Working with Matei Zaharia and James Zou, he is currently exploring the fast-growing marketplaces of artificial intelligence and data. He is broadly interested in machine learning, data management and optimization. We compare these bounds to the performance of state-of-the-art robust classifiers and analyze the impact of different layers on robustness.īio: Lingjiao Chen is a PhD candidate in the computer sciences department at Stanford University. For fixed but arbitrary distributions, we demonstrate lower bounds on both the 0-1 and cross-entropy losses for robust learning. In this talk, we will step away from this paradigm and show how fundamental bounds on learning in the presence of adversarial examples can be obtained by viewing the problem through an information-theoretic lens. Most research on adversarial examples has focused on developing better attacks and ad hoc defenses, resulting in an attacker-defender arms race. Of particular interest are adversarial examples, which are maliciously pertrubed test-time examples designed to induce misclassification. Talk Abstract: Understanding the robustness of machine learning systems has become a problem of critical interest due to their increasing deployment in safety critical systems. Talk Title: The Role of Data Geometry in Adversarial Machine Learning He is currently a postdoctoral scholar at UChicago with Ben Zhao and Nick Feamster. He received the 2018 Siemens FutureMakers Fellowship in Machine Learning, and was a finalist for the 2017 Bell Labs Prize. He was a finalist for the 2020 Bede Liu Best Dissertation Award, and won the 2019 Yan Huo *94 Graduate Fellowship and 2018 SEAS Award for Excellence at Princeton University. His work has exposed new vulnerabilities in learning algorithms, along with the development of a theoretical framework to analyze them. The second goal of my research is critical re-examination of measurement methods: I probe models designed for traditional natural language processing tasks involving large, generic datasets by exploring their results on small, socially-specific datasets that are popular in cultural analytics and computational social science.īio: Arjun studies the security of machine learning systems, with a focus on adversarial and distributed learning. These communities situate personal opinions and stories in social contexts of reception, expectation, and judgment. Two fruitful sites for this research are online communities grounded in structured cultural experiences (books, games) and online communities grounded in healthcare experiences (childbirth, contraception, pain management). The first goal of my research is to use natural language processing methods to represent complex personal experiences and self-disclosures communicated in online communities. Talk Abstract: Written communications about personal experiences-and the emotions, narratives, and values that they contain-can be both rhetorically powerful and statistically difficult to model. Talk Title: Modeling Personal Experiences Shared in Online Communities She has a master’s degree in computational linguistics from the University of Washington and a bachelor’s degree in humanities from the University of Notre Dame, and she has completed research internships at Microsoft, Facebook, Twitter, and Pacific Northwest National Laboratory. Her work translates methods from natural language processing to insights about communities and self-disclosure by modeling personal experiences shared in online communities. Her research focuses on unsupervised natural language processing methods and applications to computational social science and cultural analytics. Bio: Maria Antoniak is a PhD candidate in Information Science at Cornell University.
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