Steve Hanneke · PaperMetrix

Learning with Changing Features

2017 · arXiv (Cornell University)

In this paper we study the setting where features are added or change interpretation over time, which has applications in multiple domains such as retail, manufacturing, finance. In particular, we propose an approach to provably …

VC Classes are Adversarially Robustly Learnable, but Only Improperly

2019 · arXiv (Cornell University)

We study the question of learning an adversarially robust predictor. We show that any hypothesis class $\mathcal{H}$ with finite VC dimension is robustly PAC learnable with an improper learning rule. The requirement of being improper …

Robustly-reliable learners under poisoning attacks

2022 · arXiv (Cornell University)

Data poisoning attacks, in which an adversary corrupts a training set with the goal of inducing specific desired mistakes, have raised substantial concern: even just the possibility of such an attack can make a user …

Learning Whenever Learning is Possible: Universal Learning under General\n Stochastic Processes

2017 · arXiv (Cornell University)

This work initiates a general study of learning and generalization without\nthe i.i.d. assumption, starting from first principles. While the traditional\napproach to statistical learning theory typically relies on standard\nassumptions from probability theory (e.g., i.i.d. or stationary …

Efficient Agnostic Learning with Average Smoothness

2023 · arXiv (Cornell University)

We study distribution-free nonparametric regression following a notion of average smoothness initiated by Ashlagi et al. (2021), which measures the "effective" smoothness of a function with respect to an arbitrary unknown underlying distribution. While the …

Dual VC Dimension Obstructs Sample Compression by Embeddings

2024 · arXiv (Cornell University)

This work studies embedding of arbitrary VC classes in well-behaved VC classes, focusing particularly on extremal classes. Our main result expresses an impossibility: such embeddings necessarily require a significant increase in dimension. In particular, we …

Learning from Snapshots of Discrete and Continuous Data Streams