Dawn Song

Secure and Privacy-Preserving Federated Learning

Threat model in federated learning

Federated learning (FL) proposes a powerful new distributed learning paradigm and has grown as an active research field with large-scale real-world deployment in the last several years. In FL, participants collaboratively train a model when all the data is held locally to preserve data...

Factorized language representations with knowledge and logic

Knowledge and logical reasoning are essential constituents of spoken and written language. Despite the availability of more compact and accessible representations, such as knowledge graphs (KG) and logic forms, modern NLU technologies predominantly encapsulate knowledge and logical reasoning in vector representations along with other linguistic patterns. Despite its successes, this encapsulation erects barriers between NLU and symbolic technologies developed for knowledge representation and reasoning, which are inherently more transparent, robust, and scalable than their...

Neural Program Synthesis from Diverse and Distant Context

Creating effective visualization is an important part of data analytics. While there exist many libraries for creating visualization, writing such code remains difficult given the myriad of parameters that users need to provide. In this project, we propose the new task of synthesizing visualization programs from a combination of...

Adversarial Attacks Against Deep Reinforcement Learning Policies for Strategy Games

Program synthesis from input-output (IO) examples has been a long-standing challenge. While recent works demonstrated limited success on domain-specific languages (DSL), it remains highly challenging to apply them to real-world programming languages, such as C. Due to complicated syntax and token variation, there are three major challenges: (1) unlike many DSLs, programs in languages like C need to compile first and are not executed via interpreters; (2) the program search space grows exponentially when the syntax and semantics of the programming language become more complex; and (3)...

Robustness for Deep Learning/Ethical AI Through Human Value Modeling

Despite the recent advances in adversarial training based defenses, deep neural networks are still vulnerable to adversarial attacks outside the perturbation type they are trained to be robust against. In this project, we propose Protector, a two-stage pipeline to improve the robustness against multiple perturbation types. We demonstrate that...