Weakly Supervised Regression with Interval Targets

Xin Cheng, Yuzhou Cao, Ximing Li, Bo An, Lei Feng
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:5428-5448, 2023.

Abstract

This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regression settings can be adapted to RIT, they are not statistically consistent, and thus their empirical performance is not guaranteed. In this paper, we provide a thorough study on RIT. First, we proposed a novel statistical model to describe the data generation process for RIT and demonstrate its validity. Second, we analyze a simple selecting method for RIT, which selects a particular value in the interval as the target value to train the model. Third, we propose a statistically consistent limiting method for RIT to train the model by limiting the predictions to the interval. We further derive an estimation error bound for our limiting method. Finally, extensive experiments on various datasets demonstrate the effectiveness of our proposed method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-cheng23a, title = {Weakly Supervised Regression with Interval Targets}, author = {Cheng, Xin and Cao, Yuzhou and Li, Ximing and An, Bo and Feng, Lei}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {5428--5448}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/cheng23a/cheng23a.pdf}, url = {https://proceedings.mlr.press/v202/cheng23a.html}, abstract = {This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regression settings can be adapted to RIT, they are not statistically consistent, and thus their empirical performance is not guaranteed. In this paper, we provide a thorough study on RIT. First, we proposed a novel statistical model to describe the data generation process for RIT and demonstrate its validity. Second, we analyze a simple selecting method for RIT, which selects a particular value in the interval as the target value to train the model. Third, we propose a statistically consistent limiting method for RIT to train the model by limiting the predictions to the interval. We further derive an estimation error bound for our limiting method. Finally, extensive experiments on various datasets demonstrate the effectiveness of our proposed method.} }
Endnote
%0 Conference Paper %T Weakly Supervised Regression with Interval Targets %A Xin Cheng %A Yuzhou Cao %A Ximing Li %A Bo An %A Lei Feng %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-cheng23a %I PMLR %P 5428--5448 %U https://proceedings.mlr.press/v202/cheng23a.html %V 202 %X This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regression settings can be adapted to RIT, they are not statistically consistent, and thus their empirical performance is not guaranteed. In this paper, we provide a thorough study on RIT. First, we proposed a novel statistical model to describe the data generation process for RIT and demonstrate its validity. Second, we analyze a simple selecting method for RIT, which selects a particular value in the interval as the target value to train the model. Third, we propose a statistically consistent limiting method for RIT to train the model by limiting the predictions to the interval. We further derive an estimation error bound for our limiting method. Finally, extensive experiments on various datasets demonstrate the effectiveness of our proposed method.
APA
Cheng, X., Cao, Y., Li, X., An, B. & Feng, L.. (2023). Weakly Supervised Regression with Interval Targets. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:5428-5448 Available from https://proceedings.mlr.press/v202/cheng23a.html.

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