Discovering Nonlinear Relations with Minimum Predictive Information Regularization

01/07/2020
by   Tailin Wu, et al.
1

Identifying the underlying directional relations from observational time series with nonlinear interactions and complex relational structures is key to a wide range of applications, yet remains a hard problem. In this work, we introduce a novel minimum predictive information regularization method to infer directional relations from time series, allowing deep learning models to discover nonlinear relations. Our method substantially outperforms other methods for learning nonlinear relations in synthetic datasets, and discovers the directional relations in a video game environment and a heart-rate vs. breath-rate dataset.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 19

09/22/2021

Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals

Estimating causal relations is vital in understanding the complex intera...
05/23/2017

Consistent Multitask Learning with Nonlinear Output Relations

Key to multitask learning is exploiting relationships between different ...
08/03/2013

Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application

A new comprehensive approach to nonlinear time series analysis and model...
02/07/2020

Equivalence relations and L^p distances between time series

We introduce a general framework for defining equivalence and measuring ...
06/15/2020

On the training dynamics of deep networks with L_2 regularization

We study the role of L_2 regularization in deep learning, and uncover si...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.