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Bryon Aragam
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2020 – today
- 2024
- [c29]Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya:
Optimal estimation of Gaussian (poly)trees. AISTATS 2024: 3619-3627 - [c28]Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam:
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. AISTATS 2024: 3691-3699 - [c27]Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch:
On the Origins of Linear Representations in Large Language Models. ICML 2024 - [i44]Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya:
Optimal estimation of Gaussian (poly)trees. CoRR abs/2402.06380 (2024) - [i43]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) - [i42]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) - [i41]Bryon Aragam:
Greedy equivalence search for nonparametric graphical models. CoRR abs/2406.17228 (2024) - [i40]Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. CoRR abs/2406.18400 (2024) - [i39]Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam:
Likelihood-based Differentiable Structure Learning. CoRR abs/2410.06163 (2024) - [i38]Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam:
Breaking the curse of dimensionality in structured density estimation. CoRR abs/2410.07685 (2024) - [i37]Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar:
Identifying General Mechanism Shifts in Linear Causal Representations. CoRR abs/2410.24059 (2024) - [i36]Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam:
Dimension-independent rates for structured neural density estimation. CoRR abs/2411.15095 (2024) - 2023
- [c26]Wai Ming Tai, Bryon Aragam:
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures. COLT 2023: 2849 - [c25]Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar:
Optimizing NOTEARS Objectives via Topological Swaps. ICML 2023: 7563-7595 - [c24]Wai Ming Tai, Bryon Aragam:
Learning Mixtures of Gaussians with Censored Data. ICML 2023: 33396-33415 - [c23]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS 2023 - [c22]Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. NeurIPS 2023 - [c21]Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam:
Global Optimality in Bivariate Gradient-based DAG Learning. NeurIPS 2023 - [c20]Yibo Jiang, Bryon Aragam:
Learning Nonparametric Latent Causal Graphs with Unknown Interventions. NeurIPS 2023 - [c19]Yibo Jiang, Bryon Aragam, Victor Veitch:
Uncovering Meanings of Embeddings via Partial Orthogonality. NeurIPS 2023 - [c18]Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello:
Assumption violations in causal discovery and the robustness of score matching. NeurIPS 2023 - [p1]Bryon Aragam, Pradeep Ravikumar:
Neuro-Causal Models. Compendium of Neurosymbolic Artificial Intelligence 2023: 153-177 - [i35]Wai Ming Tai, Bryon Aragam:
Learning Mixtures of Gaussians with Censored Data. CoRR abs/2305.04127 (2023) - [i34]Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
Optimizing NOTEARS Objectives via Topological Swaps. CoRR abs/2305.17277 (2023) - [i33]Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus:
Neuro-Causal Factor Analysis. CoRR abs/2305.19802 (2023) - [i32]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) - [i31]Yibo Jiang, Bryon Aragam:
Learning nonparametric latent causal graphs with unknown interventions. CoRR abs/2306.02899 (2023) - [i30]Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. CoRR abs/2306.17361 (2023) - [i29]Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
Global Optimality in Bivariate Gradient-based DAG Learning. CoRR abs/2306.17378 (2023) - [i28]Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello:
Assumption violations in causal discovery and the robustness of score matching. CoRR abs/2310.13387 (2023) - [i27]Yibo Jiang, Bryon Aragam, Victor Veitch:
Uncovering Meanings of Embeddings via Partial Orthogonality. CoRR abs/2310.17611 (2023) - [i26]Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam:
Inconsistency of cross-validation for structure learning in Gaussian graphical models. CoRR abs/2312.17047 (2023) - 2022
- [j5]Haohan Wang, Bryon Aragam, Eric P. Xing:
Trade-offs of Linear Mixed Models in Genome-Wide Association Studies. J. Comput. Biol. 29(3): 233-242 (2022) - [j4]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) - [c17]Arash A. Amini, Bryon Aragam, Qing Zhou:
On perfectness in Gaussian graphical models. AISTATS 2022: 7505-7517 - [c16]Ming Gao, Wai Ming Tai, Bryon Aragam:
Optimal estimation of Gaussian DAG models. AISTATS 2022: 8738-8757 - [c15]Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. NeurIPS 2022 - [c14]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Identifiability of deep generative models without auxiliary information. NeurIPS 2022 - [i25]Ming Gao, Wai Ming Tai, Bryon Aragam:
Optimal estimation of Gaussian DAG models. CoRR abs/2201.10548 (2022) - [i24]Bryon Aragam, Wai Ming Tai:
A super-polynomial lower bound for learning nonparametric mixtures. CoRR abs/2203.15150 (2022) - [i23]Arash A. Amini, Bryon Aragam, Qing Zhou:
A non-graphical representation of conditional independence via the neighbourhood lattice. CoRR abs/2206.05829 (2022) - [i22]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Identifiability of deep generative models under mixture priors without auxiliary information. CoRR abs/2206.10044 (2022) - [i21]Kevin Bello, Bryon Aragam, Pradeep Ravikumar:
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. CoRR abs/2209.08037 (2022) - 2021
- [c13]Ming Gao, Bryon Aragam:
Efficient Bayesian network structure learning via local Markov boundary search. NeurIPS 2021: 4301-4313 - [c12]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Learning latent causal graphs via mixture oracles. NeurIPS 2021: 18087-18101 - [c11]Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam:
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. NeurIPS 2021: 18660-18672 - [i20]Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam:
Learning latent causal graphs via mixture oracles. CoRR abs/2106.15563 (2021) - [i19]Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam:
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. CoRR abs/2110.04719 (2021) - [i18]Ming Gao, Bryon Aragam:
Efficient Bayesian network structure learning via local Markov boundary search. CoRR abs/2110.06082 (2021) - [i17]Benjamin J. Lengerich, Caleb Ellington, Bryon Aragam, Eric P. Xing, Manolis Kellis:
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters. CoRR abs/2111.01104 (2021) - [i16]Haohan Wang, Bryon Aragam, Eric P. Xing:
Tradeoffs of Linear Mixed Models in Genome-wide Association Studies. CoRR abs/2111.03739 (2021) - 2020
- [c10]Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, Bryon Aragam:
DYNOTEARS: Structure Learning from Time-Series Data. AISTATS 2020: 1595-1605 - [c9]Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Learning Sparse Nonparametric DAGs. AISTATS 2020: 3414-3425 - [c8]Ming Gao, Yi Ding, Bryon Aragam:
A polynomial-time algorithm for learning nonparametric causal graphs. NeurIPS 2020 - [c7]David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar:
Automated Dependence Plots. UAI 2020: 1238-1247 - [i15]Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Paul Beaumont, Konstantinos Georgatzis, Bryon Aragam:
DYNOTEARS: Structure Learning from Time-Series Data. CoRR abs/2002.00498 (2020) - [i14]Ming Gao, Yi Ding, Bryon Aragam:
A polynomial-time algorithm for learning nonparametric causal graphs. CoRR abs/2006.11970 (2020) - [i13]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)
2010 – 2019
- 2019
- [j3]Haohan Wang, Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data. Bioinform. 35(7): 1181-1187 (2019) - [c6]Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing:
Fault Tolerance in Iterative-Convergent Machine Learning. ICML 2019: 5220-5230 - [c5]Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Learning Sample-Specific Models with Low-Rank Personalized Regression. NeurIPS 2019: 3570-3580 - [c4]Bryon Aragam, Arash A. Amini, Qing Zhou:
Globally optimal score-based learning of directed acyclic graphs in high-dimensions. NeurIPS 2019: 4452-4464 - [i12]Arash A. Amini, Bryon Aragam, Qing Zhou:
On perfectness in Gaussian graphical models. CoRR abs/1909.01978 (2019) - [i11]Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Learning Sparse Nonparametric DAGs. CoRR abs/1909.13189 (2019) - [i10]Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Learning Sample-Specific Models with Low-Rank Personalized Regression. CoRR abs/1910.06939 (2019) - [i9]David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar:
Diagnostic Curves for Black Box Models. CoRR abs/1912.01108 (2019) - 2018
- [j2]Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Personalized regression enables sample-specific pan-cancer analysis. Bioinform. 34(13): i178-i186 (2018) - [c3]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 - [c2]Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
DAGs with NO TEARS: Continuous Optimization for Structure Learning. NeurIPS 2018: 9492-9503 - [i8]Bryon Aragam, Chen Dan, Pradeep Ravikumar, Eric P. Xing:
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering. CoRR abs/1802.04397 (2018) - [i7]Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
DAGs with NO TEARS: Smooth Optimization for Structure Learning. CoRR abs/1803.01422 (2018) - [i6]Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Sample Complexity of Nonparametric Semi-Supervised Learning. CoRR abs/1809.03073 (2018) - [i5]Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing:
Fault Tolerance in Iterative-Convergent Machine Learning. CoRR abs/1810.07354 (2018) - 2017
- [c1]Haohan Wang, Bryon Aragam, Eric P. Xing:
Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. BIBM 2017: 431-438 - [i4]Bryon Aragam, Jiaying Gu, Qing Zhou:
Learning Large-Scale Bayesian Networks with the sparsebn Package. CoRR abs/1703.04025 (2017) - [i3]Arash A. Amini, Bryon Aragam, Qing Zhou:
Partial correlation graphs and the neighborhood lattice. CoRR abs/1711.00991 (2017) - 2015
- [j1]Bryon Aragam, Qing Zhou:
Concave penalized estimation of sparse Gaussian Bayesian networks. J. Mach. Learn. Res. 16: 2273-2328 (2015) - [i2]Bryon Aragam, Arash A. Amini, Qing Zhou:
Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression. CoRR abs/1511.08963 (2015) - 2014
- [i1]Bryon Aragam, Qing Zhou:
Concave Penalized Estimation of Sparse Bayesian Networks. CoRR abs/1401.0852 (2014)
Coauthor Index
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last updated on 2025-01-02 18:19 CET by the dblp team
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