# Adhyyan Narang

## Bio

I am a PhD Student in the ECE Department of University of Washington, advised by Prof. Maryam Fazel and Prof. Lillian Ratliff.

I recently completed my Masters in the BLISS Lab in the EECS department at UC Berkeley, where I was advised by Prof. Anant Sahai. My masterâ€™s thesis explored the role of margins for overparameterized classification problems and can be found here.

I completed my undergraduate studies at UC Berkeley with a major in EECS and minor in Theater and Performance Studies.

## Research Interests

My research interests lie within optimization, game theory, and statistical learning theory; I often use tools from signal processing and dynamical systems. Broadly, I am interested in developing theory to inspire the development of principled and robust ML systems. Topics that I am especially interested in are introduced below, and my work can be found in the publications tab.

**Overparameterized machine learning:** The success of neural networks with many more parameters than available data points cannot be explained by traditional learning theory, which emphasizes the importance of the bias variance tradeoff. Hence a gap has emerged in the theory over the past few years, and the double-descent explanation for generalization has started to gain prominence for regression problems. However, there are various open questions of interest, and my work has focussed on answering the following.

- Does double-descent apply to classification problems as well, and are these easier or harder than regression problems in overparameterized setups?
- How do lifted models differ from linear models for overparameterized classification, and can adversarial examples be understood as a consequence of overparameterization?
- The double-descent explanation emphasizes the importance of having the right prior, something that is central to all Bayesian reasoning. But where does the prior come from? We propose meta-learning as an answer.

**Convergence of gradient-algorithms in Game Theory:** An increasing number of challenges in adversarial machine learning can be best understood as game-theoretic problems. However, practitioners either ignore these game-effects or address them in ad-hoc ways. I am working to develop theory to identify when these adopted approaches can be shown to possess desirable convergence properties and to propose alternatives with guarantees when they do not.