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Revealing and modeling the hidden statespace of dynamical systems is a fundamental problem in signal processing, control theory, and learning. Classical approaches to this problem include hidden Markov models, reinforcement learning, and various system identification algorithms. More recently, the problem has been approached by such modern machine learning techniques as kernel methods, Bayesian and Gaussian processes, latent variables, and the information bottleneck. Moreover, dynamic statespace learning is the key mechanism in the way organisms cope with complex stochastic environments, i.e., biological adaptation. One familiar example of a complex dynamic system is the authorship system in the NIPS community. Such a system can be described by both internal variables, such as links between NIPS authors, and external environment variables, such as other research communities. This complex system, which generates a vast number of papers each year, can be modeled and investigated using various parametric and nonparametric methods. In this workshop we intend to review and confront different approaches to dynamical system learning, with various applications in machine learning and neuroscience. In addition, we hope this workshop will familiarize the machine learning community with many realworld examples and applications of dynamical system learning. Such examples will also serve as the basis for the discussion of such systems in the workshop.
Author Information
Naftali Tishby (The Hebrew University Jerusalem)
Naftali Tishby, is a professor of computer science and the director of the Interdisciplinary Center for Neural Computation (ICNC) at the Hebrew university of Jerusalem. He received his Ph.D. in theoretical physics from the Hebrew University and was a research staff member at MIT and Bell Labs from 1985 to 1991. He was also a visiting professor at Princeton NECI, the University of Pennsylvania and the University of California at Santa Barbara. Dr. Tishby is a leader of machine learning research and computational neuroscience. He was among the first to introduce methods from statistical physics into learning theory, and dynamical systems techniques in speech processing. His current research is at the interface between computer science, statistical physics and computational neuroscience and concerns the foundations of biological information processing and the connections between dynamics and information.
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2017 : How do the Deep Learning layers converge to the Information Bottleneck limit by Stochastic Gradient Descent? »
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2016 : Principles and Algorithms for SelfMotivated Behaviour »
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2014 Workshop: Novel Trends and Applications in Reinforcement Learning »
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2013 Workshop: Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games. »
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2012 Workshop: Information in Perception and Action »
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2011 Tutorial: Information Theory in Learning and Control »
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2010 Poster: Tight Sample Complexity of LargeMargin Learning »
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2008 Workshop: Principled Theoretical Frameworks for the PerceptionAction Cycle »
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2008 Mini Symposium: Principled Theoretical Frameworks for the PerceptionAction Cycle »
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2008 Poster: On the Reliability of Clustering Stability in the Large Sample Regime »
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2008 Spotlight: On the Reliability of Clustering Stability in the Large Sample Regime »
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2007 Oral: Cluster Stability for Finite Samples »
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2007 Poster: Cluster Stability for Finite Samples »
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2006 Poster: Information Bottleneck for Non CoOccurrence Data »
Yevgeny Seldin · Noam Slonim · Naftali Tishby