arXiv Machine Learning By Mihir More, Aritra Das, Debayan Gupta

A Rate Separation for Agnostic Direct Sums

Read the original on arXiv Machine Learning →

arXiv:2608. 06951v1 Announce Type: new Abstract: Hanneke, Moran, and Waknine \cite{HannekeMoranWaknine2024} asked how the agnostic PAC learning curve of the direct sum $C^r$ depends on the single-instance learning curve $\epsagn(n\mid C)$ and on $r$.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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.

By Shai Ben-David, Farnam Mansouri, Anay Mehrotra, Manolis Zampetakis
arXiv Machine Learning
Aug 12

Optimistic Rates for Multiclass PAC Learning

arXiv:2608. 10869v1 Announce Type: new Abstract: Worst-case multiclass bounds do not become smaller when the best classifier is already nearly correct: what is missing is an optimistic rate, a guarantee whose fluctuation scales with the oracle risk itself.

By Xiaoyu Li, Andi Han, Jiaojiao Jiang, Junbin Gao
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.

By Zhiyuan Li
arXiv Machine Learning
Jul 28

Learning Distributions from Multiple Data Providers

arXiv:2607. 24732v1 Announce Type: cross Abstract: Motivated by learning from heterogeneous and overlapping data providers, we study a stylized model of distribution learning from restricted conditional samples.

By Jon Kleinberg, Amin Saberi, Xizhi Tan, Grigoris Velegkas