Email: gouthamr [at] cmu [dot] edu
Hi, I'm Goutham Rajendran.
About me
I am a Research Associate in the Machine Learning Department at Carnegie Mellon University, working with Pradeep Ravikumar. I currently work on representation learning and generative models, researching how to learn representations of data that are interpretable and controllable. The outcome is to build generative models that are not only accurate and robust, but also highly customizable, which enables alignment with our desiderata such as fairness, reasoning or planning.
Previously, I graduated with a PhD in Computer Science from the University of Chicago, where I was extremely fortunate to have been advised by Madhur Tulsiani (Toyota Technological Institute at Chicago) and Aaron Potechin (University of Chicago). My thesis investigated the applicability of modern convex optimization techniques for various problems in machine learning and robust statistics.
Publications
Machine Learning:Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
Goutham Rajendran*, Simon Buchholz*, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
NeurIPS 2023 (Oral)
Also at workshops:
i. Structured Probabilistic Inference and Generative Modeling
ii. Spurious Correlations, Invariance, and Stability
at ICML 2023An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis
Goutham Rajendran*, Patrik Reizinger*, Wieland Brendel, Pradeep Ravikumar
Causal Inference for Time Series Data (Oral) at UAI 2023Identifiability of deep generative models without auxiliary information
Goutham Rajendran*, Bohdan Kivva*, Pradeep Ravikumar, Bryon Aragam
NeurIPS 2022 (Oral/Spotlight)
Also at workshop: Causal Representation Learning at UAI 2022Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis
Goutham Rajendran*, Aaron Potechin*
NeurIPS 2022
Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition
Goutham Rajendran*, Wei Zou*
Manuscript 2022
Structure learning in polynomial time: Greedy algorithms, Bregman information and exponential families
Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam
NeurIPS 2021 [Slides, Poster]
Learning latent causal graphs via mixture oracles
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
NeurIPS 2021 [Slides]
Sum-of-Squares Lower Bounds for Densest k-Subgraph
Chris Jones, Aaron Potechin, Goutham Rajendran**, Jeff Xu
STOC 2023
Concentration of polynomial random matrices via Efron-Stein inequalities
Goutham Rajendran**, Madhur Tulsiani
SODA 2023
Nonlinear Random Matrices and Applications to the Sum of Squares Hierarchy
Goutham Rajendran
PhD Dissertation, 2022, University of Chicago [Slides]
Sum-of-Squares Lower Bounds for Sparse Independent Set
Chris Jones, Aaron Potechin, Goutham Rajendran**, Madhur Tulsiani, Jeff Xu
FOCS 2021
Sum-of-Squares Lower Bounds for Sherrington-Kirkpatrick via Planted Affine Planes
Mrinalkanti Ghosh, Fernando Granha Jeronimo, Chris Jones, Aaron Potechin, Goutham Rajendran**
FOCS 2020
Machinery for Proving Sum-of-Squares Lower Bounds on Certification Problems
Aaron Potechin, Goutham Rajendran**
Manuscript 2020
Combinatorial Optimization via the Sum of Squares Hierarchy
Goutham Rajendran
Master's thesis, 2018, University of Chicago [Slides]
Fun stuff
I used to be a competitive programmer, including competing in ICPC. My handle in online judges is xorfire: Codeforces, Topcoder, Codechef.
In another life, I would have been a professional footballer (read: soccer) but in this one, my career has been plagued with injuries :)
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