The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2606. 29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by 7.
arXiv:2511.15146v2 Announce Type: replace Abstract: Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. Yet ranking scores is straightforwa...
arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.
The paper introduces the Descriptive‑Complexity Information Criterion (DCIC), a new framework for selecting models when predictors are highly correlated and the model class is uncertain. DCIC uses Kraft‑admissible code lengths to regularize large collections of candidate models, achieving selection consistency under sub‑Weibull noise without requiring RIP‑type conditions and providing non‑asymptotic oracle risk bounds even when the model is misspecified. The approach also unifies heterogeneous model classes on a common complexity scale, enables class–model recovery under identifiability conditions, and offers a complexity‑guided search path that balances computational effort with statistical accuracy, as demonstrated by numerical experiments.
arXiv:2606. 15217v1 Announce Type: cross Abstract: Offline model-based optimization (MBO) proposes candidates by optimizing a surrogate trained on a fixed historical dataset.
arXiv:2402. 07407v3 Announce Type: replace-cross Abstract: We propose conformal predictive programming (CPP), a framework to solve chance constrained optimization problems, i.