ProbChecklist learns predictive checklists end to end from images, time series, and text by treating learned concept probabilities as probabilistic facts in a checklist logic program.
Revisiting Score Function Estimators for $k$-Subset Sampling
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abstract
Are score function estimators an underestimated approach to learning with $k$-subset sampling? Sampling $k$-subsets is a fundamental operation in many machine learning tasks that is not amenable to differentiable parametrization, impeding gradient-based optimization. Prior work has focused on relaxed sampling or pathwise gradient estimators. Inspired by the success of score function estimators in variational inference and reinforcement learning, we revisit them within the context of $k$-subset sampling. Specifically, we demonstrate how to efficiently compute the $k$-subset distribution's score function using a discrete Fourier transform, and reduce the estimator's variance with control variates. The resulting estimator provides both exact samples and unbiased gradient estimates while also applying to non-differentiable downstream models, unlike existing methods. Experiments in feature selection show results competitive with current methods, despite weaker assumptions.
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cs.LG 1years
2024 1verdicts
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Learning Predictive Checklists with Probabilistic Logic Programming
ProbChecklist learns predictive checklists end to end from images, time series, and text by treating learned concept probabilities as probabilistic facts in a checklist logic program.