natural-language-processing-4

Selection bias

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Libraries

i

Use these libraries to find selection-bias-4 models and implementations

Datasets

PanNuke

ICLR Database

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Most implemented papers

PanNuke Dataset Extension, Insights and Baselines

N. Rajpoot, Jevgenij Gamper, Navid Alemi Koohbanani, S. Graham, M. Jahanifar, S. Khurram, A. Azam, K. Hewitt•Mon Mar 23 2020

This work study the performance of segmentation and classification models when applied to the proposed dataset and demonstrate the application of models trained on PanNuke to whole-slide images.

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Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai•Fri Apr 20 2018

This paper model CVR in a brand-new perspective by making good use of sequential pattern of user actions, i.e., impression -> click -> conversion, which is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.

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Active Structure Learning of Causal DAGs via Directed Clique Tree

K. Greenewald, Karthikeyan Shanmugam, C. Squires, Sara Magliacane, Dmitriy A. Katz, Murat Kocaoglu•Sat Oct 31 2020

This work develops a universal lower bound for single-node interventions that establishes that the largest clique is always a fundamental impediment to structure learning and presents a two-phase intervention design algorithm that matches the optimal number of interventions up to a multiplicative logarithmic factor in the number of maximal cliques.

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A Debiased MDI Feature Importance Measure for Random Forests

Xiao Li, Yu Wang, Sumanta Basu, Karl Kumbier, Bin Yu•Tue Jun 25 2019

A new analytical expression for MDI is derived, and based on this new expression, a debiased MDI feature importance measure is proposed, called MDI-oob, which achieves state-of-the-art performance in feature selection from Random Forests for both deep and shallow trees.

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Adversarial balancing-based representation learning for causal effect inference with observational data

Mykola Pechenizkiy, Xin Du, Lei Sun, W. Duivesteijn, Alexander G. Nikolaev•Mon Apr 29 2019

A neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on recent advances in representation learning, is proposed that is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches.

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Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

Shiyu Chang, Bing Bai, Mo Yu, T. Zhao, Jian Liang, Guanhua Zhang, Kun Bai, Conghui Zhu•Tue May 14 2019

This paper investigates the problem of selection bias on six NLSM datasets and finds that four out of them are significantly biased, and proposes a training and evaluation framework to alleviate the bias.

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To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions

R. Jagerman, Harrie Oosterhuis, M. de Rijke•Sun Jul 14 2019

The results show that the choice between the methodologies is consequential and depends on the presence of selection bias, and the degree of position bias and interaction noise, and that counterfactual methods can obtain the highest ranking performance; however, in other circumstances their optimization can be detrimental to the user experience.

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Automated Dependence Plots

Liu Leqi, David I. Inouye, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar•Sun Dec 01 2019

The usefulness of the automated dependence plots (ADP) across multiple use-cases and datasets including model selection, bias detection, understanding out-of-sample behavior, and exploring the latent space of a generative model is demonstrated.

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Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules

Yusuke Narita, Kohei Yata•Sun Apr 25 2021

A treatment-effect estimator is developed using algorithmic decisions as instruments for a class of stochastic and deterministic algorithms that evaluate the Coronavirus Aid, Relief, and Economic Security Act, which allocated many billions of dollars worth of relief funding to hospitals via an algorithmic rule.

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Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun Resolution

Emily McMilin•Thu Sep 29 2022

A simple causal mechanism is introduced to describe the role underspecification plays in the generation of spurious correlations and directly informs the development of two lightweight black-box evaluation methods.

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