AcademicsResearch Interests
PublicationsThe Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization Daniel LeJeune, Hamid Javadi, and Richard G. Baraniuk Advances in Neural Information Processing Systems Foundation (NeurIPS), 2021 Minipatch Learning as Implicit Ridge-Like Regularization Tianyi Yao, Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk, ans Genevera I. Allen IEEE International Conference on Big Data and Smart Computing (BigComp), 2021 The Implicit Regularization of Ordinary Least Squares Ensembles Daniel LeJeune, Hamid Javadi, and Richard G. Baraniuk AISTATS, 2020 Non-negative Matrix Factorization via Archetypal Analysis (Website) Hamid Javadi and Andrea Montanari Journal of the American Statistical Association (2019), DOI: 10.1080/01621459.2019.1594832 An Instability in Variational Inference for Topic Models Behrooz Ghorbani, Hamid Javadi, and Andrea Montanari International Conference on Machine Learning (ICML), 2019. False Discovery Rate via Debiased Lasso Adel Javanmard and Hamid Javadi Electronic Journal of Statistics 13.1 (2019): 1212-1253. A Statistical Model for Motifs Detection Hamid Javadi and Andrea Montanari IEEE Transaction on Information Theory, vol. 64, no. 12, pp 7594-7612, Dec 2018. Porcupine Neural Networks: Approximating Neural Network Landscapes Soheil Feizi, Hamid Javadi, Jesse Zhang, and David Tse Advances in Neural Information Processing Systems Foundation (NeurIPS), 2018 Soheil Feizi, Hamid Javadi, and David Tse Advances in Neural Information Processing Systems Foundation (NeurIPS), 2017 Selected CoursesInference, estimation, and information processing; Convex optimization I, II; Machine learning; Statistical learning theory; Theory of statistics; Stochastic processes on graphs; Mining massive datasets; Information theory and statistics; Advaced topics in convex optimization; Spectral graph theory; Randomized algorithms; Reinforcement learning; Modern Applied Statistics; Network information theory; Stochastic control. Selected ProjectsADMM Preconditioning via Diagonal Scaling (with Reza Takapoui) [Poster] EE 364B: Convex optimization II, Instructor: Prof. Stephen Boyd R-package for Confidence Intervals for High Dimensional Regression (with Adel Javanmard, Andrea Montanari and Sven Schmit) Douglas-Rachford Splitting for Cardinality Constrained Quadratic Programming (with Reza Takapoui and Enzo Busseti) MATH 301: Advanced Topics in Convex Optimization, Instructor: Prof. Emmanuel Candes An Efficient Algorithm for Low-Rank Kernel Regression (with Yash Deshpande) CS 229T: Statistical Learning Theory, Instructor: Prof. Percy Liang |