Crack Open (Neural) Nets - Can We Make ML-Based Networked Systems More Trustworthy?

Speaker:        Dr. Marco Canini

Title:          "Crack Open (Neural) Nets - Can We Make ML-Based
                 Networked Systems More Trustworthy?"

Date:           Monday, 31 December 2018

Time:           10:00am to 11:00am

Venue:          Room 2463 (via lift 25/26), HKUST


Machine learning (ML) solutions to challenging networking problems are a
promising avenue but the lack of interpretability and the behavioral
uncertainty affect trust and hinder adoption. A key advantage of ML
algorithms and architectures, such as deep neural networks and
reinforcement learning, is that they can discover solutions that are
attuned to specific problem instances. As an example, consider a video bit
rate adaptation logic that is tuned specifically for Netflix clients in
the United States. Yet, there is a general fear that ML systems are black
boxes. This creates uncertainty about why learning systems work, whether
they will continue to work in conditions that are different from those
seen during training or whether they will fall off performance cliffs. The
lack of interpretability of ML models is widely recognized as a major
hindrance to adoption.

This raises a crucial question: How do we ensure that learned models
behave reliably and as intended? ML solutions that cannot be trusted to do
so are brittle and may not be deployed despite their performance benefits.
We propose an approach to enhance the trustworthiness of ML solutions for
networked systems. Our approach builds on innovations in interpretable ML
tools. Given a black-box ML model, interpretable ML methods offer
explanations on any given input instance. By integrating the explanations
from these tools with operator's domain knowledge, our approach can verify
that the ML model behaves as per operator expectations, detect
misbehaviors and identify corrective actions. To demonstrate our approach,
we performed an in-depth case study on Pensieve (a recent neural video
rate adaptation system) and identified four classes of undesired


Marco Canini is an assistant professor in Computer Science at KAUST. Marco
obtained his Ph.D. in computer science and engineering from the University
of Genoa in 2009 after spending the last year as a visiting student at the
University of Cambridge, Computer Laboratory. He was a postdoctoral
researcher at EPFL from 2009 to 2012 and after that a senior research
scientist for one year at Deutsche Telekom Innovation Labs & TU Berlin.
Before joining KAUST, he was an assistant professor at the Université
catholique de Louvain. He also held positions at Intel, Microsoft and