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Found 14 papers, 0 papers with code
Theoretical Foundations of Adversarially Robust Learning
In this thesis, we explore what robustness properties can we hope to guarantee against adversarial examples and develop an understanding of how to algorithmically guarantee them.
Strategic Classification under Unknown Personalized Manipulation
Ball manipulations are a widely studied class of manipulations in the literature, where agents can modify their feature vector within a bounded radius ball.
Agnostic Multi-Robust Learning Using ERM
A fundamental problem in robust learning is asymmetry: a learner needs to correctly classify every one of exponentially-many perturbations that an adversary might make to a test-time natural example.
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
We present a minimax optimal learner for the problem of learning predictors robust to adversarial examples at test-time.
A Theory of PAC Learnability under Transformation Invariances
One interesting observation is that distinguishing between the original data and the transformed data is necessary to achieve optimal accuracy in setting (ii) and (iii), which implies that any algorithm not differentiating between the original and transformed data (including data augmentation) is not optimal.
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
We present an oracle-efficient algorithm for boosting the adversarial robustness of barely robust learners.
Transductive Robust Learning Guarantees
We study the problem of adversarially robust learning in the transductive setting.
Adversarially Robust Learning with Unknown Perturbation Sets
We study the problem of learning predictors that are robust to adversarial examples with respect to an unknown perturbation set, relying instead on interaction with an adversarial attacker or access to attack oracles, examining different models for such interactions.
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
We study the problem of reducing adversarially robust learning to standard PAC learning, i. e. the complexity of learning adversarially robust predictors using access to only a black-box non-robust learner.
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
We present a transductive learning algorithm that takes as input training examples from a distribution $P$ and arbitrary (unlabeled) test examples, possibly chosen by an adversary.
Efficiently Learning Adversarially Robust Halfspaces with Noise
We study the problem of learning adversarially robust halfspaces in the distribution-independent setting.
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity
We present and study approximate notions of dimensional and margin complexity, which correspond to the minimal dimension or norm of an embedding required to approximate, rather then exactly represent, a given hypothesis class.
VC Classes are Adversarially Robustly Learnable, but Only Improperly
We study the question of learning an adversarially robust predictor.
Predicting Demographics of High-Resolution Geographies with Geotagged Tweets
For the task of predicting gender and race/ethnicity counts at the blockgroup-level, an approach adapted from prior work to our problem achieves an average correlation of 0. 389 (gender) and 0. 569 (race) on a held-out test dataset.
