Robust modal regression with direct gradient approximation of modal regression risk

Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:380-389, 2020.

Abstract

Modal regression is aimed at estimating the global mode (i.e., global maximum) of the conditional density function of the output variable given input variables, and has led to regression methods robust against a wide-range of noises. A typical approach for modal regression takes a two-step approach of firstly approximating the modal regression risk (MRR) and of secondly maximizing the approximated MRR with some gradient method. However, this two-step approach can be suboptimal in gradient-based maximization methods because a good MRR approximator does not necessarily give a good gradient approximator of MRR. In this paper, we take a novel approach of \emph{directly} approximating the gradient of MRR in modal regression. Based on the direct approach, we first propose a modal regression method with reproducing kernels where a new update rule to estimate the conditional mode is derived based on a fixed-point method. Then, the derived update rule is theoretically investigated. Furthermore, since our direct approach is compatible with recent sophisticated stochastic gradient methods (e.g., Adam), another modal regression method is also proposed based on neural networks. Finally, the superior performance of the proposed methods is demonstrated on various artificial and benchmark datasets.

Cite this Paper


BibTeX
@InProceedings{pmlr-v124-sasaki20a, title = {Robust modal regression with direct gradient approximation of modal regression risk}, author = {Sasaki, Hiroaki and Sakai, Tomoya and Kanamori, Takafumi}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {380--389}, year = {2020}, editor = {Jonas Peters and David Sontag}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/sasaki20a/sasaki20a.pdf}, url = { http://proceedings.mlr.press/v124/sasaki20a.html }, abstract = {Modal regression is aimed at estimating the global mode (i.e., global maximum) of the conditional density function of the output variable given input variables, and has led to regression methods robust against a wide-range of noises. A typical approach for modal regression takes a two-step approach of firstly approximating the modal regression risk (MRR) and of secondly maximizing the approximated MRR with some gradient method. However, this two-step approach can be suboptimal in gradient-based maximization methods because a good MRR approximator does not necessarily give a good gradient approximator of MRR. In this paper, we take a novel approach of \emph{directly} approximating the gradient of MRR in modal regression. Based on the direct approach, we first propose a modal regression method with reproducing kernels where a new update rule to estimate the conditional mode is derived based on a fixed-point method. Then, the derived update rule is theoretically investigated. Furthermore, since our direct approach is compatible with recent sophisticated stochastic gradient methods (e.g., Adam), another modal regression method is also proposed based on neural networks. Finally, the superior performance of the proposed methods is demonstrated on various artificial and benchmark datasets.} }
Endnote
%0 Conference Paper %T Robust modal regression with direct gradient approximation of modal regression risk %A Hiroaki Sasaki %A Tomoya Sakai %A Takafumi Kanamori %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-sasaki20a %I PMLR %P 380--389 %U http://proceedings.mlr.press/v124/sasaki20a.html %V 124 %X Modal regression is aimed at estimating the global mode (i.e., global maximum) of the conditional density function of the output variable given input variables, and has led to regression methods robust against a wide-range of noises. A typical approach for modal regression takes a two-step approach of firstly approximating the modal regression risk (MRR) and of secondly maximizing the approximated MRR with some gradient method. However, this two-step approach can be suboptimal in gradient-based maximization methods because a good MRR approximator does not necessarily give a good gradient approximator of MRR. In this paper, we take a novel approach of \emph{directly} approximating the gradient of MRR in modal regression. Based on the direct approach, we first propose a modal regression method with reproducing kernels where a new update rule to estimate the conditional mode is derived based on a fixed-point method. Then, the derived update rule is theoretically investigated. Furthermore, since our direct approach is compatible with recent sophisticated stochastic gradient methods (e.g., Adam), another modal regression method is also proposed based on neural networks. Finally, the superior performance of the proposed methods is demonstrated on various artificial and benchmark datasets.
APA
Sasaki, H., Sakai, T. & Kanamori, T.. (2020). Robust modal regression with direct gradient approximation of modal regression risk. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:380-389 Available from http://proceedings.mlr.press/v124/sasaki20a.html .

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