Papers Data Summarization
“Data Summarization” 태그가 달린 논문 97편 · 필터 해제
Group Fairness in Non-monotone Submodular Maximization
Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data ite…
Data SummarizationFairnessMax-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms
Diversity maximization aims to select a diverse and representative subset of items from a large dataset. It is a fundamental optimization task that finds applications in data summarization, feature selection, web search,…
Data SummarizationDiversityFairnessfeature selection+1MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering
Visual language data such as plots, charts, and infographics are ubiquitous in the human world. However, state-of-the-art vision-language models do not perform well on these data. We propose MatCha (Math reasoning and Ch…
Chart Question AnsweringData SummarizationDerenderingImage to text+5Black-box Coreset Variational Inference
Recent advances in coreset methods have shown that a selection of representative datapoints can replace massive volumes of data for Bayesian inference, preserving the relevant statistical information and significantly ac…
Bayesian InferenceData SummarizationGaussian ProcessesVariational InferenceBalancing Utility and Fairness in Submodular Maximization (Technical Report)
Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications -- including data summarization, influence maximization, and recommendation. In many of these problems, the…
Combinatorial OptimizationData SummarizationFairnessStreaming Algorithms for Diversity Maximization with Fairness Constraints
Diversity maximization is a fundamental problem with wide applications in data summarization, web search, and recommender systems. Given a set $X$ of $n$ elements, it asks to select a subset $S$ of $k \ll n$ elements wit…
AttributeData SummarizationDiversityFairness+1Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data
With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient intake, step counts), there is now more opportunity than ever to surface meaningful behavioral insi…
Data SummarizationDecoderText GenerationTime Series+1Group Equality in Adaptive Submodular Maximization
In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both non-adaptive and adaptive settings. It has been shown that the utility function of many machine learni…
Data SummarizationFairnessGuided Exploration of Data Summaries
Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful sum…
Data SummarizationDeep Reinforcement LearningSubmodlib: A Submodular Optimization Library
Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has …
Data SummarizationDiversityOperations for Autonomous Spacecraft
Onboard autonomy technologies such as planning and scheduling, identification of scientific targets, and content-based data summarization, will lead to exciting new space science missions. However, the challenge of opera…
Data SummarizationSchedulingNNK-Means: Data summarization using dictionary learning with non-negative kernel regression
An increasing number of systems are being designed by gathering significant amounts of data and then optimizing the system parameters directly using the obtained data. Often this is done without analyzing the dataset str…
Data SummarizationDictionary LearningregressionTowards General Robustness to Bad Training Data
In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance. Our key insight is that regardless of how bad data are generated, they tend to contribute little to…
Data SummarizationData Summarization via Bilevel Optimization
The increasing availability of massive data sets poses a series of challenges for machine learning. Prominent among these is the need to learn models under hardware or human resource constraints. In such resource-constra…
Active LearningBilevel OptimizationData SummarizationregressionA Unified Framework for Task-Driven Data Quality Management
High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM). Existing DQM schemes often cannot satisfactorily improve ML performance becaus…
Data SummarizationData ValuationManagementAdaptive Sampling for Fast Constrained Maximization of Submodular Function
Several large-scale machine learning tasks, such as data summarization, can be approached by maximizing functions that satisfy submodularity. These optimization problems often involve complex side constraints, imposed by…
Data SummarizationSynthetic Dataset Generation of Driver Telematics
This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included …
Dataset GenerationData SummarizationregressionSequential estimation of Spearman rank correlation using Hermite series estimators
In this article we describe a new Hermite series based sequential estimator for the Spearman rank correlation coefficient and provide algorithms applicable in both the stationary and non-stationary settings. To treat the…
ClusteringData Summarizationfeature selectionSequential Correlation EstimationIntroduction to Core-sets: an Updated Survey
In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions. For exa…
ClusteringData SummarizationSurveyVery Fast Streaming Submodular Function Maximization
Data summarization has become a valuable tool in understanding even terabytes of data. Due to their compelling theoretical properties, submodular functions have been in the focus of summarization algorithms. These algori…
AstronomyData Summarization