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#learning-theory

5 curated events
papersTODAY 04:00 UTC

Study Tightens Sample Complexity Bounds for Valiant's CNF Learning Algorithm

Researchers revisit Valiant's 1984 algorithm for learning CNF formulas with bounded clause size and variable degree from uniformly random satisfying assignments. Working in the local lemma regime, they derive near-tight sample complexity guarantees under a stated condition relating clause size to variable degree. The work is a theoretical contribution to computational learning theory rather than a practical tool release.

research/learning-theory/sample-complexity/cnf
papersTODAY 04:00 UTC

Minimax-Optimal Regret Bounds for Linear Contextual Bandits with Adaptive Action Sets

A new arXiv paper studies stochastic linear contextual bandits where the set of available actions can vary arbitrarily, depending on both the unknown parameter and past interactions. The authors prove matching upper and lower bounds on regret that agree up to logarithmic factors, characterizing the problem's minimax rate.

bandits/reinforcement-learning/learning-theory/arxiv
papersSEP 10 04:00 UTC

Language Generation in the Limit: Finite Witnesses and a Separation-Width Hierarchy

A new paper gives a complete characterization of when an algorithm can output valid unseen elements of an unknown infinite language from any exhaustive stream of positive examples. The authors show generation is possible exactly when families admit finite witnesses, and they structure the problem's difficulty into a separation-width hierarchy over countable domains. The result extends classical inductive inference and language learnability theory to arbitrary families.

theory/language-generation/learning-theory
papersSEP 10 04:00 UTC

Researchers study sequence prediction when the oracle can lie

A new machine learning theory paper on arXiv examines how a learner can predict elements of a sequence when the oracle supplying feedback may misreport outcomes. The authors frame the task as a repeated interaction in which the environment picks an outcome from a finite alphabet and the learner must commit to a probability distribution without reliable ground truth. The work analyzes what prediction performance can still be guaranteed in this adversarial setting.

learning-theory/prediction/adversarial/research
papersSEP 10 04:00 UTC

Paper shows exponential deterministic–randomized gap in ERM-oracle complexity for thresholds

A new arXiv preprint addresses a question raised by Attias, Hanneke, and Ramaswami (NeurIPS 2025) about whether randomized learners can provably get by with fewer oracle calls than deterministic ones when a hypothesis class is accessible only through an oracle. Focusing on the instance they singled out, transductive online learning of thresholds over an unknown ordering, the authors establish an exponential separation between deterministic and randomized ERM-oracle complexity, showing randomization can reduce the required number of calls exponentially.

learning-theory/online-learning/oracle-complexity/randomization