[robotics-worldwide] [meetings] Call for contributions: NIPS’18 Workshop on "Modeling and Decision-Making in the Spatiotemporal Domain", Montréal, Canada, Dec. 07, 2018

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[robotics-worldwide] [meetings] Call for contributions: NIPS’18 Workshop on "Modeling and Decision-Making in the Spatiotemporal Domain", Montréal, Canada, Dec. 07, 2018

Ransalu Senanayake
[meetings] Call for contributions: NIPS’18 Workshop on "Modeling and Decision-Making in the Spatiotemporal Domain", Montréal, Canada, Dec. 07, 2018


         Call for Contributions

NIPS’18 Workshop on Modeling and Decision-Making in the Spatiotemporal Domain

at the Thirty-second Conference on Neural Information Processing Systems

Friday December 07, 2018

at the Palais des Congrès de Montréal, Montréal, Canada

Website: https://urldefense.proofpoint.com/v2/url?u=https-3A__nips.cc_Conferences_2018_Schedule-3FshowEvent-3D10930&d=DwIF-g&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=nfw6PV4z5Rda1WKA_NdD87qOAhOFdChjEJUahqWUQc8&s=ZdiWlBZbI5MyaEe6Hqzu5ome-ERbJ5618MJV6fRoaa8&e=


1. Call for contributions

We welcome short papers (max 6 pages, excluding references) with theory, direct applications, or attempts to improve efficiency in existing spatiotemporal modeling techniques. Submission instructions can be found on https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_site_nips18spatiotemporal_&d=DwIF-g&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=nfw6PV4z5Rda1WKA_NdD87qOAhOFdChjEJUahqWUQc8&s=1qarvxoWSUQljVeYyfufbAczzodQqQD_pGi0NjwifQk&e=<https://urldefense.proofpoint.com/v2/url?u=https-3A__openreview.net_group-3Fid-3DNIPS.cc_2018_Workshop_Spatiotemporal&d=DwIF-g&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=nfw6PV4z5Rda1WKA_NdD87qOAhOFdChjEJUahqWUQc8&s=8V2PN1YscgFhTp30uUScFKxSuf84FLoUjwUxlnWnYfg&e=>. Accepted papers will be will be available online on OpenReview<https://urldefense.proofpoint.com/v2/url?u=https-3A__openreview.net_&d=DwIF-g&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=nfw6PV4z5Rda1WKA_NdD87qOAhOFdChjEJUahqWUQc8&s=EtpTwSJTOoFd_1yyzl5nk1huis0THVEN_GekkfT1q0k&e=> and presented as contributed talks or posters.

2. Important Dates

  Paper submission deadline:  September 30, 2018 11:59 pm UTC

  Acceptance notification: October 21, 2018

  Camera ready deadline: November 30, 2018 11:59 pm UTC

  Workshop: December 07, 2018 8.00-6:30

3. Workshop Overview

Understanding the evolution of a process over space and time is fundamental to a variety of disciplines. To name a few, such phenomena that exhibit dynamics in both space and time include propagation of diseases, variations in air pollution, dynamics in fluid flows, and patterns in neural activity. In addition to these fields in which modeling the nonlinear evolution of a process is the focus, there is also an emerging interest in decision-making and controlling of autonomous agents in the spatiotemporal domain. That is, in addition to learning what actions to take, when and where to take actions is crucial for an agent to successfully interact with dynamic environments. Although various modeling techniques and conventions are used in different application domains, the fundamental principles remain unchanged. Automatically capturing the dependencies between spatial and temporal components, making accurate predictions into the future, quantifying the uncertainty associated with predictions, real-time performance, and working in both big data and data scarce regimes are some of the key aspects that deserve our attention. Establishing connections between Machine Learning and Statistics, this workshop aims at:

(1) raising open questions on challenges of spatiotemporal modeling and decision-making,

(2) establishing connections among diverse application domains of spatiotemporal modeling, and

(3) encouraging conversation between theoreticians and practitioners to develop robust predictive models.


Theory: stochastic processes, deep learning/convolutional LSTM, kernel methods, chaos theory, time-frequency analysis, reinforcement learning for dynamic environments, dynamic policy learning, biostatistics, epidemiology, geostatistcs, climatology, neuroscience, etc.


    Natural phenomena: disease propagation and outbreaks, environmental monitoring, climate modeling, etc.

   Social sciences and economics: predictive policing, population mapping, poverty mapping, food resources, agriculture, etc.

   Engineering/robotics: active data collection, traffic modeling, motion prediction, fluid dynamics, etc.


Ransalu Senanayake, The University of Sydney (rsen4557<at>uni.sydney.edu.au)

Neal Jean, Stanford University

Fabio Ramos, The University of Sydney

Girish Chowdhary, University of Illinois Urbana-Champaign

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