We study the problem of learning to predict a spatiotemporal output sequence given an input sequence. In contrast to conventional sequence prediction problems such as part-of-speech tagging (where output sequences are selected using a relatively small set of discrete labels), our goal is to predict sequences that lie within a high-dimensional continuous output space. We present a decision tree framework for learning an accurate non-parametric spatiotemporal sequence predictor. Our approach enjoys several attractive properties, including ease of training, fast performance at test time, and the ability to robustly tolerate corrupted training data using a novel latent variable approach. We evaluate on several datasets, and demonstrate substantial improvements over existing decision tree based sequence learning frameworks such as SEARN and DAgger.
|Title of host publication||KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery (ACM)|
|Publication status||Published - 10 Aug 2015|
|Event||21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Sydney, Australia|
Duration: 10 Aug 2015 → 13 Aug 2015
|Conference||21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Period||10/08/15 → 13/08/15|