arXiv Machine Learning
Jun 29
Surprises in Proper Positive-Only Learning
arXiv:2606. 28309v1 Announce Type: cross Abstract: Binary classification from positive-only samples is a variant of PAC learning in which the learner receives i.
Recursively Enumerably Representable Classes and Computable Versions of the Fundamental Theorem of Statistical Learning
arXiv:2511. 02644v2 Announce Type: replace Abstract: We study computable probably approximately correct (CPAC) learning, where learners are required to be computable functions.
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arXiv Machine Learning
Aug 7
Positive-Data Learning of Fixed-Observation Linear MCFGs from Working Binary Presentations
arXiv:2605. 11644v2 Announce Type: replace-cross Abstract: We study positive-data learning of languages admitting reduced working binary linear nondeleting multiple context-free grammar presentations of bounded fan-out.
arXiv Machine Learning
Aug 4
The No-Clash Teaching Dimension is Bounded by VC Dimension
arXiv:2603. 23561v4 Announce Type: replace-cross Abstract: In the realm of machine learning theory, to prevent unnatural coding schemes between teacher and learner, No-Clash Teaching Dimension was introduced as provably optimal complexity measure for collusion-free teaching.
arXiv Machine Learning
Jul 3
Fast approximation and learning of binary classification tasks in o-minimal structures using ReLU neural networks
arXiv:2607. 01266v1 Announce Type: cross Abstract: We study binary classification problems whose decision sets are given by definable sets in o-minimal expansions of the real field.
Hugging Face Trending Papers
6d ago
Bagging Robustly Learns VC Classes with Linear Sample Complexity
We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnable with sample complexity linear in the VC dimension $d$, providing an exponential improvement over the previous upper bound of Montasser, Hanneke, and Srebro (2019).
arXiv Machine Learning
Jul 20
Testing Distributions Against Bounded Distinguishers
arXiv:2607. 15645v1 Announce Type: cross Abstract: Motivated by the challenge of testing distributions over high-dimensional or continuous domains, we study distribution testing with respect to bounded classes of distinguishers.
arXiv Machine Learning
Jul 23
On the Computational Complexity of Structural Generalization
arXiv:2607. 19573v1 Announce Type: cross Abstract: Structural generalization has been measured repeatedly by several benchmarks, yet it has never been formally defined.
arXiv Machine Learning
Jul 9
The Optimal Sample Complexity of Learning Autoregressive Chain-of-Thought
arXiv:2607. 07423v1 Announce Type: new Abstract: We prove that, in the realizable PAC setting, the sample complexity of exact-trace learning for full autoregressive Chain-of-Thought traces is upper bounded by the standard multiclass rate of the local next-token class, where this rate is governed by the Daniely--Shalev-Shwartz dimension.
arXiv Machine Learning
5d ago
Bagging Robustly Learns VC Classes with Linear Sample Complexity
arXiv:2608. 13514v1 Announce Type: cross Abstract: We revisit the problem of learning predictors robust to adversarial examples at test-time.
arXiv Machine Learning
Jul 8
Boosting with List-Decodable Codes
arXiv:2607. 05791v1 Announce Type: cross Abstract: Boosting is a fundamental technique for generically improving the accuracy of learning algorithms (Schapire 1989).
arXiv Machine Learning
Jul 20
Publicly-Verifiable Certificates for Statistical Algorithms
arXiv:2607. 15528v1 Announce Type: new Abstract: Following Goldwasser, Rothblum, Shafer, and Yehudayoff, who defined a framework for interactive proofs of learning [ITCS'21], we initiate the study of non-interactive proofs of learning.
arXiv Machine Learning
Jun 25
Margin in Abstract Spaces
arXiv:2603. 07221v2 Announce Type: replace Abstract: Margin-based learning, exemplified by linear and kernel methods, is one of the few classical settings where generalization guarantees are independent of the number of parameters.