Goutham Rajendran

31 papers A* 13Journal 17Unranked 1

Year	Rank	Type	Title / Venue / Authors
2024	—	`conf`	An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CLeaR Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Kumar Ravikumar
2024	A*	`conf`	Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. NeurIPS Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2024	J	`jnl`	Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. CoRR Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2024	A*	`conf`	Efficient Certificates of Anti-Concentration Beyond Gaussians. FOCS Ainesh Bakshi, Pravesh K. Kothari, Goutham Rajendran, Madhur Tulsiani, Aravindan Vijayaraghavan
2024	J	`jnl`	Efficient Certificates of Anti-Concentration Beyond Gaussians. CoRR Ainesh Bakshi, Pravesh Kothari, Goutham Rajendran, Madhur Tulsiani, Aravindan Vijayaraghavan
2024	A*	`conf`	From Causal to Concept-Based Representation Learning. NeurIPS Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2024	J	`jnl`	Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. CoRR Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2024	A*	`conf`	On the Origins of Linear Representations in Large Language Models. ICML Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch
2024	J	`jnl`	On the Origins of Linear Representations in Large Language Models. CoRR Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch
2023	J	`jnl`	An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CoRR Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Ravikumar
2023	A*	`conf`	Concentration of polynomial random matrices via Efron-Stein inequalities. SODA Goutham Rajendran, Madhur Tulsiani
2023	A*	`conf`	Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2023	J	`jnl`	Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
2023	J	`jnl`	Nonlinear Random Matrices and Applications to the Sum of Squares Hierarchy. CoRR Goutham Rajendran
2023	A*	`conf`	Sum-of-Squares Lower Bounds for Densest k-Subgraph. STOC Chris Jones, Aaron Potechin, Goutham Rajendran, Jeff Xu
2023	J	`jnl`	Sum-of-Squares Lower Bounds for Densest k-Subgraph. CoRR Chris Jones, Aaron Potechin, Goutham Rajendran, Jeff Xu
2022	J	`jnl`	Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition. CoRR Goutham Rajendran, Wei Zou
2022	J	`jnl`	Combinatorial Optimization via the Sum of Squares Hierarchy. CoRR Goutham Rajendran
2022	J	`jnl`	Concentration of polynomial random matrices via Efron-Stein inequalities. CoRR Goutham Rajendran, Madhur Tulsiani
2022	J	`jnl`	Identifiability of deep generative models under mixture priors without auxiliary information. CoRR Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2022	A*	`conf`	Identifiability of deep generative models without auxiliary information. NeurIPS Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2022	A*	`conf`	Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis. NeurIPS Aaron Potechin, Goutham Rajendran
2021	A*	`conf`	Learning latent causal graphs via mixture oracles. NeurIPS Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2021	J	`jnl`	Learning latent causal graphs via mixture oracles. CoRR Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
2021	A*	`conf`	Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. NeurIPS Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam
2021	J	`jnl`	Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. CoRR Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam
2021	A*	`conf`	Sum-of-Squares Lower Bounds for Sparse Independent Set. FOCS Chris Jones, Aaron Potechin, Goutham Rajendran, Madhur Tulsiani, Jeff Xu
2021	J	`jnl`	Sum-of-Squares Lower Bounds for Sparse Independent Set. CoRR Chris Jones, Aaron Potechin, Goutham Rajendran, Madhur Tulsiani, Jeff Xu
2020	J	`jnl`	Machinery for Proving Sum-of-Squares Lower Bounds on Certification Problems. CoRR Aaron Potechin, Goutham Rajendran
2020	A*	`conf`	Sum-of-Squares Lower Bounds for Sherrington-Kirkpatrick via Planted Affine Planes. FOCS Mrinalkanti Ghosh, Fernando Granha Jeronimo, Chris Jones, Aaron Potechin, Goutham Rajendran
2020	J	`jnl`	Sum-of-Squares Lower Bounds for Sherrington-Kirkpatrick via Planted Affine Planes. CoRR Mrinalkanti Ghosh, Fernando Granha Jeronimo, Chris Jones, Aaron Potechin, Goutham Rajendran