ML Anthology

Aragam, Bryon

45 publications

NeurIPS 2025 Differentiable Structure Learning and Causal Discovery for General Binary Data Chang Deng, Bryon Aragam
ICML 2025 Dimension-Independent Rates for Structured Neural Density Estimation Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
NeurIPS 2024 Breaking the Curse of Dimensionality in Structured Density Estimation Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
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
NeurIPS 2024 Identifying General Mechanism Shifts in Linear Causal Representations Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar
AISTATS 2024 Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
NeurIPS 2024 Markov Equivalence and Consistency in Differentiable Structure Learning Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
ICML 2024 On the Origins of Linear Representations in Large Language Models Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch
AISTATS 2024 Optimal Estimation of Gaussian (poly)trees Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
NeurIPS 2023 Assumption Violations in Causal Discovery and the Robustness of Score Matching Francesco Montagna, Atalanti Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
NeurIPS 2023 Global Optimality in Bivariate Gradient-Based DAG Learning Chang Deng, Kevin Bello, Pradeep K. Ravikumar, Bryon Aragam
ICMLW 2023 Global Optimality in Bivariate Gradient-Based DAG Learning Chang Deng, Kevin Bello, Pradeep Kumar Ravikumar, Bryon Aragam
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
ICML 2023 Learning Mixtures of Gaussians with Censored Data Wai Ming Tai, Bryon Aragam
NeurIPS 2023 Learning Nonparametric Latent Causal Graphs with Unknown Interventions Yibo Jiang, Bryon Aragam
ICMLW 2023 Neuro-Causal Factor Analysis Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus
ICML 2023 Optimizing NOTEARS Objectives via Topological Swaps Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar
COLT 2023 Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures Wai Ming Tai, Bryon Aragam
NeurIPS 2023 Uncovering Meanings of Embeddings via Partial Orthogonality Yibo Jiang, Bryon Aragam, Victor Veitch
NeurIPS 2023 iSCAN: Identifying Causal Mechanism Shifts Among Nonlinear Additive Noise Models Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep K. Ravikumar
AISTATS 2022 On Perfectness in Gaussian Graphical Models Arash Amini, Bryon Aragam, Qing Zhou
AISTATS 2022 Optimal Estimation of Gaussian DAG Models Ming Gao, Wai Ming Tai, Bryon Aragam
NeurIPS 2022 DAGMA: Learning DAGs via M-Matrices and a Log-Determinant Acyclicity Characterization Kevin Bello, Bryon Aragam, Pradeep K. Ravikumar
JMLR 2022 Fundamental Limits and Tradeoffs in Invariant Representation Learning Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
NeurIPS 2022 Identifiability of Deep Generative Models Without Auxiliary Information Bohdan Kivva, Goutham Rajendran, Pradeep K. Ravikumar, Bryon Aragam
NeurIPS 2021 Efficient Bayesian Network Structure Learning via Local Markov Boundary Search Ming Gao, Bryon Aragam
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
NeurIPS 2020 A Polynomial-Time Algorithm for Learning Nonparametric Causal Graphs Ming Gao, Yi Ding, Bryon Aragam
UAI 2020 Automated Dependence Plots David Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
AISTATS 2020 DYNOTEARS: Structure Learning from Time-Series Data Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, Bryon Aragam
AISTATS 2020 Learning Sparse Nonparametric DAGs Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric Xing
ICMLW 2019 Every Sample a Task: Pushing the Limits of Heterogeneous Models with Personalized Regression Ben Lengerich, Bryon Aragam, Eric Xing
ICML 2019 Fault Tolerance in Iterative-Convergent Machine Learning Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric Xing
NeurIPS 2019 Globally Optimal Score-Based Learning of Directed Acyclic Graphs in High-Dimensions Bryon Aragam, Arash Amini, Qing Zhou
NeurIPS 2019 Learning Sample-Specific Models with Low-Rank Personalized Regression Ben Lengerich, Bryon Aragam, Eric P Xing
NeurIPS 2018 DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, Eric P Xing
NeurIPS 2018 The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models Chen Dan, Liu Leqi, Bryon Aragam, Pradeep K Ravikumar, Eric P Xing
JMLR 2015 Concave Penalized Estimation of Sparse Gaussian Bayesian Networks Bryon Aragam, Qing Zhou