Omar Montasser
16 papers · 2019–2026 · 4 venues · across top CS/AI venues
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Achievements
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π£ Hot Topic Early Bird π Conference Polyglot Β· Bronze π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Bronze πΊοΈ Taxonomy Completionist (29)
π
Keyword Champion
(3)
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Deep Specialist
(13)
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Keyword Collector
(69)
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Century Club Β· Bronze
π₯
Unstoppable Β· Bronze
Venues
Top co-authors
Research topics
Machine Learning
(16)
Optimization & Theory
(14)
Learning Types Β· Machine Learning
(8)
Core Methods
(6)
Artificial Intelligence
(5)
Application Areas
(4)
Core AI
(4)
Deep Learning
(2)
Mathematics & Optimization
(1)
Learning Paradigms
(1)
Learning Types Β· Deep Learning
(1)
Models
(1)
Optimization
(1)
Keywords
vc dimension
(7)
adversarial robustness
(6)
sample complexity
(4)
pac learning
(4)
robust learning
(4)
perturbation set
(3)
transformation invariance
(2)
adversarial example
(2)
empirical risk minimization
(2)
transductive learning
(2)
hypothesis class
(2)
adversarial robust learning
(2)
domain adaptation
(1)
robust classification
(1)
data augmentation
(1)
binary classification
(1)
density estimation
(1)
domain generalization
(1)
label noise
(1)
game theory
(1)
Papers
DDPM Score Matching and Distribution Learning (Extended Abstract)
COLT 2026
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
NeurIPS 2024
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
NeurIPS 2024
Strategic Classification under Unknown Personalized Manipulation
NeurIPS 2023
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
NeurIPS 2022
A Theory of PAC Learnability under Transformation Invariances
NeurIPS 2022
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
NeurIPS 2022
Transductive Robust Learning Guarantees
AISTATS 2022
Adversarially Robust Learning with Unknown Perturbation Sets
COLT 2021
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity
COLT 2020
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
NeurIPS 2020
Efficiently Learning Adversarially Robust Halfspaces with Noise
ICML 2020
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
NeurIPS 2020
VC Classes are Adversarially Robustly Learnable, but Only Improperly
COLT 2019