Omar Montasser
15 papers · 2019–2025 · 4 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (6) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (4) 🐝 Cross-Pollinator (8)
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Hot Topic Early Bird
🧭
Keyword Pioneer
🏆
Keyword Champion
(2)
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Deep Specialist
(11)
🔥
Unstoppable
(7)
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Century Club
(15)
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Keyword Collector
(58)
Conferences
NIPS (8)
COLT (4)
AISTATS (2)
ICML (1)
Top co-authors
Keywords
vc dimension
(6)
adversarial robustness
(5)
pac learning
(4)
sample complexity
(4)
perturbation set
(3)
robust learning
(3)
empirical risk minimization
(2)
adversarial example
(2)
adversarial robust learning
(2)
transformation invariance
(2)
transductive learning
(2)
game theory
(1)
out-of-distribution generalization
(1)
domain adaptation
(1)
robust classification
(1)
domain generalization
(1)
halfspace learning
(1)
distribution shift
(1)
query complexity
(1)
data augmentation
(1)
Papers
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks
COLT 2025
Agnostic Multi-Robust Learning using ERM
AISTATS 2024
Derandomizing Multi-Distribution Learning
NIPS 2024
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
NIPS 2024
Strategic Classification under Unknown Personalized Manipulation
NIPS 2023
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
NIPS 2022
Transductive Robust Learning Guarantees
AISTATS 2022
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
NIPS 2022
A Theory of PAC Learnability under Transformation Invariances
NIPS 2022
Adversarially Robust Learning with Unknown Perturbation Sets
COLT 2021
Efficiently Learning Adversarially Robust Halfspaces with Noise
ICML 2020
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
NIPS 2020
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity
COLT 2020
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
NIPS 2020
VC Classes are Adversarially Robustly Learnable, but Only Improperly
COLT 2019