<ul><li>Researchers have revisited the problem of estimating multiple linear regressors with self-selection bias.</li><li>They have introduced a new algorithm that resolves the main open question from a previous study on self selection.</li><li>The algorithm reduces self-selection to a statistical problem called coarsening, allowing for significant improvements in running time.</li><li>The findings have implications for linear regression and coarse Gaussian mean estimation.</li></ul>

Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond

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