arXiv Machine Learning
Aug 11
Optimal Learning Under Tsybakov Noise
arXiv:2608. 08416v1 Announce Type: new Abstract: Probably Approximately Correct (PAC) learning [Val84] is a fundamental learning model that has been extensively investigated.
Realizable Bayes-Consistency for General Metric Losses
arXiv:2605. 03823v3 Announce Type: replace Abstract: We study strong universal Bayes-consistency in the realizable setting for learning with general metric losses, extending classical characterizations beyond $0$-$1$ classification (Bousquet et al.
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arXiv Machine Learning
Jun 16
Online Realizable Regression and Applications for ReLU Networks
arXiv:2602. 19172v2 Announce Type: replace Abstract: Realizable online regression can behave very differently from online classification.
arXiv Machine Learning
Jul 27
Encoding orders and trees in real-valued functions
arXiv:2607. 21761v1 Announce Type: cross Abstract: We prove function-theoretic analogues of a quantitative result of Hodges on extracting the order property from a sufficiently large 2-tree coded in a binary relation.
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.
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
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.
arXiv AI
1d ago
Optimal Lower Bounds for Networked Information Aggregation
arXiv:2608. 15472v1 Announce Type: cross Abstract: The problem of networked information aggregation, studied in Kearns et al.
arXiv AI
6d ago
Post-Training with Policy Gradients: Optimality and the Base Model Barrier
arXiv:2603. 06957v2 Announce Type: replace-cross Abstract: We study post-training linear autoregressive models with outcome and process rewards.
arXiv Machine Learning
Jun 17
On Randomized Algorithms in Online Strategic Classification
arXiv:2602. 06257v2 Announce Type: replace Abstract: Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions.
arXiv Machine Learning
Aug 10
Dirichlet Follow-the-Leader Closes the Gap in Simultaneous Multiclass U-Calibration
arXiv:2608. 06656v1 Announce Type: new Abstract: Can one forecaster attain the optimal regret rate for every bounded proper loss and also adapt to every smooth proper loss?
arXiv Machine Learning
Aug 7
Optimal Rates for Learning with Monotone Adversaries
arXiv:2608. 06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.
arXiv Machine Learning
Jul 1
A Complete Characterization of Learnability for Adversarial Noisy Bandits
arXiv:2605. 09200v2 Announce Type: replace Abstract: We study adversarial noisy bandits given a known function class $\mathcal{F}$.