← Authors

Bryon Aragam

36 papers · 2015–2025 · 6 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (6) 🌈 Renaissance Researcher (5) 🐝 Cross-Pollinator (13) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (10) πŸ—ΊοΈ Taxonomy Completionist (16) 🐣 Hot Topic Early Bird 🏠 Conference Loyalist (21) πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (5) πŸ’Ž Century Club (36) ⚑ Prolific Year (9) πŸ“ˆ Trend Setter ❓ The Questioner πŸ”₯ Unstoppable (8) πŸ—ƒοΈ Keyword Collector (145)

Conferences

NIPS (21) AISTATS (6) ICML (5) JMLR (2) COLT (1) UAI (1)

Top co-authors

Pradeep K Ravikumar (8) Pradeep Ravikumar (7) Goutham Rajendran (7) Wai Ming Tai (7) Pradeep K. Ravikumar (6) Kevin Bello (6) Ming Gao (5) Yibo Jiang (4) Qing Zhou (3) Eric P. Xing (3)

Research topics

Probability (1)

Keywords

structure learning (13) directed acyclic graph (12) graphical model (10) causal discovery (8) causal inference (6) sample complexity (6) gaussian graphical model (4) latent variable (3) continuous optimization (3) score-based learning (3) causal mechanism shift (2) structural causal model (2) representation learning (2) causal representation learning (2) causal representation (2) non-convex optimization (2) latent variable model (2) conditional independence (2) information theory (2) bayesian network (2)

Papers

Dimension-Independent Rates for Structured Neural Density Estimation ICML 2025
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models AISTATS 2024
Identifying General Mechanism Shifts in Linear Causal Representations NIPS 2024
Breaking the curse of dimensionality in structured density estimation NIPS 2024
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers NIPS 2024
Markov Equivalence and Consistency in Differentiable Structure Learning NIPS 2024
From Causal to Concept-Based Representation Learning NIPS 2024
On the Origins of Linear Representations in Large Language Models ICML 2024
Optimal estimation of Gaussian (poly)trees AISTATS 2024
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures COLT 2023
Learning Mixtures of Gaussians with Censored Data ICML 2023
Optimizing NOTEARS Objectives via Topological Swaps ICML 2023
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models NIPS 2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing NIPS 2023
Assumption violations in causal discovery and the robustness of score matching NIPS 2023
Learning Nonparametric Latent Causal Graphs with Unknown Interventions NIPS 2023
Global Optimality in Bivariate Gradient-based DAG Learning NIPS 2023
Uncovering Meanings of Embeddings via Partial Orthogonality NIPS 2023
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization NIPS 2022
Optimal estimation of Gaussian DAG models AISTATS 2022
On perfectness in Gaussian graphical models AISTATS 2022
Fundamental Limits and Tradeoffs in Invariant Representation Learning JMLR 2022
Identifiability of deep generative models without auxiliary information NIPS 2022
Learning latent causal graphs via mixture oracles NIPS 2021
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families NIPS 2021
Efficient Bayesian network structure learning via local Markov boundary search NIPS 2021
Automated Dependence Plots UAI 2020
A polynomial-time algorithm for learning nonparametric causal graphs NIPS 2020
DYNOTEARS: Structure Learning from Time-Series Data AISTATS 2020
Learning Sparse Nonparametric DAGs AISTATS 2020
Fault Tolerance in Iterative-Convergent Machine Learning ICML 2019
Learning Sample-Specific Models with Low-Rank Personalized Regression NIPS 2019
Globally optimal score-based learning of directed acyclic graphs in high-dimensions NIPS 2019
DAGs with NO TEARS: Continuous Optimization for Structure Learning NIPS 2018
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models NIPS 2018
Concave Penalized Estimation of Sparse Gaussian Bayesian Networks JMLR 2015