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Found 17 papers, 3 papers with code
Fully Dynamic Submodular Maximization over Matroids
Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning.
Deletion Robust Non-Monotone Submodular Maximization over Matroids
Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constraint.
Deletion Robust Submodular Maximization over Matroids
Maximizing a monotone submodular function is a fundamental task in machine learning.
Online MAP Inference of Determinantal Point Processes
In this paper, we provide an efficient approximation algorithm for finding the most likelihood configuration (MAP) of size $k$ for Determinantal Point Processes (DPP) in the online setting where the data points arrive in an arbitrary order and the algorithm cannot discard the selected elements from its local memory.
Sliding Window Algorithms for k-Clustering Problems
The sliding window model of computation captures scenarios in which data is arriving continuously, but only the latest $w$ elements should be used for analysis.
Data Structures and Algorithms
Edge-Weighted Online Bipartite Matching
Online bipartite matching and its variants are among the most fundamental problems in the online algorithms literature.
Data Structures and Algorithms Computer Science and Game Theory
Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
We show how one can achieve the tight $(1/2)$-approximation guarantee with $O(k)$ shared memory while minimizing not only the required rounds of computations but also the total number of communicated bits.
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Can we efficiently extract useful information from a large user-generated dataset while protecting the privacy of the users and/or ensuring fairness in representation?
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Inspired by many applications of bipartite matching in online advertising and machine learning, we study a simple and natural iterative proportional allocation algorithm: Maintain a priority score $\priority_a$ for each node $a\in \mathds{A}$ on one side of the bipartition, initialized as $\priority_a=1$.
Data Summarization at Scale: A Two-Stage Submodular Approach
The sheer scale of modern datasets has resulted in a dire need for summarization techniques that identify representative elements in a dataset.
Deletion-Robust Submodular Maximization at Scale
Can we efficiently extract useful information from a large user-generated dataset while protecting the privacy of the users and/or ensuring fairness in representation.
Probabilistic Submodular Maximization in Sub-Linear Time
As a remedy, we introduce the problem of sublinear time probabilistic submodular maximization: Given training examples of functions (e. g., via user feature vectors), we seek to reduce the ground set so that optimizing new functions drawn from the same distribution will provide almost as much value when restricted to the reduced ground set as when using the full set.
Fast Distributed Submodular Cover: Public-Private Data Summarization
The goal is to provide a succinct summary of massive dataset, ideally as small as possible, from which customized summaries can be built for each user, i. e. it can contain elements from the public data (for diversity) and users' private data (for personalization).
Consistent Hashing with Bounded Loads
Designing algorithms for balanced allocation of clients to servers in dynamic settings is a challenging problem for a variety of reasons.
Data Structures and Algorithms
Horizontally Scalable Submodular Maximization
A variety of large-scale machine learning problems can be cast as instances of constrained submodular maximization.
Sparse Solutions to Nonnegative Linear Systems and Applications
For learning a mixture of $k$ axis-aligned Gaussians in $d$ dimensions, we give an algorithm that outputs a mixture of $O(k/\epsilon^3)$ Gaussians that is $\epsilon$-close in statistical distance to the true distribution, without any separation assumptions.
Trading off Mistakes and Don't-Know Predictions
If the number of don't know predictions is forced to be zero, the model reduces to the well-known mistake-bound model introduced by Littlestone [Lit88].
