161 citations · 305 across the 27 of their papers we have counts for
12 papers · 1 filter
Ordering-Based Causal Structure Learning in the Presence of Latent Variables
Daniel Irving Bernstein, Basil Saeed, Chandler Squires +1
We consider the task of learning a causal graph in the presence of latent confounders given i.i.d.~samples from the model. While current algorithms for causal structure discovery i…
Permutation-Based Causal Structure Learning with Unknown Intervention Targets
Chandler Squires, Yuhao Wang, Caroline Uhler
We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention targets are partially or completely unknown. This…
Overparameterized Neural Networks Implement Associative Memory
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
Identifying computational mechanisms for memorization and retrieval of data is a long-standing problem at the intersection of machine learning and neuroscience. Our main finding is…
Covariance Matrix Estimation under Total Positivity for Portfolio Selection
Raj Agrawal, Uma Roy, Caroline Uhler
Selecting the optimal Markowitz porfolio depends on estimating the covariance matrix of the returns of assets from periods of historical data. Problematically, is typic…
Algebraic Statistics in Practice: Applications to Networks
Marta Casanellas, Sonja Petrović, Caroline Uhler
Algebraic statistics uses tools from algebra (especially from multilinear algebra, commutative algebra and computational algebra), geometry and combinatorics to provide insight int…
Anchored Causal Inference in the Presence of Measurement Error
Basil Saeed, Anastasiya Belyaeva, Yuhao Wang +1
We consider the problem of learning a causal graph in the presence of measurement error. This setting is for example common in genomics, where gene expression is corrupted through…