161 citations · 1k across the 69 of their papers we have counts for
11 papers · 1 filter
Efficient Permutation Discovery in Causal DAGs
Chandler Squires, Joshua Amaniampong, Caroline Uhler
The problem of learning a directed acyclic graph (DAG) up to Markov equivalence is equivalent to the problem of finding a permutation of the variables that induces the sparsest gra…
Causal Imputation via Synthetic Interventions
Chandler Squires, Dennis Shen, Anish Agarwal +2
Consider the problem of determining the effect of a compound on a specific cell type. To answer this question, researchers traditionally need to run an experiment applying the drug…
Joint Inference of Multiple Graphs from Matrix Polynomials
Madeline Navarro, Yuhao Wang, Antonio G. Marques +2
Inferring graph structure from observations on the nodes is an important and popular network science task. Departing from the more common inference of a single graph and motivated…
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks
Eshaan Nichani, Adityanarayanan Radhakrishnan, Caroline Uhler
Recent works have demonstrated that increasing model capacity through width in over-parameterized neural networks leads to a decrease in test risk. For neural networks, however, mo…
Linear Convergence of Generalized Mirror Descent with Time-Dependent Mirrors
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
The Polyak-Lojasiewicz (PL) inequality is a sufficient condition for establishing linear convergence of gradient descent, even in non-convex settings. While several recent works us…
Optimal Transport using GANs for Lineage Tracing
Neha Prasad, Karren Yang, Caroline Uhler
In this paper, we present Super-OT, a novel approach to computational lineage tracing that combines a supervised learning framework with optimal transport based on Generative Adver…
Improved Conditional Flow Models for Molecule to Image Synthesis
Karren Yang, Samuel Goldman, Wengong Jin +4
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent…
Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing
Anastasiya Belyaeva, Louis Cammarata, Adityanarayanan Radhakrishnan +4
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and exper…
Multiscale Simulations of Complex Systems by Learning their Effective Dynamics
Pantelis R. Vlachas, Georgios Arampatzis, Caroline Uhler +1
Predictive simulations of complex systems are essential for applications ranging from weather forecasting to drug design. The veracity of these predictions hinges on their capacity…
On Alignment in Deep Linear Neural Networks
Adityanarayanan Radhakrishnan, Eshaan Nichani, Daniel Bernstein +1
We study the properties of alignment, a form of implicit regularization, in linear neural networks under gradient descent. We define alignment for fully connected networks with mul…
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
Basil Saeed, Snigdha Panigrahi, Caroline Uhler
We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG). We provide a graphical representation of such…