AISTATS*2014 Talks and Papers
Proceedings-track PapersThe AISTATS 2014 proceedings-track papers are available in the online proceedings.
H01 Gaussian Processes for Data-Efficient Learning in Robotics and
Marc Deisenroth, Dieter Fox, Carl Rasmussen
Autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. We follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in challenging real robot and control tasks.
Citation: MP Deisenroth, D Fox, and CE Rasmussen. Gaussian Processes for Data-Efficient Learning in Robotics and Control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014. Accepted for publication. http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.218
H02 Bayesian Monitoring for the Comprehensive Nuclear-Test-Ban Treaty
Stuart Russell, Erik Sudderth, Nimar Arora
Verification for the Comprehensive Nuclear-Test-Ban Treaty requires detecting and characterizing all seismic events above a minimum magnitude occurring anywhere on Earth. The treaty defines a network of sensors, the International Monitoring System (IMS), managed by the United Nations CTBTO. NET-VISA, a Bayesian monitoring system applied to IMS data, exhibits a 2x-3x reduction in detection failures compared to the current CTBTO system; the UN has recommended its deployment for treaty verification, subject to approval by member states. NET-VISA's prior is a complex, open-universe generative probability model (written originally in the Bayesian Logic formal language and trained on historical data) describing event occurrence, signal propagation, signal detection, and noise processes; the evidence consists of "blips" (above-threshold signals, 90% of which are noise) extracted from raw IMS waveform data. More recent work extends the generative model all the way to the raw waveforms, promising greater sensitivity but requiring new modeling and inference techniques.
Citation: Nimar S. Arora, Stuart Russell, and Erik Sudderth, ``NET-VISA: Network Processing Vertically Integrated Seismic Analysis.'' In Bulletin of the Seismological Society of America, 103(2A), 709-729, 2013. http://www.bssaonline.org/content/103/2A/709.abstract.
H03 Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.
Citation: Yoshua Bengio, Aaron Courville, Pascal Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, Aug. 2013, doi:10.1109/TPAMI.2013.50 http://www.computer.org/csdl/trans/tp/2013/08/ttp2013081798-abs.html
H04 Spatiotemporal point process models of conflicts
Andrew Zammit-Mangion, Michael Dewar, Visakan Kadirkamanathan, Guido Sanguinetti
Modern conflicts are characterised by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict, however, remains a challenging task due to the heterogeneous and dynamic nature of the data typically available. Here we propose the use of dynamic spatiotemporal modelling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation and volatility. Using ideas from statistics, signal processing and ecology, we provide a predictive framework able to assimilate data and give confidence estimates on the predictions. We demonstrate our methods on the Wikileaks Afghan War Diary. Our results show that the approach allows deeper insights into conflict dynamics and allows a strikingly accurate (in a statistical sense) forward prediction of armed opposition group activity in 2010, based solely on data from previous years.
Citation: Andrew Zammit-Mangion, Michael Dewar, Visakan Kadirkamanathan, and Guido Sanguinetti, Point process modelling of the Afghan War Diary, Proc Natl Acad Sci U S A. 2012 July 31; 109(31): 12414-12419 Web: http://www.pnas.org/content/early/2012/07/11/1203177109.abstract