CS Events

Computer Science Department Colloquium

From Deep Learning to Program Learning


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Monday, December 05, 2022, 10:30am


Speaker: He Zhu


He Zhu is an assistant professor in the Department of Computer Science at Rutgers University. He was a research scientist at Galois, Inc. He received his Ph.D. degree from Purdue University. His work spans programming languages, formal methods, and machine learning. He is currently interested in building intelligent program learning systems that tightly integrate deep learning and program synthesis and that can be formally verified. Dr. Zhu received two distinguished paper awards from the prestigious ACM SIGPLAN conference on Programming Language Design and Implementation. His research was supported by the National Science Foundation and the Defense Advanced Research Projects Agency.

Location : CoRE 301 + Virtual

Event Type: Computer Science Department Colloquium

Abstract:  Deep learning has led to encouraging successes in many challenging tasks. However, a deep neural net inherently lacks interpretability due to the difficulty of identifying how the model's learned logic relates to its complex network structure. It is difficult to debug and reason about deep neural nets at the same level developers manage conventional software systems. Program-guided models (i.e. neurosymbolic programs) have recently attracted much interest due to their interpretability and compositionality. Yet, synthesizing programs requires optimizing over a combinatorial, non-differentiable, and rapidly exploded space of program structures. In this talk, I will present our recent efforts on enabling human-readable, domain-specific programs as an efficient learning representation. Powered by novel program synthesis algorithms, our method jointly optimizes program structures and program parameters. As a step toward trustworthy learning, it adapts formal methods typically designed for traditional human-written software systems to provide formal correctness guarantees to program-guided models. Experiment results over application domains such as behavior classification and reinforcement learning demonstrate that our algorithms excel in discovering optimal programs that are highly interpretable and verifiable.

Contact  Matthew Stone

Zoom Link: https://rutgers.zoom.us/j/6798718294?pwd=UDBEUFZTVmV5aTBiNkYxOXIxK1B4UT09