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Joint Model and Data Sparsification via the Marginal Likelihood

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arXiv:2605. 29908v1 Announce Type: new Abstract: Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression.

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Authors: Alexander Timans, Thomas M\"ollenhoff, Christian A. Naesseth, Mohammad Emtiyaz Khan, Eric Nalisnick
Released: 2026-05-29 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Joint Model and Data Sparsification via the Marginal Likelihood overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: under, line, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.29908

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