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 (CoRR, 2020)

Sum-of-Squares Lower Bounds for Sparse Independent Set. - Chris Jones; Aaron Potechin; Goutham Rajendran; Madhur Tulsiani; Jeff Xu (FOCS, 2021)

Learning latent causal graphs via mixture oracles. - Bohdan Kivva; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2021)

Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. - Goutham Rajendran; Bohdan Kivva; Ming Gao; Bryon Aragam (NeurIPS, 2021)

Identifiability of deep generative models without auxiliary information. - Bohdan Kivva; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2022)

Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis. - Aaron Potechin; Goutham Rajendran (NeurIPS, 2022)

Identifiability of deep generative models under mixture priors without auxiliary information. - Bohdan Kivva; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (CoRR, 2022)

Combinatorial Optimization via the Sum of Squares Hierarchy. - Goutham Rajendran (CoRR, 2022)

Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition. - Goutham Rajendran; Wei Zou (CoRR, 2022)

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. - Simon Buchholz; Goutham Rajendran; Elan Rosenfeld; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (NeurIPS, 2023)

Concentration of polynomial random matrices via Efron-Stein inequalities. - Goutham Rajendran; Madhur Tulsiani (SODA, 2023)

Sum-of-Squares Lower Bounds for Densest k-Subgraph. - Chris Jones; Aaron Potechin; Goutham Rajendran; Jeff Xu (STOC, 2023)

Nonlinear Random Matrices and Applications to the Sum of Squares Hierarchy. - Goutham Rajendran (CoRR, 2023)

An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. - Goutham Rajendran; Patrik Reizinger; Wieland Brendel; Pradeep Kumar Ravikumar (CLeaR, 2024)

Efficient Certificates of Anti-Concentration Beyond Gaussians. - Ainesh Bakshi; Pravesh K. Kothari; Goutham Rajendran; Madhur Tulsiani; Aravindan Vijayaraghavan (FOCS, 2024)

On the Origins of Linear Representations in Large Language Models. - Yibo Jiang; Goutham Rajendran; Pradeep Kumar Ravikumar; Bryon Aragam; Victor Veitch (ICML, 2024)

Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. - Yibo Jiang; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2024)

From Causal to Concept-Based Representation Learning. - Goutham Rajendran; Simon Buchholz; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (NeurIPS, 2024)

Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. - Goutham Rajendran; Simon Buchholz; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (CoRR, 2024)