Bryon Aragam
36 papers · 2015–2025 · 6 conferences · across top CS/AI conferences
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(36)
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Conferences
NIPS (21)
AISTATS (6)
ICML (5)
JMLR (2)
COLT (1)
UAI (1)
Top co-authors
Research topics
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