Preprints
A Cross-modal Autoencoder Framework Learns Holisitic Representations of Cardiovascular State [bioRxiv]
Adityanarayanan Radhakrishnan*, Samuel Freesun Friedman*, Shaan Khurshid, Kenney Ng, Puneet Batra, Steven Lubitz, Anthony Philippakis, Caroline Uhler.
Accepted in Nature Communications.
Wide and Deep Networks Achieve Consistency in Classification [arXiv]
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
Under review in PNAS.
Transfer Learning with Kernel Methods [arXiv]
Adityanarayanan Radhakrishnan*, Max Ruiz Luyten*, Neha Prasad, Caroline Uhler.
Under review in Nature Communications.
Feature learning in neural networks and kernel machines that recursively learn features [arXiv]
Adityanarayanan Radhakrishnan*, Daniel Beaglehole*, Parthe Pandit, Mikhail Belkin.
Quadratic Models for Understanding Neural Network Dynamics [arXiv]
Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin
Submitted to ICML.
A Mechanism for Producing Aligned Latent Spaces with Autoencoders [arXiv]
Saachi Jain*, Adityanarayanan Radhakrishnan*, Caroline Uhler
Linear Convergence of Generalized Mirror Descent with Time-dependent Mirrors [arXiv]
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
2022
Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks [Link]
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
PNAS 119, Article 16
2021
Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing [Link]
Anastasiya Belyaeva*, Louis Cammarata*, Adityanarayanan Radhakrishnan*, Chandler Squires, Karren Dai Yang, G.V. Shivashankar, Caroline Uhler
Nature Communications 12, Article 1024
Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size [arXiv]
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
ICML Workshop on Beyond first-order methods in ML systems
Do Deeper Convolutional Networks Perform Better? [arXiv]
Eshaan Nichani*, Adityanarayanan Radhakrishnan*, Caroline Uhler.
ICML Workshop on Over-parameterization: Pitfalls and Opportunities
On Alignment in Deep Linear Neural Networks [arXiv]
Adityanarayanan Radhakrishnan*, Eshaan Nichani*, Daniel Bernstein, Caroline Uhler
ICML Workshop on Over-parameterization: Pitfalls and Opportunities
Multi-domain translation between single-cell imaging and sequencing data using autoencoders [Link]
Karren Yang, Anastasiya Belyaeva, Saradha Venkatachalapathy, Karthik Damodaran, Abigail Katcoff, Adityanarayanan Radhakrishnan, G.V. Shivashankar, Caroline Uhler
Nature Communications 12, Article 31
2020
Overparameterized Autoencoders Implement Associative Memory [Link]
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
PNAS 119, Article16.
2019
Memorization in Overparameterized Autoencoders [arXiv, openreview]
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
ICML Workshop on Identifying and Understanding Deep Learning Phenomena.
2018
Patchnet: Interpretable Neural Net- works for Image Classification [arXiv]
Adityanarayanan Radhakrishnan, Charles Durham, Alican Soylemezoglu, Caroline Uhler
NeurIPS Machine Learning for Health (ML4H) Workshop.
Counting Markov equivalence classes for DAG models on trees [arXiv, DOI]
Adityanarayanan Radhakrishnan, Liam Solus, Caroline Uhler
Discrete Applied Mathematics.
2017
Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis [Link]
Adityanarayanan Radhakrishnan, Karthik Damodaran, Alican Soylemezoglu, Caroline Uhler, G.V. Shivashankar
Scientific Reports 7, Article 17946
Counting Markov equivalence classes by number of immoralities [arXiv, workshop link]
Adityanarayanan Radhakrishnan, Liam Solus, Caroline Uhler
UAI Special Workshop on Causality
Adit Radha 