About This Episode
Today we’re joined by Samory Kpotufe, an associate professor at Columbia University and program chair of the 2021 Conference on Learning Theory (COLT).
In our conversation with Samory, we explore his research at the intersection of machine learning, statistics, and learning theory, and his goal of reaching self-tuning, adaptive algorithms. We discuss Samory’s research in transfer learning and other potential procedures that could positively affect transfer, as well as his work understanding unsupervised learning including how clustering could be applied to real-world applications like cybersecurity, IoT (Smart homes, smart city sensors, etc) using methods like dimension reduction, random projection, and others. If you enjoyed this interview, you should definitely check out our conversation with Jelani Nelson on the “Theory of Computation.”
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Connect with Samory!
- Paper: A No-Free-Lunch Theorem For Multitask Learning
- Paper: Self-Tuning Bandits over Unknown Covariate-Shifts
- Paper: A Comparative Study of Network Traffic Representations for Novelty Detection
- Paper: An Efficient One-Class SVM for Anomaly Detection in the Internet of Things
- Paper: Feature Extraction for Novelty Detection in Network Traffic
- COLT 2021