On the research front, I currently would like to think and learn more about the burgeoning empirical science of understanding why modern machine learning works. My background is in theory, but I’d like to learn more about how to empirically think about such questions.
During my PhD, my research focused broadly on algorithmic statistics, machine learning theory, and online learning. A bit more specifically, my PhD research focused on the theory of statistical learning in settings where one cares about learning not just on average over a population, but on a (potentially very large) number of overlapping subgroups of the population. Such multi-group considerations can be captured in formalizations such as multicalibration or multi-group PAC learning, and they are meant to model problems that have more complex desiderata such as fairness or robustness. I also dablled in online learning, sequential decision-making, and all the cool theory that comes out of it.
The price of multi-group transductive learning
Noah Bergam, Samuel Deng, Daniel Hsu.
Preprint.
Group-realizable multi-group learning by minimizing empirical risk
Navid Ardeshir, Samuel Deng, Daniel Hsu, and Jingwen Liu.
The 37th International Conference on Algorithmic Learning Theory (ALT), 2026.
Mathematics for Machine Learning: A Bridge Course
Samuel Deng.
Technical Symposium on Computer Science Education (SIGCSE TS), 2025.
Poster
Group-wise oracle-efficient algorithms for online multi-group learning
Samuel Deng, Daniel Hsu, and Jingwen Liu.
Advances in Neural Information Processing Systems (NeurIPS), 2024.
Poster
Multi-group Learning for Hierarchical Groups
Samuel Deng and Daniel Hsu.
International Conference on Machine Learning (ICML), 2024.
Poster
Learning Tensor Representations for Meta-Learning.
Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks.
Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta.
Advances in Neural Information Processing Systems (NeurIPS), 2021.
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?
Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr.
IEEE Symposium on Security and Privacy (Oakland), 2021.
Ensuring Fairness Beyond the Training Data.
Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette Wing, Daniel Hsu.
Advances in Neural Information Processing Systems (NeurIPS), 2020.
Biased Programmers? Or Biased Data? A Field Experiment on Operationalizing AI Ethics.
Bo Cowgill, Fabrizio Dell’Acqua, Samuel Deng, Daniel Hsu, Nakul Verma, Augustin Chaintreau.
21st ACM Conference on Economics and Computation, 2020.
Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Science
Samuel Deng, Achille Varzi.
NeurIPS Workshop on Human-Centric Machine Learning, 2019.
Undergraduate Senior Thesis, 2019. full pdf