Papers › Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

3 Jun 2021NeurIPS 2021 12arXiv:2106.01798archive 2025-07-28

Mathias Niepert, Pasquale Minervini, Luca Franceschi

Combining discrete probability distributions and combinatorial optimization problems with neural network components has numerous applications but poses several challenges. We propose Implicit Maximum Likelihood Estimation (I-MLE), a framework for end-to-end learning of models combining discrete exponential family distributions and differentiable neural components. I-MLE is widely applicable as it only requires the ability to compute the most probable states and does not rely on smooth relaxations. The framework encompasses several approaches such as perturbation-based implicit differentiation and recent methods to differentiate through black-box combinatorial solvers. We introduce a novel class of noise distributions for approximating marginals via perturb-and-MAP. Moreover, we show that I-MLE simplifies to maximum likelihood estimation when used in some recently studied learning settings that involve combinatorial solvers. Experiments on several datasets suggest that I-MLE is competitive with and often outperforms existing approaches which rely on problem-specific relaxations.

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Syntology Ran 7 of 10 code samples harvested from 3 repositories linked to this paper; 3 have no recorded run. Of those that ran: 3 ran · our draft was wrong; 2 ran · fixture could not drive it; 2 ran with no contract checked.

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nec-research/tf-imle officialmentioned in papermentioned on GitHubpytorch report
edinburghnlp/torch-adaptive-imle mentioned on GitHubpytorchMIT report
uclnlp/torch-imle mentioned on GitHubpytorch report

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10 samples harvested; 7 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

3ran · our draft was wrong
2ran · fixture could not drive it
2ran
3unverified

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_IMLE_PID nec-research/tf-imle/SYNTH/imle.py official repository ran licence not identified · pointer only · 518265639d392d1a · report
_maybe_ctx_call nec-research/tf-imle/SYNTH/imle.py official repository ran · our draft was wrong licence not identified · pointer only · a6163b5a095df556 · report
imle_pid nec-research/tf-imle/SYNTH/imle.py official repository ran · our draft was wrong licence not identified · pointer only · b781568015623ad1 · report
TargetDistribution uclnlp/torch-imle/imle/wrapper.py community (archive-listed) ran MIT (permissive) · 63886bbe7b1f023d · report
imle uclnlp/torch-imle/imle/wrapper.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 728c8123b133d083 · report
BaseNoiseDistribution uclnlp/torch-imle/imle/wrapper.py community (archive-listed) unverified MIT (permissive) · 8c92b1b7ad6eec54 · report
BaseTargetDistribution uclnlp/torch-imle/imle/wrapper.py community (archive-listed) unverified MIT (permissive) · cb1a5474e20e34cf · report
draw_gaussian_diag_samples SerChirag/rs-imle/helpers/imle_helpers.py community ran · fixture could not drive it no licence file found · pointer only · aac686caa4fc5236 · report
gaussian_analytical_kl SerChirag/rs-imle/helpers/imle_helpers.py community ran · fixture could not drive it no licence file found · pointer only · f8752a89ed52495c · report
get_conv SerChirag/rs-imle/helpers/imle_helpers.py community unverified no licence file found · pointer only · 52eba97aab7c3b34 · report

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