Ph.D. student Department of Electrical Engineering and Computer Science (EECS) University of California, Berkeley |
I am a PhD student at the University of California, Berkeley, in the Department of Electrical Engineering and Computer Science (EECS). My advisors are Martin Wainwright and Mike Jordan. I am affiliated with the Berkeley AI Research (BAIR) lab.
My research interests span continuous optimization and high-dimensional statistics (and the many connections between these areas). More specifically, my recent work attempts to address–from a theoretical point of view–the statistical challenges encountered in modern learning problems: buzz-words include "distribution shift," "covariate shift," and "transfer learning."
Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints
with Martin J. Wainwright and Lin Xiao.
[ arXiv ]
Optimally tackling covariate shift in RKHS-based nonparametric regression
with Cong Ma and Martin J. Wainwright.
[ arXiv ]
[ Annals of Statistics (to appear, 2023+) ]
A new similarity measure for covariate shift with applications to nonparametric regression
with Cong Ma and Martin J. Wainwright.
[ arXiv ]
[ ICML 2022 (proceedings)
(slides)
(poster)
(long oral)
]
Cluster-and-Conquer: A Framework For Time-Series Forecasting
with Rajat Sen, Nikhil Rao, N. Benjamin Erichson, Michael I. Jordan, and Inderjit S. Dhillon.
[ arXiv ]
Weighted matrix completion from non-random, non-uniform sampling patterns
with Simon Foucart, Deanna Needell, Yaniv Plan, and Mary Wootters.
[ arXiv ]
[ IEEE Transactions on Information Theory ]
FedSplit: an algorithmic framework for fast federated optimization
with Martin J. Wainwright.
[ arXiv ]
[ NeurIPS 2020]
On identifying and mitigating bias in the estimation of the COVID-19 case fatality rate
with Anastasios Angelopoulos, Rohit Varma, and Michael I. Jordan.
[ arXiv ]
[ Harvard Data Science Review ]
[ code ]
Note that the links below typically reflect current course offerings, and therefore do not represent the course that I helped teach. Nonetheless, I have included the links in case the context is helpful.
UC Berkeley
Stat210B: Mathematical Statistics. GSI, Spring 2021.
Stanford University
EE364a: Convex Optimization I. Instructor, Summer 2017. TA, Winter 2017 and 2018.
EE104: Introduction to Machine Learning. TA, Spring 2018.
EE103: Introduction to Matrix Methods. TA, Fall 2016, 2017, 2018.
CS106 (A, B, X): Introductory data structures and algorithms sequence. TA, many quarters.
Cory Hall, BLISS Lab
pathakr@berkeley.edu
http://www.cs.berkeley.edu/~pathakr/