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Sat Jul 24 08:45 AM -- 06:05 PM (PDT)
Time Series Workshop
Yian Ma · Ehi Nosakhare · Yuyang Wang · Scott Yang · Rose Yu

Workshop Home Page

Time series is one of the fastest growing and richest types of data. In a variety of domains including dynamical systems, healthcare, climate science and economics, there have been increasing amounts of complex dynamic data due to a shift away from parsimonious, infrequent measurements to nearly continuous real-time monitoring and recording. This burgeoning amount of new data calls for novel theoretical and algorithmic tools and insights.

The goals of our workshop are to: (1) highlight the fundamental challenges that underpin learning from time series data (e.g. covariate shift, causal inference, uncertainty quantification), (2) discuss recent developments in theory and algorithms for tackling these problems, and (3) explore new frontiers in time series analysis and their connections with emerging fields such as causal discovery and machine learning for science. In light of the recent COVID-19 outbreak, we also plan to have a special emphasis on non-stationary dynamics, causal inference, and their applications to public health at our workshop.

Time series modeling has a long tradition of inviting novel approaches from many disciplines including statistics, dynamical systems, and the physical sciences. This has led to broad impact and a diverse range of applications, making it an ideal topic for the rapid dissemination of new ideas that take place at ICML. We hope that the diversity and expertise of our speakers and attendees will help uncover new approaches and break new ground for these challenging and important settings. Our previous workshops have received great popularity at ICML, and we envision our workshop will continue to appeal to the ICML audience and stimulate many interdisciplinary discussions.

Morning Poster: [ protected link dropped ]
Afternoon Poster: [ protected link dropped ]

Openning Remarks (Introduction)
Mihaela Van der Schaar: Time-series in healthcare: challenges and solutions (Invited Talk)
Mike West: Multiscale Bayesian Modelling: Ideas and Examples from Consumer Sales (Invited Talk)
Morning Coffee Break (Break)
Contributed Talk: JKOnet: Proximal Optimal Transport Modeling of Population Dynamics (Contributed Talk)
Dominik Janzing: Quantifying causal influence in time series and beyond (Invited Talk)
Morning Poster Session: PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series (Poster)
Morning Poster Session: Changepoint Detection using Self Supervised Variational AutoEncoders (Poster)
Morning Poster Session: Monte Carlo EM for Deep Time Series Anomaly Detection (Poster)
Morning Poster Session: A Study of Joint Graph Inference and Forecasting (Poster)
Morning Poster Session: Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators. (Poster)
Morning Poster Session: Inferring the Structure of Ordinary Differential Equations (Poster)
Morning Poster Session: Understanding Local Linearisation in Variational Gaussian Process State Space Models (Poster)
Morning Poster Session: Forward Prediction for Physical Reasoning (Poster)
Morning Poster Session: Modeling the El NiƱo Southern Oscillation with Neural Differential Equations (Poster)
Morning Poster Session: Continuous Latent Process Flows (Poster)
Morning Poster Session: Towards Robust, Scalable and Interpretable Time Series Forecasting using Bayesian Vector Auto-Regression (Poster)
Morning Poster Session: VIKING: Variational Bayesian Variance Tracking Winning a Post-Covid Day-Ahead Electricity Load Forecasting Competition (Poster)
Morning Poster Session: Revisiting Dynamic Regret of Strongly Adaptive Methods (Poster)
Morning Poster Session: First Hitting Time Guarantees for Nonlinear Time Series Models (Poster)
Morning Poster Session: Electric Load Forecasting with Boosting based Sample Transfer (Poster)
Morning Poster Session: Prediction-Constrained Hidden Markov Models for Semi-Supervised Classification (Poster)
Morning Poster Session: Recurrent Intensity Modeling for User Recommendation and Online Matching (Poster)
Morning Poster Session: JKOnet: Proximal Optimal Transport Modeling of Population Dynamics (Poster)
Morning Poster Session: ST-DETR: Spatio-Temporal Object Traces Attention Detection Transformer (Poster)
Morning Poster Session: Integrating LSTMs and GNNs for COVID-19 Forecasting (Poster)
Morning Poster Session: An efficient Gaussian process framework for analysis of oscillations in nonstationary time series (Poster)
Morning Poster Session: Deep Signature Statistics for Likelihood-free Time-series Models (Poster)
Morning Poster Session: Temporal Dependencies in Feature Importance for Time Series Predictions (Poster)
Morning Poster Session: Evolving-Graph Gaussian Processes (Poster)
Morining Poster Session: Online Learning with Optimism and Delay (Poster)
Morning Poster Session: Probabilistic Time Series Forecasting with Implicit Quantile Networks (Poster)
Morning Poster Session: Flexible Temporal Point Processes Modeling with Nonlinear Hawkes Processes with Gaussian Processes Excitations and Inhibitions (Poster)
Contributed Talk: PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series (Contributed Talk)
David Duvenaud (Invited Talk)
David Duvenaud: Live Q&A (Live Q&A)
Afternoon Coffee Break (Break)
Contributed Talk: Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data (Contributed Talk)
Lester Mackey: Online Learning with Optimism and Delay (Invited Talk)
Contributed Talk: Ecological Inference using Constrained Kalman filters for the COVID-19 Pandemic (Contributed Talk)
Afternoon Poster Session: DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting (Poster)
Afternoon Poster Session: Time2Cluster: Clustering Time Series Using Neighbor Information (Poster)
Afternoon Poster Session: DAMA-Net: A Novel Predictive Model for Irregularly Asynchronously andSparsely Sampled Multivariate Time Series (Poster)
Afternoon Poster Session: High-Order Representation Learning for Multivariate Time Series Forecasting (Poster)
Afternoon Poster Session: Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes (Poster)
Afternoon Poster Session: Robust Price Optimization in Retail (Poster)
Afternoon Poster Session: Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data (Poster)
Afternoon Poster Session: Ecological Inference using Constrained Kalman filters for the COVID-19 Pandemic (Poster)
Awards and Closing Remarks (Closing)