ECE/CS Colloquium Speaker

Speaker: Max Simchowitz, Massachusetts Institute of Technology

Date: Wednesday, March 20

Time: 12:30pm EST

Location: B205 E-Quad

Host: Elad Hazan, Chi Jin

Event page: https://ece.princeton.edu/events/mathematical-foundations-physical-agents

Title: Mathematical Foundations for Physical Agents

Abstract: From robotics to autonomous vehicles, machine learning agents deployed in the physical world (“physical agents”) promise to revolutionize endeavors ranging from manufacturing to agriculture to domestic labor. In this talk, we will develop mathematical foundations, from the ground up, for how to carry out this vision. We will begin our investigation by examining linear dynamical systems, a simple and fundamental model of the interaction between a physical agent and its environment. We prove mathematically that simple exploration attains optimal performance for some of both the simplest and the most complex learning problems in this class. The above finding, while powerful, strongly motivates moving past linear dynamics as a mathematical testbed for understanding learning with physical agents.

Hence, we turn to providing mathematical guarantees for a setting of real-world importance that does not fit the linear mold: behavior cloning. Behavior cloning — teaching a robot to imitate from example demonstrations — lies at the heart of many of today’s most promising robot learning endeavors due to its intuitive data collection and simplicity. Though it can work incredibly well, we still do not have a clear understanding of what circumstances ensure its success. Bringing together the flexibility of generative models with key intuitions arising from the study of linear control, we introduce a framework for behavior cloning that enables an agent to imitate nearly arbitrary behavior with provable guarantees, even when the dynamics governing the agent and environments interaction are nonlinear. We conclude by outlining ongoing work and future steps towards building out the mathematical and conceptual tooling for understanding the next steps towards general, capable and flexible physical agents.

Bio: Max Simchowitz is a postdoctoral researcher in the Robot Locomotion Group at MIT CSAIL. He studies the theoretical foundations of machine learning problems with a sequential or dynamical component; he currently focuses on robotics and out-of-distribution learning, and with past work ranging broadly across control, reinforcement learning, optimization and algorithmic fairness. He received his PhD from University of California, Berkeley in 2021 under Ben Recht and Michael I. Jordan, and his work has been recognized with an ICML 2018 Best Paper Award, ICML 2022 Outstanding Paper Award, and RSS 2023 Best Paper Finalist designation.

CS Colloquium Speaker

Speaker: Zhuang Liu, Meta AI Research

Date: Thursday, March 21

Time: 12:30pm EST

Location: CS 105

Host: Jia Deng

Event page: https://www.cs.princeton.edu/events/26580

Title: Scaling Deep Learning Up and Down

Abstract: Deep learning with neural networks has emerged as a key approach for discovering patterns and modeling relationships in complex data. AI systems powered by deep learning are used widely in applications across a broad spectrum of scales. There have been strong needs for scaling deep learning both upward and downward. Scaling up highlights the pursuit of scalability - the ability to utilize increasingly abundant computing and data resources to achieve superior capabilities, overcoming diminishing returns. Scaling down represents the demand for efficiency - there is limited data for many application domains, and deployment is often in compute-limited settings. My research focuses on scaling deep learning both up and down, to build capable models and understand their behaviors in different computational and data environments.

In this talk, we present studies in both directions. For scaling up, we first explore the design of scalable neural network architectures that are widely adopted in various fields. We then discuss an intriguing observation on modern vision datasets and its implication on scaling training data. For scaling down, we introduce simple, effective, and popularly used approaches for compressing convolutional networks and large language models, alongside interesting empirical findings. Notably, a recurring theme in this talk is the careful examination of implicit assumptions in the literature, which often leads to surprising revelations that reshape community understanding. Finally, we discuss exciting avenues for future deep learning and vision research, such as developing next-gen architectures and modeling datasets.

Bio: Zhuang Liu is currently a Research Scientist at Meta AI Research (FAIR) in New York City. He received his Ph.D. from UC Berkeley EECS in 2022, advised by Trevor Darrell. His research areas include deep learning and computer vision. His work focuses on scaling neural networks both up and down, to build capable models and understand their behaviors in different computational and data environments. His work is broadly applied in different areas of computing and other disciplines. He is a recipient of the CVPR 2017 Best Paper Award.