Timezone: »
Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments). Conventional methods of uplift modeling require every instance to be jointly equipped with two types of labels: the taken action and its outcome. However, obtaining two labels for each instance at the same time is difficult or expensive in many real-world problems. In this paper, we propose a novel method of uplift modeling that is applicable to a more practical setting where only one type of labels is available for each instance. We show a mean squared error bound for the proposed estimator and demonstrate its effectiveness through experiments.
Author Information
Ikko Yamane (The University of Tokyo/RIKEN)
Florian Yger (Université Paris-Dauphine)
Jamal Atif (Université Paris-Dauphine)
Masashi Sugiyama (RIKEN / University of Tokyo)
More from the Same Authors
-
2020 Poster: Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning »
Yu Yao · Tongliang Liu · Bo Han · Mingming Gong · Jiankang Deng · Gang Niu · Masashi Sugiyama -
2020 Poster: Part-dependent Label Noise: Towards Instance-dependent Label Noise »
Xiaobo Xia · Tongliang Liu · Bo Han · Nannan Wang · Mingming Gong · Haifeng Liu · Gang Niu · Dacheng Tao · Masashi Sugiyama -
2020 Spotlight: Part-dependent Label Noise: Towards Instance-dependent Label Noise »
Xiaobo Xia · Tongliang Liu · Bo Han · Nannan Wang · Mingming Gong · Haifeng Liu · Gang Niu · Dacheng Tao · Masashi Sugiyama -
2020 Poster: Rethinking Importance Weighting for Deep Learning under Distribution Shift »
Tongtong Fang · Nan Lu · Gang Niu · Masashi Sugiyama -
2020 Poster: Learning from Aggregate Observations »
Yivan Zhang · Nontawat Charoenphakdee · Zhenguo Wu · Masashi Sugiyama -
2020 Poster: Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring »
Taira Tsuchiya · Junya Honda · Masashi Sugiyama -
2020 Spotlight: Rethinking Importance Weighting for Deep Learning under Distribution Shift »
Tongtong Fang · Nan Lu · Gang Niu · Masashi Sugiyama -
2020 Poster: Provably Consistent Partial-Label Learning »
Lei Feng · Jiaqi Lv · Bo Han · Miao Xu · Gang Niu · Xin Geng · Bo An · Masashi Sugiyama -
2020 Poster: Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators »
Takeshi Teshima · Isao Ishikawa · Koichi Tojo · Kenta Oono · Masahiro Ikeda · Masashi Sugiyama -
2020 Oral: Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators »
Takeshi Teshima · Isao Ishikawa · Koichi Tojo · Kenta Oono · Masahiro Ikeda · Masashi Sugiyama -
2019 Poster: Theoretical evidence for adversarial robustness through randomization »
Rafael Pinot · Laurent Meunier · Alexandre Araujo · Hisashi Kashima · Florian Yger · Cedric Gouy-Pailler · Jamal Atif -
2019 Poster: Uncoupled Regression from Pairwise Comparison Data »
Liyuan Xu · Junya Honda · Gang Niu · Masashi Sugiyama -
2019 Poster: Are Anchor Points Really Indispensable in Label-Noise Learning? »
Xiaobo Xia · Tongliang Liu · Nannan Wang · Bo Han · Chen Gong · Gang Niu · Masashi Sugiyama -
2019 Poster: On the Calibration of Multiclass Classification with Rejection »
Chenri Ni · Nontawat Charoenphakdee · Junya Honda · Masashi Sugiyama -
2018 Poster: Binary Classification from Positive-Confidence Data »
Takashi Ishida · Gang Niu · Masashi Sugiyama -
2018 Spotlight: Binary Classification from Positive-Confidence Data »
Takashi Ishida · Gang Niu · Masashi Sugiyama -
2018 Poster: Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces »
Motoya Ohnishi · Masahiro Yukawa · Mikael Johansson · Masashi Sugiyama -
2018 Poster: Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks »
Yusuke Tsuzuku · Issei Sato · Masashi Sugiyama -
2018 Poster: Masking: A New Perspective of Noisy Supervision »
Bo Han · Jiangchao Yao · Gang Niu · Mingyuan Zhou · Ivor Tsang · Ya Zhang · Masashi Sugiyama -
2018 Poster: Co-teaching: Robust training of deep neural networks with extremely noisy labels »
Bo Han · Quanming Yao · Xingrui Yu · Gang Niu · Miao Xu · Weihua Hu · Ivor Tsang · Masashi Sugiyama -
2017 Poster: Positive-Unlabeled Learning with Non-Negative Risk Estimator »
Ryuichi Kiryo · Gang Niu · Marthinus C du Plessis · Masashi Sugiyama -
2017 Poster: Learning from Complementary Labels »
Takashi Ishida · Gang Niu · Weihua Hu · Masashi Sugiyama -
2017 Oral: Positive-Unlabeled Learning with Non-Negative Risk Estimator »
Ryuichi Kiryo · Gang Niu · Marthinus C du Plessis · Masashi Sugiyama -
2017 Poster: Expectation Propagation for t-Exponential Family Using q-Algebra »
Futoshi Futami · Issei Sato · Masashi Sugiyama -
2017 Poster: Generative Local Metric Learning for Kernel Regression »
Yung-Kyun Noh · Masashi Sugiyama · Kee-Eung Kim · Frank Park · Daniel Lee -
2016 Poster: Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning »
Gang Niu · Marthinus Christoffel du Plessis · Tomoya Sakai · Yao Ma · Masashi Sugiyama -
2014 Poster: Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP »
Shinichi Nakajima · Issei Sato · Masashi Sugiyama · Kazuho Watanabe · Hiroko Kobayashi -
2014 Poster: Multitask learning meets tensor factorization: task imputation via convex optimization »
Kishan Wimalawarne · Masashi Sugiyama · Ryota Tomioka -
2014 Poster: Analysis of Learning from Positive and Unlabeled Data »
Marthinus C du Plessis · Gang Niu · Masashi Sugiyama -
2013 Poster: Parametric Task Learning »
Ichiro Takeuchi · Tatsuya Hongo · Masashi Sugiyama · Shinichi Nakajima -
2013 Poster: Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering »
Shinichi Nakajima · Akiko Takeda · S. Derin Babacan · Masashi Sugiyama · Ichiro Takeuchi -
2012 Poster: Perfect Dimensionality Recovery by Variational Bayesian PCA »
Shinichi Nakajima · Ryota Tomioka · Masashi Sugiyama · S. Derin Babacan -
2012 Poster: Density-Difference Estimation »
Masashi Sugiyama · Takafumi Kanamori · Taiji Suzuki · Marthinus C du Plessis · Song Liu · Ichiro Takeuchi -
2011 Poster: Relative Density-Ratio Estimation for Robust Distribution Comparison »
Makoto Yamada · Taiji Suzuki · Takafumi Kanamori · Hirotaka Hachiya · Masashi Sugiyama -
2011 Poster: Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification »
Ichiro Takeuchi · Masashi Sugiyama -
2011 Poster: Analysis and Improvement of Policy Gradient Estimation »
Tingting Zhao · Hirotaka Hachiya · Gang Niu · Masashi Sugiyama -
2011 Poster: Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent »
Shinichi Nakajima · Masashi Sugiyama · S. Derin Babacan -
2010 Spotlight: Global Analytic Solution for Variational Bayesian Matrix Factorization »
Shinichi Nakajima · Masashi Sugiyama · Ryota Tomioka -
2010 Poster: Global Analytic Solution for Variational Bayesian Matrix Factorization »
Shinichi Nakajima · Masashi Sugiyama · Ryota Tomioka -
2008 Poster: Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection »
Takafumi Kanamori · Shohei Hido · Masashi Sugiyama -
2007 Poster: Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation »
Masashi Sugiyama · Shinichi Nakajima · Hisashi Kashima · Paul von Buenau · Motoaki Kawanabe -
2007 Poster: Multi-Task Learning via Conic Programming »
Tsuyoshi Kato · Hisashi Kashima · Masashi Sugiyama · Kiyoshi Asai -
2006 Workshop: Learning when test and training inputs have different distributions »
Joaquin Quiñonero Candela · Masashi Sugiyama · Anton Schwaighofer · Neil D Lawrence -
2006 Poster: Mixture Regression for Covariate Shift »
Amos Storkey · Masashi Sugiyama