Please note: This seminar will take place in DC 1304.
Lunjia Hu, PhD candidate
Computer Science Department, Stanford University
Machine learning holds significant potential for positive societal impact. However, in critical applications involving people such as healthcare, employment, and lending, machine learning raises serious concerns of fairness, robustness, and interpretability. Addressing these concerns is crucial for making machine learning more trustworthy.
Please note: This seminar will take place in DC 3317 and online.
Mahsa Derakhshan, Assistant Professor
Khoury College of Computer Sciences, Northeastern University
In this talk, we discuss the stochastic vertex cover problem. In this problem, G is an arbitrary known graph, and G* is an unknown random subgraph of G containing each of its edges independently with a known probability p. Edges of G* can only be verified using edge queries. The goal in this problem is to find a minimum vertex cover of G* using a small number of queries.
Please note: This PhD seminar will take place in DC 1304 and online.
Shubhankar Mohapatra, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Xi He
Despite several works that succeed in generating synthetic data with differential privacy (DP) guarantees, they are inadequate for generating high-quality synthetic data when the input data has missing values.
