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Quantized Distributed Training of Large Models with Convergence Guarantees
Ilia Markov, Adrian Vladu, Qi Guo +1
Communication-reduction techniques are a popular way to improve scalability in data-parallel training of deep neural networks (DNNs). The recent emergence of large language models…
CrAM: A Compression-Aware Minimizer
Alexandra Peste, Adrian Vladu, Eldar Kurtic +2
Deep neural networks (DNNs) often have to be compressed, via pruning and/or quantization, before they can be deployed in practical settings. In this work we propose a new compressi…
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
Alexandra Peste, Eugenia Iofinova, Adrian Vladu +1
The increasing computational requirements of deep neural networks (DNNs) have led to significant interest in obtaining DNN models that are sparse, yet accurate. Recent work has inv…
Projection-Free Bandit Optimization with Privacy Guarantees
Alina Ene, Huy L. Nguyen, Adrian Vladu
We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting. This setting is important whenever the decision set has a com…
Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities
Alina Ene, Huy L. Nguyen, Adrian Vladu
We provide new adaptive first-order methods for constrained convex optimization. Our main algorithms AdaACSA and AdaAGD+ are accelerated methods, which are universal in the sense t…