Timezone: »
For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean. Modal regression algorithms address this issue by instead finding the conditional mode(s). Most, however, are nonparametric approaches and so can be difficult to scale. Further, parametric approximators, like neural networks, facilitate learning complex relationships between inputs and targets. In this work, we propose a parametric modal regression algorithm. We use the implicit function theorem to develop an objective, for learning a joint function over inputs and targets. We empirically demonstrate on several synthetic problems that our method (i) can learn multi-valued functions and produce the conditional modes, (ii) scales well to high-dimensional inputs, and (iii) can even be more effective for certain uni-modal problems, particularly for high-frequency functions. We demonstrate that our method is competitive in a real-world modal regression problem and two regular regression datasets.
Author Information
Yangchen Pan (University of Alberta)
Ehsan Imani (University of Alberta)
Amir-massoud Farahmand (Vector Institute and University of Toronto)
Martha White (University of Alberta)
More from the Same Authors
-
2020 Poster: Towards Safe Policy Improvement for Non-Stationary MDPs »
Yash Chandak · Scott Jordan · Georgios Theocharous · Martha White · Philip Thomas -
2020 Spotlight: Towards Safe Policy Improvement for Non-Stationary MDPs »
Yash Chandak · Scott Jordan · Georgios Theocharous · Martha White · Philip Thomas -
2020 Session: Orals & Spotlights Track 14: Reinforcement Learning »
Deepak Pathak · Martha White -
2019 Workshop: The Optimization Foundations of Reinforcement Learning »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 Poster: Learning Macroscopic Brain Connectomes via Group-Sparse Factorization »
Farzane Aminmansour · Andrew Patterson · Lei Le · Yisu Peng · Daniel Mitchell · Franco Pestilli · Cesar F Caiafa · Russell Greiner · Martha White -
2019 Poster: Importance Resampling for Off-policy Prediction »
Matthew Schlegel · Wesley Chung · Daniel Graves · Jian Qian · Martha White -
2019 Poster: Meta-Learning Representations for Continual Learning »
Khurram Javed · Martha White -
2019 Poster: Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm »
Amir-massoud Farahmand -
2018 Poster: Supervised autoencoders: Improving generalization performance with unsupervised regularizers »
Lei Le · Andrew Patterson · Martha White -
2018 Poster: Context-dependent upper-confidence bounds for directed exploration »
Raksha Kumaraswamy · Matthew Schlegel · Adam White · Martha White -
2018 Poster: Iterative Value-Aware Model Learning »
Amir-massoud Farahmand -
2018 Poster: An Off-policy Policy Gradient Theorem Using Emphatic Weightings »
Ehsan Imani · Eric Graves · Martha White -
2016 Poster: Estimating the class prior and posterior from noisy positives and unlabeled data »
Shantanu Jain · Martha White · Predrag Radivojac -
2012 Poster: Convex Multi-view Subspace Learning »
Martha White · Yao-Liang Yu · Xinhua Zhang · Dale Schuurmans -
2010 Poster: Relaxed Clipping: A Global Training Method for Robust Regression and Classification »
Yao-Liang Yu · Min Yang · Linli Xu · Martha White · Dale Schuurmans -
2010 Poster: Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains »
Martha White · Adam M White