| Identifiability of deep generative models without auxiliary information B Kivva, G Rajendran, P Ravikumar, B Aragam Advances in Neural Information Processing Systems 35, 15687-15701, 2022 | 57 | 2022 |
| Learning latent causal graphs via mixture oracles B Kivva, G Rajendran, P Ravikumar, B Aragam Advances in Neural Information Processing Systems 34, 18087-18101, 2021 | 54 | 2021 |
| Sum-of-squares lower bounds for sherrington-kirkpatrick via planted affine planes M Ghosh, FG Jeronimo, C Jones, A Potechin, G Rajendran 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS …, 2020 | 54 | 2020 |
| Learning linear causal representations from interventions under general nonlinear mixing S Buchholz, G Rajendran, E Rosenfeld, B Aragam, B Schölkopf, ... Advances in Neural Information Processing Systems 36, 2024 | 50 | 2024 |
| Sum-of-squares lower bounds for sparse independent set C Jones, A Potechin, G Rajendran, M Tulsiani, J Xu 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 31 | 2022 |
| Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families G Rajendran, B Kivva, M Gao, B Aragam Advances in Neural Information Processing Systems 34, 18660-18672, 2021 | 21 | 2021 |
| Identifiability of deep generative models under mixture priors without auxiliary information B Kivva, G Rajendran, PK Ravikumar, B Aragam UAI 2022 Workshop on Causal Representation Learning, 2022 | 18 | 2022 |
| Machinery for proving sum-of-squares lower bounds on certification problems A Potechin, G Rajendran arXiv preprint arXiv:2011.04253, 2020 | 18 | 2020 |
| On the origins of linear representations in large language models Y Jiang, G Rajendran, P Ravikumar, B Aragam, V Veitch arXiv preprint arXiv:2403.03867, 2024 | 12 | 2024 |
| Sum-of-squares lower bounds for densest k-subgraph C Jones, A Potechin, G Rajendran, J Xu Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 84-95, 2023 | 12 | 2023 |
| Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis A Potechin, G Rajendran Advances in Neural Information Processing Systems 35, 35724-35740, 2022 | 12 | 2022 |
| Concentration of polynomial random matrices via Efron-Stein inequalities G Rajendran, M Tulsiani Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2023 | 11 | 2023 |
| Learning interpretable concepts: Unifying causal representation learning and foundation models G Rajendran, S Buchholz, B Aragam, B Schölkopf, P Ravikumar arXiv preprint arXiv:2402.09236, 2024 | 8 | 2024 |
| An interventional perspective on identifiability in gaussian lti systems with independent component analysis G Rajendran, P Reizinger, W Brendel, PK Ravikumar Causal Learning and Reasoning, 41-70, 2024 | 6 | 2024 |
| Analyzing robustness of end-to-end neural models for automatic speech recognition G Rajendran, W Zou arXiv preprint arXiv:2208.08509, 2022 | 4 | 2022 |
| Combinatorial optimization via the sum of squares hierarchy G Rajendran arXiv preprint arXiv:2208.04374, 2022 | 4 | 2022 |
| Nonlinear Random Matrices and Applications to the Sum of Squares Hierarchy G Rajendran The University of Chicago, 2022 | 4 | 2022 |
| Efficient certificates of anti-concentration beyond gaussians A Bakshi, PK Kothari, G Rajendran, M Tulsiani, A Vijayaraghavan 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS …, 2024 | 2 | 2024 |
| Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers Y Jiang, G Rajendran, P Ravikumar, B Aragam arXiv preprint arXiv:2406.18400, 2024 | 2 | 2024 |
| From Causal to Concept-Based Representation Learning G Rajendran, S Buchholz, B Aragam, B Schölkopf, PK Ravikumar The Thirty-eighth Annual Conference on Neural Information Processing Systems, 0 | | |
