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Found 13 papers, 3 papers with code
Path convergence of Markov chains on large graphs
In both cases we show that, as the size of the graph goes to infinity, the random trajectories of the stochastic processes converge to deterministic curves on the space of measure-valued graphons.
Stochastic optimization on matrices and a graphon McKean-Vlasov limit
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 permutation.
Gradient flows on graphons: existence, convergence, continuity equations
Wasserstein gradient flows on probability measures have found a host of applications in various optimization problems.
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
We further quantitatively measure the quality of our codes by applying it to the efficient image retrieval as well as out-of-distribution (OOD) detection problems.
SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics
There have been promising attempts to use the intermediate representations of such a model to separate corrupted examples from clean ones.
Robust Meta-learning for Mixed Linear Regression with Small Batches
Together, this approach is robust against outliers and achieves a graceful statistical trade-off; the lack of $\Omega(k^{1/2})$-size tasks can be compensated for with smaller tasks, which can now be as small as $O(\log k)$.
Meta-learning for mixed linear regression
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.
Soft Threshold Weight Reparameterization for Learnable Sparsity
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of maximizing prediction accuracy given an overall parameter budget.
Non-Gaussianity of Stochastic Gradient Noise
What enables Stochastic Gradient Descent (SGD) to achieve better generalization than Gradient Descent (GD) in Neural Network training?
Universality Patterns in the Training of Neural Networks
This paper proposes and demonstrates a surprising pattern in the training of neural networks: there is a one to one relation between the values of any pair of losses (such as cross entropy, mean squared error, 0/1 error etc.)
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
This paper studies the problem of sparse regression where the goal is to learn a sparse vector that best optimizes a given objective function.
Clustered Monotone Transforms for Rating Factorization
This non-linear transformation of the rating scale shatters the low-rank structure of the rating matrix, therefore resulting in a poor fit and consequentially, poor recommendations.
A case study of Empirical Bayes in User-Movie Recommendation system
In this article we provide a formulation of empirical bayes described by Atchade (2011) to tune the hyperparameters of priors used in bayesian set up of collaborative filter.
