Counting Markov Equivalence Classes by Number of Immoralities. - Adityanarayanan Radhakrishnan; Liam Solus; Caroline Uhler (UAI, 2017)

Patchnet: Interpretable Neural Networks for Image Classification. - Adityanarayanan Radhakrishnan; Charles Durham; Ali Soylemezoglu; Caroline Uhler (CoRR, 2017)

Downsampling leads to Image Memorization in Convolutional Autoencoders. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2018)

Counting Markov equivalence classes for DAG models on trees. - Adityanarayanan Radhakrishnan; Liam Solus; Caroline Uhler (Discret. Appl. Math., 2018)

Overparameterized Neural Networks Can Implement Associative Memory. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2019)

Balancedness and Alignment are Unlikely in Linear Neural Networks. - Adityanarayanan Radhakrishnan; Eshaan Nichani; Daniel Irving Bernstein; Caroline Uhler (CoRR, 2020)

Linear Convergence and Implicit Regularization of Generalized Mirror Descent with Time-Dependent Mirrors. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2020)

Do Deeper Convolutional Networks Perform Better? - Eshaan Nichani; Adityanarayanan Radhakrishnan; Caroline Uhler (CoRR, 2020)

Overparameterized neural networks implement associative memory. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (Proc. Natl. Acad. Sci. USA, 2020)

A Mechanism for Producing Aligned Latent Spaces with Autoencoders. - Saachi Jain; Adityanarayanan Radhakrishnan; Caroline Uhler (CoRR, 2021)

Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks. - Adityanarayanan Radhakrishnan; George Stefanakis; Mikhail Belkin; Caroline Uhler (CoRR, 2021)

Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2021)

Wide and Deep Neural Networks Achieve Optimality for Classification. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Caroline Uhler (CoRR, 2022)

Quadratic models for understanding neural network dynamics. - Libin Zhu; Chaoyue Liu 001; Adityanarayanan Radhakrishnan; Mikhail Belkin (CoRR, 2022)

Transfer Learning with Kernel Methods. - Adityanarayanan Radhakrishnan; Max Ruiz Luyten; Neha Prasad; Caroline Uhler (CoRR, 2022)

Feature learning in neural networks and kernel machines that recursively learn features. - Adityanarayanan Radhakrishnan; Daniel Beaglehole; Parthe Pandit; Mikhail Belkin (CoRR, 2022)

Mechanism of feature learning in convolutional neural networks. - Daniel Beaglehole; Adityanarayanan Radhakrishnan; Parthe Pandit; Mikhail Belkin (CoRR, 2023)

Foundations of Machine Learning: Over-parameterization and Feature Learning - Adityanarayanan Radhakrishnan (2023)

Quadratic models for understanding catapult dynamics of neural networks. - Libin Zhu; Chaoyue Liu 001; Adityanarayanan Radhakrishnan; Mikhail Belkin (ICLR, 2024)

Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning. - Libin Zhu; Chaoyue Liu 001; Adityanarayanan Radhakrishnan; Mikhail Belkin (ICML, 2024)

Linear Recursive Feature Machines provably recover low-rank matrices. - Adityanarayanan Radhakrishnan; Mikhail Belkin; Dmitriy Drusvyatskiy (CoRR, 2024)

Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. - Neil Mallinar; Daniel Beaglehole; Libin Zhu; Adityanarayanan Radhakrishnan; Parthe Pandit; Mikhail Belkin (CoRR, 2024)

Context-Scaling versus Task-Scaling in In-Context Learning. - Amirhesam Abedsoltan; Adityanarayanan Radhakrishnan; Jingfeng Wu; Mikhail Belkin (CoRR, 2024)

Aggregate and conquer: detecting and steering LLM concepts by combining nonlinear predictors over multiple layers. - Daniel Beaglehole; Adityanarayanan Radhakrishnan; Enric Boix-Adserà; Mikhail Belkin (CoRR, 2025)