paper-with-me

홈 › Papers

Submodularity for Data Selection in Machine Translation

2014-10-01 · EMNLP 2014 10 · Katrin Kirchhoff, Jeff Bilmes
📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Language ModellingMachine TranslationTranslation

Similar Papers 제목 키워드 기반

Submodularity In Machine Learning and Artificial Intelligence

2022-01-31 · Jeff Bilmes

In this manuscript, we offer a gentle review of submodularity and supermodularity and their properties. We offer a plethora of submodular definitions; a full description of a number of example submodular functions and th…

Abstractive Text SummarizationBIG-bench Machine Learningfeature selection

Scalable Greedy Feature Selection via Weak Submodularity

2017-03-08 · Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis, Sahand Negahban 외

Greedy algorithms are widely used for problems in machine learning such as feature selection and set function optimization. Unfortunately, for large datasets, the running time of even greedy algorithms can be quite high.…

feature selection

Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio

2019-04-24 · Kaito Fujii, Shinsaku Sakaue

We propose a new concept named adaptive submodularity ratio to study the greedy policy for sequential decision making. While the greedy policy is known to perform well for a wide variety of adaptive stochastic optimizati…

Decision Makingfeature selectionSequential Decision MakingStochastic Optimization

Fast Parallel Algorithms for Statistical Subset Selection Problems

2019-03-06 · NeurIPS 2019 12 · Sharon Qian, Yaron Singer

In this paper, we propose a new framework for designing fast parallel algorithms for fundamental statistical subset selection tasks that include feature selection and experimental design. Such tasks are known to be weakl…

Combinatorial OptimizationExperimental Designfeature selection

Causal meets Submodular: Subset Selection with Directed Information

2016-12-01 · NeurIPS 2016 12 · Yuxun Zhou, Costas J. Spanos

We study causal subset selection with Directed Information as the measure of prediction causality. Two typical tasks, causal sensor placement and covariate selection, are correspondingly formulated into cardinality const…