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Found 12 papers, 4 papers with code
Efficiently Computing Similarities to Private Datasets
We abstract out this common subroutine and study the following fundamental algorithmic problem: Given a similarity function $f$ and a large high-dimensional private dataset $X \subset \mathbb{R}^d$, output a differentially private (DP) data structure which approximates $\sum_{x \in X} f(x, y)$ for any query $y$.
Fairness in Submodular Maximization over a Matroid Constraint
Submodular maximization over a matroid constraint is a fundamental problem with various applications in machine learning.
Fairness in Streaming Submodular Maximization over a Matroid Constraint
Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset.
Near-Optimal Correlation Clustering with Privacy
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more.
Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers
Deep neural networks (DNNs) have grown exponentially in size over the past decade, leaving only those who have massive datacenter-based resources with the ability to develop and train such models.
Correlation Clustering in Constant Many Parallel Rounds
Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining.
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data.
Efficient Algorithms for Device Placement of DNN Graph Operators
However, for such settings (large models and multiple heterogeneous devices), we require automated algorithms and toolchains that can partition the ML workload across devices.
Beyond $1/2$-Approximation for Submodular Maximization on Massive Data Streams
It is the first low-memory, single-pass algorithm that improves the factor $0. 5$, under the natural assumption that elements arrive in a random order.
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
It is the first low-memory, singlepass algorithm that improves the factor 0. 5, under the natural assumption that elements arrive in a random order.
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach
We study the classical problem of maximizing a monotone submodular function subject to a cardinality constraint k, with two additional twists: (i) elements arrive in a streaming fashion, and (ii) m items from the algorithm's memory are removed after the stream is finished.
Active Learning and Proofreading for Delineation of Curvilinear Structures
In a proofreading context, we similarly find regions of the resulting reconstruction that should be verified in priority to obtain a nearly-perfect result.
