Search Results for author:
Found 11 papers, 3 papers with code
Quantized Distributed Training of Large Models with Convergence Guarantees
Communication-reduction techniques are a popular way to improve scalability in data-parallel training of deep neural networks (DNNs).
CrAM: A Compression-Aware Minimizer
In this work we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way, in order to produce models whose local loss behavior is stable under compression operations such as pruning.
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
The increasing computational requirements of deep neural networks (DNNs) have led to significant interest in obtaining DNN models that are sparse, yet accurate.
Ranked #1 on
Network Pruning
on CIFAR-100
Decomposable Submodular Function Minimization via Maximum Flow
This paper bridges discrete and continuous optimization approaches for decomposable submodular function minimization, in both the standard and parametric settings.
Projection-Free Bandit Optimization with Privacy Guarantees
We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting.
Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities
We provide new adaptive first-order methods for constrained convex optimization.
Improved Convergence for $\ell_\infty$ and $\ell_1$ Regression via Iteratively Reweighted Least Squares
The iteratively reweighted least squares method (IRLS) is a popular technique used in practice for solving regression problems.
Data Structures and Algorithms
A Parallel Double Greedy Algorithm for Submodular Maximization
We study parallel algorithms for the problem of maximizing a non-negative submodular function.
Towards Deep Learning Models Resistant to Adversarial Attacks
Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.
Multidimensional Binary Search for Contextual Decision-Making
We consider a multidimensional search problem that is motivated by questions in contextual decision-making, such as dynamic pricing and personalized medicine.
Tight Bounds for Approximate Carathéodory and Beyond
We give a deterministic nearly-linear time algorithm for approximating any point inside a convex polytope with a sparse convex combination of the polytope's vertices.
