Name
Abstract | ||
|---|---|---|
We study the problem of structured prediction under test-time budget constraints. We propose a novel approach based on selectively acquiring computationally costly features during test-time in order to reduce the computational cost of prediction with minimal performance degradation. We formulate a novel empirical risk minimization (ERM) for policy learning. We show that policy learning can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two structured prediction tasks, optical character recognition and dependency parsing and show significant reduction in the feature costs without degrading accuracy. |
Year | Venue | Field |
|---|---|---|
2017 | THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Data mining,Heuristic,Budget constraint,Computer science,Adaptive system,Structured prediction,Optical character recognition,Dependency grammar,Artificial intelligence,Feature generation,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tolga Bolukbasi | 1 | 115 | 8.02 |
| Kai-Wei Chang | 2 | 4735 | 276.81 |
| Joseph Wang | 3 | 46 | 6.14 |
| Venkatesh Saligrama | 4 | 1350 | 112.74 |