101 citations · 148 across the 10 of their papers we have counts for
10 papers
Path convergence of Markov chains on large graphs
Siva Athreya, Soumik Pal, Raghav Somani +1
We consider two classes of natural stochastic processes on finite unlabeled graphs. These are Euclidean stochastic optimization algorithms on the adjacency matrix of weighted graph…
Stochastic optimization on matrices and a graphon McKean-Vlasov limit
Zaid Harchaoui, Sewoong Oh, Soumik Pal +2
We consider stochastic gradient descents on the space of large symmetric matrices of suitable functions that are invariant under permuting the rows and columns using the same permu…
Gradient flows on graphons: existence, convergence, continuity equations
Sewoong Oh, Soumik Pal, Raghav Somani +1
Wasserstein gradient flows on probability measures have found a host of applications in various optimization problems. They typically arise as the continuum limit of exchangeable p…
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan +6
Learning binary representations of instances and classes is a classical problem with several high potential applications. In modern settings, the compression of high-dimensional ne…
SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics
Jonathan Hayase, Weihao Kong, Raghav Somani +1
Modern machine learning increasingly requires training on a large collection of data from multiple sources, not all of which can be trusted. A particularly concerning scenario is w…
Robust Meta-learning for Mixed Linear Regression with Small Batches
Weihao Kong, Raghav Somani, Sham Kakade +1
A common challenge faced in practical supervised learning, such as medical image processing and robotic interactions, is that there are plenty of tasks but each task cannot afford…
Meta-learning for mixed linear regression
Weihao Kong, Raghav Somani, Zhao Song +2
In modern supervised learning, there are a large number of tasks, but many of them are associated with only a small amount of labeled data. These include data from medical image pr…
Soft Threshold Weight Reparameterization for Learnable Sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani +4
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of maximizing prediction accuracy given an overall parameter budget. Existing methods rely on uniform…
Non-Gaussianity of Stochastic Gradient Noise
Abhishek Panigrahi, Raghav Somani, Navin Goyal +1
What enables Stochastic Gradient Descent (SGD) to achieve better generalization than Gradient Descent (GD) in Neural Network training? This question has attracted much attention. I…
Clustered Monotone Transforms for Rating Factorization
Gaurush Hiranandani, Raghav Somani, Oluwasanmi Koyejo +1
Exploiting low-rank structure of the user-item rating matrix has been the crux of many recommendation engines. However, existing recommendation engines force raters with heterogene…