161 citations · 1k across the 69 of their papers we have counts for
14 papers · 1 filter
Synthetic Potential Outcomes and Causal Mixture Identifiability
Bijan Mazaheri, Chandler Squires, Caroline Uhler
Heterogeneous data from multiple populations, sub-groups, or sources is often represented as a ``mixture model'' with a single latent class influencing all of the observed covariat…
Transfer Learning with Kernel Methods
Adityanarayanan Radhakrishnan, Max Ruiz Luyten, Neha Prasad +1
Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple machine lear…
Active Learning for Optimal Intervention Design in Causal Models
Jiaqi Zhang, Louis Cammarata, Chandler Squires +2
Sequential experimental design to discover interventions that achieve a desired outcome is a key problem in various domains including science, engineering and public policy. When t…
Wide and Deep Neural Networks Achieve Optimality for Classification
Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
While neural networks are used for classification tasks across domains, a long-standing open problem in machine learning is determining whether neural networks trained using standa…
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…
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…
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…
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…
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…
Multi-Domain Translation by Learning Uncoupled Autoencoders
Karren D. Yang, Caroline Uhler
Multi-domain translation seeks to learn a probabilistic coupling between marginal distributions that reflects the correspondence between different domains. We assume that data from…
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
Karren D. Yang, Caroline Uhler
Generative adversarial networks (GANs) are an expressive class of neural generative models with tremendous success in modeling high-dimensional continuous measures. In this paper,…