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 approaches aim to instead find the conditional mode, but are restricted to nonparametric approaches. Such approaches can be difficult to scale, and make it difficult to benefit from parametric function approximation, like neural networks, which can learn complex relationships between inputs and targets. In this work, we propose a parametric modal regression algorithm, by using the implicit function theorem to develop an objective for learning a joint parameterized 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) is even more effective for certain unimodal problems, particularly for high frequency data where the joint function over inputs and targets can better capture the complex relationship between them. We conclude by showing that our method provides small improvements on two regression datasets that have asymmetric distributions over the targets.
We introduce a simple and novel modal regression algorithm which is easy to scale to large problems.  The paper proposes an implicit function approach to learning the modes of multimodal regression. The present work proposes a parametric approach to estimate the conditional mode using the Implicit Function Theorem for multi-modal distributions. 