem Balancing privacy and utility in differential privacy for machine learning.
method Proposes a pro-active DP framework that allows a-priori selection of DP-SGD parameters to maximize test accuracy.
result Proactive DP can optimize utility of DP-SGD with a fixed privacy budget (ε, δ).
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…
This article improves communication efficiency in distributed ML over wireless networks.
problem Achieving high ML inference accuracy at scale with zero communication latency.
method Optimizing communication payload types, techniques, scheduling, and ML architectures.
result Communication-efficient and distributed learning frameworks are presented.
PAGER detects failures in deep regression models using a new framework.
problem Detecting failures in deep regression models.
method PAGER uses a combination of epistemic uncertainty and manifold non-conformity scores.
result PAGER accurately characterizes and detects failures in deep regressors.
A Bayesian framework models adversarial uncertainty for robust machine learning.
problem Vulnerability of machine learning models to adversarial attacks.
method Formal Bayesian framework that models adversarial uncertainty through a stochastic channel, articulating probabilistic assumptions.
result Explicitly modeling adversarial uncertainty leads to improved robustification strategies.
Proposes a method to create robust models that adapt to target domains.
problem Creating reliable models when training and target distributions differ.
method Integrates prior knowledge of data generating process to learn stable relationships.
result The Surgery Estimator finds stable relationships in more scenarios than previous methods.
An active learning approach reduces AoI violation in vehicular networks.
problem Dynamic nature of vehicular networks makes resource allocation challenging.
method Gaussian process regression (GPR) for online decentralized active learning.
result Significant improvement in AoI violation probability with at least 50% reduction.
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…
This article guides data scientists on avoiding discrimination in machine learning.
problem Machine learning systems can create or exacerbate societal disparities.
method Provides a taxonomy of practices and measures to mitigate discrimination.
result Data scientists should be intentional about modeling and reducing discriminatory outcomes.
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…
AdaptHetero uses MLI to tailor EHR models for subgroup-specific predictions.
problem Lack of subgroup-specific, operationalizable modeling strategies in EHRs.
method Integrates MLI with unsupervised clustering to identify subgroup-specific characteristics.
result Improves predictive performance by up to 174.39 percent across many subpopulations.
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…
Model predicts activist fund targets with 78.2% accuracy.
problem Predicting activist fund targets to mitigate risks and inform investments.
method Evaluated 123 model configurations using machine learning techniques.
result Best model achieved AUC-ROC of 0.782.
Study creates open-access wildfire dataset for Russia.
problem Data scarcity for comprehensive Eurasian wildfire research.
method Machine learning for exploratory data analysis and predictive modeling.
result Identified key environmental factors influencing fire behavior.
AI predicts employee attrition to prevent turnover.
problem Predicting and preventing employee attrition.
method Ensemble classification and Linear Regression models.
result Predicts employee attrition with over 91% accuracy.
Deep learning models can learn confounding factors instead of device fingerprints in wireless signals.
problem Learning device fingerprints from complex-valued deep neural networks in the presence of confounding factors.
method Investigating complex-valued deep neural networks (DNNs) to distinguish between wireless transmitters, focusing on clock drift and channel variations.
result DNNs learn confounding features rather than device-specific characteristics, requiring strategies to promote generalization.
Prognosticator improves performance in non-stationary MDPs.
problem Real-world applications often violate the stationary assumption in reinforcement learning.
method A policy gradient algorithm that maximizes a forecast of future performance.
result Prognosticator outperforms online adaptation techniques in non-stationary MDPs.
New hybrid model combines GARCH and reinforcement learning for improved VaR estimation.
problem Inaccurate VaR estimation in volatile financial markets.
method Combines GARCH volatility models with DDQN reinforcement learning for dynamic risk forecasting.
result Significant improvement in VaR accuracy and reduction in breaches.
AMUSE uses reinforcement learning to predict optimal model updates.
problem Concept drift weakens model performance over time.
method Reinforcement learning in a simulated environment.
result AMUSE proactively recommends updates based on performance improvements.
ARL bridges non-Markovian decision processes with reinforcement learning, improving foresight and stability.
problem Inaccurate foresight in non-Markovian environments due to state-based methods' limitations.
method Lifted state space into a signature-augmented manifold, using a self-consistent field approach to anticipate future path-law.
result ARL achieves deterministic evaluation of expected returns with reduced computational complexity and variance.
Activity2vec learns representations from wearable activity data.
problem Proactive screening and monitoring of chronic conditions.
method Adversarial unsupervised representation learning with three components.
result Activity2vec outperforms many baselines in disorder prediction tasks.
Algorithms learned from data are increasingly used for deciding many aspects in our life: from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence that decision making by inappropriately trained algorithms may unintentionally discriminate people. For example, in automated matching of cand…
Paper proposes SDS for 5G security using machine learning.
problem Cybersecurity threats and expanding IoT in 5G networks.
method SDS uses machine learning, specifically a CNN with NAS, to detect anomalies.
result CNN achieved 100% accuracy in identifying benign traffic and 96.4% in detecting anomalies.
New method detects adversarial attacks in industrial systems.
problem Adversarial attacks compromise machine learning models in industrial control systems.
method Introduces domain adaptation layer and Monte Carlo simulation for proactive defense.
result Approximates Bayes optimal mitigation for improved detection model health.
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…