161 citations · 1k across the 69 of their papers we have counts for
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Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
Establishing a fast rate of convergence for optimization methods is crucial to their applicability in practice. With the increasing popularity of deep learning over the past decade…
Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample
Michael Truell, Jan-Christian Hütter, Chandler Squires +2
We study the problem of maximum likelihood estimation given one data sample () over Brownian Motion Tree Models (BMTMs), a class of Gaussian models on trees. BMTMs are often u…
Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks
Adityanarayanan Radhakrishnan, George Stefanakis, Mikhail Belkin +1
Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in m…
Matching a Desired Causal State via Shift Interventions
Jiaqi Zhang, Chandler Squires, Caroline Uhler
Transforming a causal system from a given initial state to a desired target state is an important task permeating multiple fields including control theory, biology, and materials s…
A Mechanism for Producing Aligned Latent Spaces with Autoencoders
Saachi Jain, Adityanarayanan Radhakrishnan, Caroline Uhler
Aligned latent spaces, where meaningful semantic shifts in the input space correspond to a translation in the embedding space, play an important role in the success of downstream t…
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
Scott Sussex, Andreas Krause, Caroline Uhler
Causal structure learning is a key problem in many domains. Causal structures can be learnt by performing experiments on the system of interest. We address the largely unexplored p…
The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
Raj Agrawal, Chandler Squires, Neha Prasad +1
Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables ar…
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry
Anastasiya Belyaeva, Kaie Kubjas, Lawrence J. Sun +1
The spatial organization of the DNA in the cell nucleus plays an important role for gene regulation, DNA replication, and genomic integrity. Through the development of chromosome c…