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M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling
Machine Learning · Submitted 24 Jan 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We also conduct extensive experiments to demonstrate the effectiveness and efficiency of our M$^3$TN.
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The abstract, sentence by sentence
Uplift modeling is a technique used to predict the effect of a treatment (e.g., discounts) on an individual's response. Although several methods have been proposed for multi-valued treatment, they are extended from binary treatment methods. There are still some limitations. Firstly, existing methods calculate uplift based on predicted responses, which may not guarantee a consistent uplift distribution between treatment and control groups. Moreover, this may cause cumulative errors for multi-valued treatment. Secondly, the model parameters become numerous with many prediction heads, leading to reduced efficiency. To address these issues, we propose a novel \underline{M}ulti-gate \underline{M}ixture-of-Experts based \underline{M}ulti-valued \underline{T}reatment \underline{N}etwork (M$^3$TN). M$^3$TN consists of two components: 1) a feature representation module with Multi-gate Mixture-of-Experts to improve the efficiency; 2) a reparameterization module by modeling uplift explicitly to improve the effectiveness. We also conduct extensive experiments to demonstrate the effectiveness and efficiency of our M$^3$TN.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Uplift modeling is a technique used to predict the effect of a treatment (e.g., discounts) on an individual's response. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Although several methods have been proposed for multi-valued treatment, they are extended from binary treatment methods. | background 0.80 | 0.04 | 0.02 | asserted 1.00 |
| s2 | There are still some limitations. | gap 0.78 | 0.05 | 0.02 | asserted 0.93 |
| s3 | Firstly, existing methods calculate uplift based on predicted responses, which may not guarantee a consistent uplift distribution between treatment and control groups. | gap 0.60 | 0.05 | 0.02 | hedged possibility 0.99 |
| s4 | Moreover, this may cause cumulative errors for multi-valued treatment. | background 0.43 | 0.06 | 0.02 | hedged possibility 0.98 |
| s5 | Secondly, the model parameters become numerous with many prediction heads, leading to reduced efficiency. | gap 0.52 | 0.05 | 0.02 | asserted 1.00 |
| s6 | To address these issues, we propose a novel \underline{M}ulti-gate \underline{M}ixture-of-Experts based \underline{M}ulti-valued \underline{T}reatment \underline{N}etwork (M$^3$TN). | contribution 0.98 | 0.86 | 0.18 | asserted 0.99 |
| s7 | M$^3$TN consists of two components: 1) a feature representation module with Multi-gate Mixture-of-Experts to improve the efficiency; 2) a reparameterization module by modeling uplift explicitly to improve the effectiveness. | method 0.92 | 0.82 | 0.17 | asserted 1.00 |
| s8 | We also conduct extensive experiments to demonstrate the effectiveness and efficiency of our M$^3$TN. | contribution 0.57 | 0.96 | 0.40 | asserted 0.99 |
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The paper
Original abstract
Uplift modeling is a technique used to predict the effect of a treatment (e.g., discounts) on an individual's response. Although several methods have been proposed for multi-valued treatment, they are extended from binary treatment methods. There are still some limitations. Firstly, existing methods calculate uplift based on predicted responses, which may not guarantee a consistent uplift distribution between treatment and control groups. Moreover, this may cause cumulative errors for multi-valued treatment. Secondly, the model parameters become numerous with many prediction heads, leading to reduced efficiency. To address these issues, we propose a novel \underline{M}ulti-gate \underline{M}ixture-of-Experts based \underline{M}ulti-valued \underline{T}reatment \underline{N}etwork (M$^3$TN). M$^3$TN consists of two components: 1) a feature representation module with Multi-gate Mixture-of-Experts to improve the efficiency; 2) a reparameterization module by modeling uplift explicitly to improve the effectiveness. We also conduct extensive experiments to demonstrate the effectiveness and efficiency of our M$^3$TN.
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Cites as background 2 papers
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While MoE was first developed as an ensemble method of multiple individual models, Eigen et al. [ref] and Shazeer et al. [ref] turn it into basic building blocks (MoE layer) and stack them in a neural network.
Learning Factored Representations in a Deep Mixture of Experts · Feature Representation Module · 0.96
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While MoE was first developed as an ensemble method of multiple individual models, Eigen et al. [ref] and Shazeer et al. [ref] turn it into basic building blocks (MoE layer) and stack them in a neural network.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer · Feature Representation Module · 0.96
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