Selection bias
60 papers with code • 0 benchmarks • 1 datasets
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PanNuke
Most implemented papers
PanNuke Dataset Extension, Insights and Baselines
The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides.
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.
A Debiased MDI Feature Importance Measure for Random Forests
Based on the original definition of MDI by Breiman et al. for a single tree, we derive a tight non-asymptotic bound on the expected bias of MDI importance of noisy features, showing that deep trees have higher (expected) feature selection bias than shallow ones.
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets
arthua196/Leakage-Neutral-Learning-for-QuoraQP
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ACL 2019
Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process.
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from historical data and model user behavior to deal with biases; and online methods that perform interventions to deal with bias but use no explicit user models.
Automated Dependence Plots
davidinouye/adp
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To address these drawbacks, we formalize a method for automating the selection of interesting PDPs and extend PDPs beyond showing single features to show the model response along arbitrary directions, for example in raw feature space or a latent space arising from some generative model.
Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules
We use this observation to develop a treatment-effect estimator for a class of stochastic and deterministic decision-making algorithms.
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
vanderschaarlab/mlforhealthlabpub
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NeurIPS 2017
Stemming from the potential outcomes model, we propose a novel multi- task learning framework in which factual and counterfactual outcomes are mod- eled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS).
Deep Counterfactual Networks with Propensity-Dropout
Shantanu48114860/Deep-Counterfactual-Networks-with-Propensity-Dropout
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The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers.
Target contrastive pessimistic risk for robust domain adaptation
In domain adaptation, classifiers with information from a source domain adapt to generalize to a target domain.
