Search Results for author:
Found 9 papers, 3 papers with code
High-dimensional SGD aligns with emerging outlier eigenspaces
We rigorously study the joint evolution of training dynamics via stochastic gradient descent (SGD) and the spectra of empirical Hessian and gradient matrices.
Optimality of Message-Passing Architectures for Sparse Graphs
We study the node classification problem on feature-decorated graphs in the sparse setting, i. e., when the expected degree of a node is $O(1)$ in the number of nodes, in the fixed-dimensional asymptotic regime, i. e., the dimension of the feature data is fixed while the number of nodes is large.
Differentially private multivariate medians
We develop novel finite-sample performance guarantees for differentially private multivariate depth-based medians, which are essentially sharp.
High-dimensional limit theorems for SGD: Effective dynamics and critical scaling
We prove limit theorems for the trajectories of summary statistics (i. e., finite-dimensional functions) of SGD as the dimension goes to infinity.
Effects of Graph Convolutions in Multi-layer Networks
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information.
Graph Attention Retrospective
They were introduced to allow a node to aggregate information from features of neighbor nodes in a non-uniform way, in contrast to simple graph convolution which does not distinguish the neighbors of a node.
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Recently there has been increased interest in semi-supervised classification in the presence of graphical information.
General Classification
Out-of-Distribution Generalization
+1
Hardness of Random Optimization Problems for Boolean Circuits, Low-Degree Polynomials, and Langevin Dynamics
For the case of Boolean circuits, our results improve the state-of-the-art bounds known in circuit complexity theory (although we consider the search problem as opposed to the decision problem).
Online stochastic gradient descent on non-convex losses from high-dimensional inference
Here one produces an estimator of an unknown parameter from independent samples of data by iteratively optimizing a loss function.
