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Pradeep Ravikumar
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Publications
- 2024
- [i83]Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. CoRR abs/2402.09236 (2024) - [i82]Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch:
On the Origins of Linear Representations in Large Language Models. CoRR abs/2403.03867 (2024) - 2023
- [c140]Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar:
Optimizing NOTEARS Objectives via Topological Swaps. ICML 2023: 7563-7595 - [c138]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS 2023 - [c137]Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. NeurIPS 2023 - [c136]Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam:
Global Optimality in Bivariate Gradient-based DAG Learning. NeurIPS 2023 - [p2]Bryon Aragam, Pradeep Ravikumar:
Neuro-Causal Models. Compendium of Neurosymbolic Artificial Intelligence 2023: 153-177 - [i79]Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
Optimizing NOTEARS Objectives via Topological Swaps. CoRR abs/2305.17277 (2023) - [i76]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR abs/2306.02235 (2023) - [i75]Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. CoRR abs/2306.17361 (2023) - [i74]Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
Global Optimality in Bivariate Gradient-based DAG Learning. CoRR abs/2306.17378 (2023) - 2022
- [j10]Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar:
Fundamental Limits and Tradeoffs in Invariant Representation Learning. J. Mach. Learn. Res. 23: 340:1-340:49 (2022) - [c123]Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. NeurIPS 2022 - [c122]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Identifiability of deep generative models without auxiliary information. NeurIPS 2022 - [i61]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Identifiability of deep generative models under mixture priors without auxiliary information. CoRR abs/2206.10044 (2022) - [i59]Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. CoRR abs/2209.08037 (2022) - 2021
- [c110]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Learning latent causal graphs via mixture oracles. NeurIPS 2021: 18087-18101 - [i49]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Learning latent causal graphs via mixture oracles. CoRR abs/2106.15563 (2021) - 2020
- [c107]Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Learning Sparse Nonparametric DAGs. AISTATS 2020: 3414-3425 - [c95]David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar:
Automated Dependence Plots. UAI 2020: 1238-1247 - [i35]Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar:
Fundamental Limits and Tradeoffs in Invariant Representation Learning. CoRR abs/2012.10713 (2020) - 2019
- [i30]Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Learning Sparse Nonparametric DAGs. CoRR abs/1909.13189 (2019) - [i27]David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar:
Diagnostic Curves for Black Box Models. CoRR abs/1912.01108 (2019) - 2018
- [c81]Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. NeurIPS 2018: 9344-9354 - [c80]Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
DAGs with NO TEARS: Continuous Optimization for Structure Learning. NeurIPS 2018: 9492-9503 - [i25]Bryon Aragam, Chen Dan, Pradeep Ravikumar, Eric P. Xing:
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering. CoRR abs/1802.04397 (2018) - [i22]Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
DAGs with NO TEARS: Smooth Optimization for Structure Learning. CoRR abs/1803.01422 (2018) - [i18]Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Sample Complexity of Nonparametric Semi-Supervised Learning. CoRR abs/1809.03073 (2018)
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