David Tedaldi

GENERATING LONG-TERM NETWORK CHANGES FROM SLA VIOLATIONS

LEARNING SLA VIOLATION PROBABILITY FROM INTELLIGENT FINE GRAINED PROBING

MODEL COUNTERFACTUAL SCENARIOS OF SLA VIOLATIONS ALONG NETWORK PATHS

Using throughput mode distribution as a proxy for quality of experience and path selection in the internet

Learning robust and accurate rules for device classification from clusters of devices

Flash classification using machine learning for device classification systems

Adaptive training of machine learning models based on live performance metrics

Learning and assessing device classification rules

Detecting spoofing in device classification systems

Revisiting device classification rules upon observation of new endpoint attributes

Unsupervised learning of local-aware attribute relevance for device classification and clustering

Protecting device classification systems from adversarial endpoints

Compressed transmission of network data for networking machine learning systems

Learning robust and accurate rules for device classification from clusters of devices

Learning robust and accurate rules for device classification from clusters of devices

Predicting network states for answering what-if scenario outcomes

Device type classification using metric learning in weakly supervised settings

Detection and resolution of rule conflicts in device classification systems

Continuous validation of active labeling for device type classification

Learning stable representations of devices for clustering-based device classification systems

Active learning for interactive labeling of new device types based on limited feedback

Deep learning architecture for collaborative anomaly detection and explanation

ANOMALY SEVERITY SCORING IN A NETWORK ASSURANCE SERVICE

Anomaly detection with root cause learning in a network assurance service

Using random forests to generate rules for causation analysis of network anomalies