2 research outputs found
Learning to Speed Up Structured Output Prediction
Predicting structured outputs can be computationally onerous due to the
combinatorially large output spaces. In this paper, we focus on reducing the
prediction time of a trained black-box structured classifier without losing
accuracy. To do so, we train a speedup classifier that learns to mimic a
black-box classifier under the learning-to-search approach. As the structured
classifier predicts more examples, the speedup classifier will operate as a
learned heuristic to guide search to favorable regions of the output space. We
present a mistake bound for the speedup classifier and identify inference
situations where it can independently make correct judgments without input
features. We evaluate our method on the task of entity and relation extraction
and show that the speedup classifier outperforms even greedy search in terms of
speed without loss of accuracy.Comment: International Conference on Machine Learning, Stockholm, Sweden, 201
Strategic Prediction with Latent Aggregative Games
We introduce a new class of context dependent, incomplete information games
to serve as structured prediction models for settings with significant
strategic interactions. Our games map the input context to outcomes by first
condensing the input into private player types that specify the utilities,
weighted interactions, as well as the initial strategies for the players. The
game is played over multiple rounds where players respond to weighted
aggregates of their neighbors' strategies. The predicted output from the model
is a mixed strategy profile (a near-Nash equilibrium) and each observation is
thought of as a sample from this strategy profile. We introduce two new
aggregator paradigms with provably convergent game dynamics, and characterize
the conditions under which our games are identifiable from data. Our games can
be parameterized in a transferable manner so that the sets of players can
change from one game to another. We demonstrate empirically that our games as
models can recover meaningful strategic interactions from real voting data