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108 papers · 1 filter
EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems
Luan Pham, Victor Nicolet, Joey Dodds +2
Anomaly detection and localization (ADL) is critical for maintaining reliability and availability in cloud systems. Recent ADL developments focus on metric and log data, leaving ev…
Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks
Hongfei Du, Emre Barut, Fang Jin
Despite the popularity of Convolutional Neural Networks (CNN), the problem of uncertainty quantification (UQ) of CNN has been largely overlooked. Lack of efficient UQ tools severel…
A Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training
Johnny R. Zhang, Xiaomei Mi, Gaoyuan Du +4
Stochastic optimization powers the scalability of modern artificial intelligence, spanning machine learning, deep learning, reinforcement learning, and large language model trainin…
Federated Learning Clients Clustering with Adaptation to Data Drifts
Minghao Li, Dmitrii Avdiukhin, Rana Shahout +3
Federated Learning (FL) trains deep models across edge devices without centralizing raw data, preserving user privacy. However, client heterogeneity slows down convergence and limi…
Detecting Security-Relevant Methods using Multi-label Machine Learning
Oshando Johnson, Goran Piskachev, Ranjith Krishnamurthy +1
To detect security vulnerabilities, static analysis tools need to be configured with security-relevant methods. Current approaches can automatically identify such methods using bin…
Learning Action Embeddings for Off-Policy Evaluation
Matej Cief, Jacek Golebiowski, Philipp Schmidt +2
Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy. OPE is a viable alternative to run…