Rishabh Dudeja

I am PhD student at the Statistics Department at Columbia University. I am fortunate to be advised by Professor Arian Maleki and Professor Daniel Hsu.
 
I am interested in understanding information theoretic and computational phenomena that arise in high dimensional statistical inference problems using tools from information theory, applied probability and statistical physics.
 
You can find more information about me in my CV.
 
Feel free to email me at: rd2714 (at) columbia (dot) edu.

 

Publications

 1.

 Learning single-index models in Gaussian space
Rishabh Dudeja, Daniel Hsu
Conference on Learning Theory (COLT),  2018.

 

 2.

 Attribute-efficient learning of monomials over highly-correlated variables.
Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli. 
Conference on Algorithmic Learning Theory (ALT),  2019.

 

3.

Analysis of Spectral Methods for Phase Retrieval with Random Orthogonal Matrices. 
Rishabh Dudeja, Milad Bakhshizadeh, Junjie Ma, Arian Maleki.
Transactions on Information Theory, 2020 [Journal Link].

 

 4.

 Spectral Method for Phase Retrieval: an Expectation Propagation Perspective.
Junjie Ma, Rishabh Dudeja, Ji Xu, Arian Maleki, Xiaodong Wang.
Transactions on Information Theory, 2020 (Accepted).

 

 5.

 Information Theoretic Limits for Phase Retrieval with Subsampled Haar Sensing Matrices.
Rishabh Dudeja, Junjie Ma, Arian Maleki.
Transactions on Information Theory, 2020 [Journal Link].

 

6.

 Statistical Query Lower Bounds for Tensor PCA.
Rishabh Dudeja, Daniel Hsu.
Preprint, 2020.

 

7.

 Universality of Linearized Message Passing for Phase Retrieval with Structured Sensing Matrices.
Rishabh Dudeja, Milad Bakhshizadeh.
Preprint, 2020.

 

 

Teaching

I have served as the TA for the following courses at Columbia:

1.   Applied Linear Regression Analysis ( B.A./M.A. Level)

 Fall 2015
2.  Introduction to Statistics (B.A. Level)

 Spring 2016
3.   Linear Regression Models ( B.A./M.A. Level)

 Fall 2016
4.   Applied Categorical Data Analysis (B.A. Level)

 Spring 2017, Spring 2018
5.   Probability and Statistical Inference (M.A. Level)

 Fall 2017
6.   Statistical Computing and Introduction to Data Science ( B.A./M.A. Level)

 Fall 2018
7.   Multivariate Statistical Inference ( B.A./M.A. Level)

 Spring 2019
8.   Statistical Inference and Time Series Modelling ( B.A./M.A. Level)  Fall 2019, Spring 2020