Pratik Patil |
Research | Software | Talks | Personal |
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Brief bio: I am currently a postdoctoral researcher in the Department of Statistics at the University of California at Berkeley. I received my Ph.D. jointly in Statistics and Machine Learning from Carnegie Mellon University in Pittsburgh. Before that, I received an M.A.Sc. in Electrical and Computer Engineering along with an M.Sc. in Statistics from the University of Toronto, where I was an exchange student from the Indian Institute of Technology at Guwahati while pursuing a B.Tech. in Electronics and Communications Engineering. More details can be found in my CV. Interests: Broadly in statistical machine learning, optimization, and information theory. Specifically I have on the following topics: exact asymptotics, random matrix theory, cross-validation and tuning, bagging and ensemble methods, sketching and randomized algorithms, free probability theory, uncertainty quantification, constrained statistical inference, model evaluation and benchmarking, network information theory, signal processing and wireless communications.
Email: pratikpatil[at]berkeley[dot]edu |
Revisiting Model Optimism and Model Complexity in the Wake of Overparameterized Learning
Pratik Patil, Jin-Hong Du, and Ryan Tibshirani
Asymptotically Free Ridge Ensembles: Risks, Cross-Validation, and Tuning
Pratik Patil and Daniel LeJeune
Precise Asymptotics of Subagging of Regularized M-estimators
Takuya Koriyama, Pratik Patil, Jin-Hong Du, Kai Tan, and Pierre Bellec
Optimization-based Frequentist Confidence Intervals for Functionals in Constrained Inverse Problems: Resolving the Burrus Conjecture
Pau Batlle, Pratik Patil, Michael Stanley, Houman Owhadi, and Mikael Kuusela
Optimal Ridge Regularization for Out-of-Distribution Prediction
Pratik Patil, Jin-Hong Du, and Ryan Tibshirani
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent in High-Dimensional Least Squares
Pratik Patil, Yuchen Wu, and Ryan Tibshirani
Generalized Equivalences between Subsampling and Ridge Regularization
Pratik Patil and Jin-Hong Du
Mitigating Multiple Descents: A Model-Agnostic Framework for Risk Monotonization
Pratik Patil, Arun Kuchibhotla, Yuting Wei, and Alessandro Rinaldo
Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression
Pratik Patil, Alessandro Rinaldo, and Ryan Tibshirani
Objective Frequentist Uncertainty Quantification for Atmospheric CO2 Retrievals
Pratik Patil, Mikael Kuusela, and Jon Hobbs