Adrian Vladu

According to our database1, Adrian Vladu authored at least 28 papers between 2010 and 2023.

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Bibliography

2023
Interior Point Methods with a Gradient Oracle.
Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Discrepancy Minimization via Regularization.
Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

Quantized Distributed Training of Large Models with Convergence Guarantees.
Proceedings of the International Conference on Machine Learning, 2023

CrAM: A Compression-Aware Minimizer.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
CrAM: A Compression-Aware Minimizer.
CoRR, 2022

2021
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Decomposable Submodular Function Minimization via Maximum Flow.
Proceedings of the 38th International Conference on Machine Learning, 2021

Faster Sparse Minimum Cost Flow by Electrical Flow Localization.
Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

Projection-Free Bandit Optimization with Privacy Guarantees.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Adaptive Gradient Methods for Constrained Convex Optimization.
CoRR, 2020

Circulation Control for Faster Minimum Cost Flow in Unit-Capacity Graphs.
Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

2019
Improved Convergence for $\ell_\infty$ and $\ell_1$ Regression via Iteratively Reweighted Least Squares.
CoRR, 2019

Submodular maximization with matroid and packing constraints in parallel.
Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 2019

Improved Convergence for $\ell_1$ and $\ell_∞$ Regression via Iteratively Reweighted Least Squares.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Multidimensional Binary Search for Contextual Decision-Making.
Oper. Res., 2018

A Parallel Double Greedy Algorithm for Submodular Maximization.
CoRR, 2018

Submodular Maximization with Packing Constraints in Parallel.
CoRR, 2018

Towards Deep Learning Models Resistant to Adversarial Attacks.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs.
Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017

Negative-Weight Shortest Paths and Unit Capacity Minimum Cost Flow in Õ (<i>m</i><sup>10/7</sup> log <i>W</i>) Time (Extended Abstract).
Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017

Tight Bounds for Approximate Carathéodory and Beyond.
Proceedings of the 34th International Conference on Machine Learning, 2017

Matrix Scaling and Balancing via Box Constrained Newton's Method and Interior Point Methods.
Proceedings of the 58th IEEE Annual Symposium on Foundations of Computer Science, 2017

2016
Negative-Weight Shortest Paths and Unit Capacity Minimum Cost Flow in Õ(m<sup>10/7</sup> log W) Time.
CoRR, 2016

Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More.
Proceedings of the IEEE 57th Annual Symposium on Foundations of Computer Science, 2016

2015
Improved Parallel Algorithms for Spanners and Hopsets.
Proceedings of the 27th ACM on Symposium on Parallelism in Algorithms and Architectures, 2015

How To Elect a Leader Faster than a Tournament.
Proceedings of the 2015 ACM Symposium on Principles of Distributed Computing, 2015

2010
Online Ranking for Tournament Graphs.
Proceedings of the Approximation and Online Algorithms - 8th International Workshop, 2010


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