Fundamental Perspectives on Machine Learning: Strategic Agents & Contemporary Models

Through recent advances in machine learning (ML) technology, we are getting closer to realizing the broadly stated goal of “articially intelligent”, autonomous agents. In many cases — like cognitive radio, swarm robotics, and e-commerce — these agents will not be acting in isolation, and it is critical for them to directly interact with other agents who themselves behave strategically. The ensuing questions of how agents should learn from strategically generated data, and how such strategic behavior will manifest, are well-posed even when simple ML algorithms are used. On the other hand, most of the recent empirical success in single-agent AI is driven by the construction of overparameterized neural networks that would traditionally be considered too complex for reliable performance. Foundational mechanisms for understanding their state-of-the-art empirical performance remain elusive. In this talk, I present two vignettes of my research that engage separately with central diculties in strategic learning and contemporary models. First, I present a scheme by which an agent can provably learn from an unknown environment, by adapting online to the model that seems to best describe the data while remaining robust to strategically generated data. I also briey touch upon credible approximations to how strategic agents will behave in the presence of such adaptive learning. Next, I present a signal-processing perspective on the overparameterized (high-dimensional) linear model, and ramications for generalization in least-squares regression and classication. In addition to the commonly discussed pitfall of noise overtting, I show that a phenomenon of signal “bleed”, observed classically in statistical signal processing and under-sampling theory, is equally dangerous for generalization. I use these phenomena to characterize special situations in which overparameterization is actually bene- cial. I conclude with future directions that I plan to address for a more complete foundational understanding of multi-agent learning.