28 citations · 325 across the 73 of their papers we have counts for
8 papers · 1 filter
Sketch and Scale: Geo-distributed tSNE and UMAP
Viska Wei, Nikita Ivkin, Vladimir Braverman +1
Running machine learning analytics over geographically distributed datasets is a rapidly arising problem in the world of data management policies ensuring privacy and data security…
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
In this paper we consider multi-objective reinforcement learning where the objectives are balanced using preferences. In practice, the preferences are often given in an adversarial…
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
Jingfeng Wu, Difan Zou, Vladimir Braverman +1
Understanding the algorithmic bias of \emph{stochastic gradient descent} (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existin…
Near-Optimal Entrywise Sampling of Numerically Sparse Matrices
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan +1
Many real-world data sets are sparse or almost sparse. One method to measure this for a matrix is the \emph{numerical sparsity}, denoted $\mathsf{ns}(…
Data-Independent Structured Pruning of Neural Networks via Coresets
Ben Mussay, Daniel Feldman, Samson Zhou +2
Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the co…
Obtaining Adjustable Regularization for Free via Iterate Averaging
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
Regularization for optimization is a crucial technique to avoid overfitting in machine learning. In order to obtain the best performance, we usually train a model by tuning the reg…