Learning Graphical Models
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
Sanjiban Choudhury, Shervin Javdani, Siddhartha Srinivasa, Sebastian Scherer
In this paper, we do so by drawing a novel equivalence between motion planning and the Bayesian active learning paradigm of decision region determination (DRD) . Unfortunately, a straight application of existing methods requires computation exponential in the number of edges in a graph.
Collapsed variational Bayes for Markov jump processes
Boqian Zhang, Jiangwei Pan, Vinayak A. Rao
Neural Information Processing Systems http://nips.cc/
Tractability in Structured Probability Spaces
Arthur Choi, Yujia Shen, Adnan Darwiche
In particular, we introduce the notion of a hierarchical route distribution and show how they can be leveraged to construct tractable PSDDs over route distributions, allowing them to scale to larger maps.
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Neural Information Processing Systems http://nips.cc/
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems
Le Fang, Fan Yang, Wen Dong, Tong Guan, Chunming Qiao
Technological breakthroughs allow us to collect data with increasing spatiotemporal resolution from complex interaction systems.
Inferring Generative Model Structure with Static Analysis
Paroma Varma, Bryan D. He, Payal Bajaj, Nishith Khandwala, Imon Banerjee, Daniel Rubin, Christopher Ré
We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically.
Adversarial Surrogate Losses for Ordinal Regression
Rizal Fathony, Mohammad Ali Bashiri, Brian Ziebart
Neural Information Processing Systems http://nips.cc/
Hierarchical Methods of Moments
Matteo Ruffini, Guillaume Rabusseau, Borja Balle
Despite their theoretical appeal, the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification. In this paper we present a hierarchical approach to methods of moments to circumvent such limitations.
Dynamic-Depth Context Tree Weighting
Joao V. Messias, Shimon Whiteson
Reinforcement learning (RL) in partially observable settings is challenging because the agent's observations are not Markov. Recently proposed methods can learn variable-order Markov models of the underlying process but have steep memory requirements and are sensitive to aliasing between observation histories due to sensor noise. This paper proposes dynamic-depth context tree weighting (D2-CTW), a model-learning method that addresses these limitations. D2-CTW dynamically expands a suffix tree while ensuring that the size of the model, but not its depth, remains bounded. We show that D2-CTW approximately matches the performance of state-of-the-art alternatives at stochastic time-series prediction while using at least an order of magnitude less memory. We also apply D2-CTW to model-based RL, showing that, on tasks that require memory of past observations, D2-CTW can learn without prior knowledge of a good state representation, or even the length of history upon which such a representation should depend.