31 research outputs found
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Automatic machine learning is an important problem in the forefront of
machine learning. The strongest AutoML systems are based on neural networks,
evolutionary algorithms, and Bayesian optimization. Recently AlphaD3M reached
state-of-the-art results with an order of magnitude speedup using reinforcement
learning with self-play. In this work we extend AlphaD3M by using a pipeline
grammar and a pre-trained model which generalizes from many different datasets
and similar tasks. Our results demonstrate improved performance compared with
our earlier work and existing methods on AutoML benchmark datasets for
classification and regression tasks. In the spirit of reproducible research we
make our data, models, and code publicly available.Comment: ICML Workshop on Automated Machine Learnin
Bayesian Optimal Active Search and Surveying
We consider two active binary-classification problems with atypical
objectives. In the first, active search, our goal is to actively uncover as
many members of a given class as possible. In the second, active surveying, our
goal is to actively query points to ultimately predict the proportion of a
given class. Numerous real-world problems can be framed in these terms, and in
either case typical model-based concerns such as generalization error are only
of secondary importance.
We approach these problems via Bayesian decision theory; after choosing
natural utility functions, we derive the optimal policies. We provide three
contributions. In addition to introducing the active surveying problem, we
extend previous work on active search in two ways. First, we prove a novel
theoretical result, that less-myopic approximations to the optimal policy can
outperform more-myopic approximations by any arbitrary degree. We then derive
bounds that for certain models allow us to reduce (in practice dramatically)
the exponential search space required by a naive implementation of the optimal
policy, enabling further lookahead while still ensuring that optimal decisions
are always made.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
AlphaD3M: Machine Learning Pipeline Synthesis
peer reviewedWe introduce AlphaD3M, an automatic machine learning (AutoML) system based on
meta reinforcement learning using sequence models with self play. AlphaD3M is
based on edit operations performed over machine learning pipeline primitives
providing explainability. We compare AlphaD3M with state-of-the-art AutoML
systems: Autosklearn, Autostacker, and TPOT, on OpenML datasets. AlphaD3M
achieves competitive performance while being an order of magnitude faster,
reducing computation time from hours to minutes, and is explainable by design
The Design and Performance of a CORBA Audio/Video Streaming Service
Factory patterns [Gamma et al., 1995], as described in Section 2.3.1. Flexibility in data transfer protocol: A CORBA A/V Streaming Service implementation may need to select from a variety of transfer protocols. For instance, an Internet-based streaming application, such as Realvideo [RealNetworks, 1998], may use the UDP protocol, whereas a local intranet video-conferencing tool [et al., 1996] might prefer the QoS features offered by native high-speed ATM protocols. Likewise, RTP [Schulzrinne et al., 1994] is gaining acceptance as a transfer protocol for streaming audio and video data over the Internet. Thus, it is essential that a A/V Streaming Service support a range of data transfer protocols dynamically. The CORBA A/V Streaming Service defines a simple specialized protocol Simple Flow Protocol (SFP), which makes no assumptions about the communication protocols used for data streaming and provides an architecture independent flow content transfer. Consequently, the stream establis..