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Time Series Workshop
Oren Anava · Azadeh Khaleghi · Vitaly Kuznetsov · Alexander Rakhlin

Fri Dec 11 05:30 AM -- 03:30 PM (PST) @ 514 bc
Event URL: https://sites.google.com/site/nipsts2015/home »

Data, in the form of time-dependent sequential observations emerge in many key real-world problems ranging from biological data, to financial markets, to weather forecasting and audio/video processing. However, despite the ubiquity of such data, the vast majority of learning algorithms have been primarily developed for the setting in which sample points are drawn i.i.d. from some possibly unknown fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form of data-generating distribution. Such assumptions may undermine the possibly complex nature of modern data which can possess long-range dependency patterns that we now have the computing power to discern. On the other extreme, some online learning algorithms consider a non-stochastic framework without any distributional assumptions. However, such methods may fail to fully address the stochastic aspect of real-world time-series data.

The goal of this workshop is to bring together theoretical and applied researchers interested in the analysis of time series, and the development of new algorithms to process sequential data. This includes algorithms for time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as a general theory for learning and comparing stochastic processes. We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and many others to contribute to this workshop.

Fri 6:00 a.m. - 6:45 a.m. [iCal]
Learning Theory and Algorithms for Time Series (Talk)
Mehryar Mohri
Fri 6:50 a.m. - 7:15 a.m. [iCal]
Isotonic Hawkes Process (Talk)
Yichen Wang
Fri 11:30 a.m. - 12:10 p.m. [iCal]
Bayesian Time Series: Structured Representations for Scalability (Talk)
Emily Fox
Fri 12:10 p.m. - 12:20 p.m. [iCal]
Sparse Adaptive Prior for Time Dependent Model Parameters (Talk)
Jesse Dodge
Fri 12:20 p.m. - 12:40 p.m. [iCal]
Design of Covariance Functions using Inter-Domain Inducing Variables (Talk)
Felipe Tobar
Fri 12:40 p.m. - 1:00 p.m. [iCal]
Markov GP for Scalable Expressive Online Bayessian Nonparametric Time Series Forecasting (Talk)
Yves-Laurent Kom Samo
Fri 1:30 p.m. - 2:10 p.m. [iCal]
Between stochastic and adversarial: forecasting with online ARMA models (Talk)
Shie Mannor
Fri 2:10 p.m. - 2:25 p.m. [iCal]
Confidence intervals for the mixing time of a reversible Markov chain from a single sample path (Talk)
Csaba Szepesvari
Fri 2:25 p.m. - 3:00 p.m. [iCal]
Wavelet Methods for Time Series (Talk)
Ramo Gencay
Fri 3:05 p.m. - 3:20 p.m. [iCal]
Temporal Regularized Matrix Factorization (Talk)
Hsiang-Fu Yu

Author Information

Oren Anava (Technion)
Azadeh Khaleghi (Mathematics & Statistics, Lancaster University)
Vitaly Kuznetsov (Google Research)

Vitaly Kuznetsov is a research scientist at Google. Prior to joining Google Research, Vitaly received his Ph.D. in mathematics from the Courant Institute of Mathematical Sciences at New York University. Vitaly has contributed to a number of different areas in machine learning, in particular the development of the theory and algorithms for forecasting non-stationary time series. At Google, his work is focused on the design and implementation of large-scale machine learning tools and algorithms for time series modeling, forecasting and anomaly detection. His current research interests include all aspects of applied and theoretical time series analysis, in particular, in non-stationary environments.

Alexander Rakhlin (UPenn)

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