Selection bias
102 papers with code • 0 benchmarks • 2 datasets
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PanNuke
ICLR Database
Most implemented papers
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
In this paper, we find that this problem usually occurs when the positions of support samples are in the vicinity of task centroid -- the mean of all class centroids in the task.
Mathematical Capabilities of ChatGPT
We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology.
Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR
waterdisappear/data-bias-in-mstar
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Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR.
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.
Robust importance-weighted cross-validation under sample selection bias
Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk.
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Causal inference using observational data is challenging, especially in the bivariate case.
Effects of sampling skewness of the importance-weighted risk estimator on model selection
For sample selection bias settings, and for small sample sizes, the importance-weighted risk estimator produces overestimates for datasets in the body of the sampling distribution, i. e. the majority of cases, and large underestimates for data sets in the tail of the sampling distribution.
A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias.
