Rajendran, Goutham

14 publications

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

NeurIPS 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

ICMLW 2024 Do LLMs Dream of Elephants (when Told Not to)? Latent Concept Association and Associative Memory in Transformers Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam

ICMLW 2024 Do LLMs Dream of Elephants (when Told Not to)? Latent Concept Association and Associative Memory in Transformers Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam

NeurIPSW 2024 Do LLMs Dream of Elephants (when Told Not to)? Latent Concept Association and Associative Memory in Transformers Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam

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

NeurIPSW 2024 From Causal to Concept-Based Representation Learning Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Kumar Ravikumar

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

NeurIPS 2023 Learning Linear Causal Representations from Interventions Under General Nonlinear Mixing Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep K. Ravikumar

ICMLW 2023 Learning Linear Causal Representations from Interventions Under General Nonlinear Mixing Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Kumar Ravikumar

NeurIPS 2022 Identifiability of Deep Generative Models Without Auxiliary Information Bohdan Kivva, Goutham Rajendran, Pradeep K. Ravikumar, Bryon Aragam

NeurIPS 2022 Sub-Exponential Time Sum-of-Squares Lower Bounds for Principal Components Analysis Aaron Potechin, Goutham Rajendran

NeurIPS 2021 Learning Latent Causal Graphs via Mixture Oracles Bohdan Kivva, Goutham Rajendran, Pradeep K. 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