Idan Attias
18 papers · 2019–2026 · 6 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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πΊοΈ Taxonomy Completionist (25) π Academic Marathon Β· Sprint π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π¬
Deep Specialist
(16)
π
Keyword Champion
(3)
β‘
Prolific Year Β· Productive
π₯
Unstoppable Β· Bronze
ποΈ
Keyword Collector
(91)
π
Century Club Β· Bronze
Top co-authors
Research topics
Machine Learning
(17)
Optimization & Theory
(17)
Mathematics & Optimization
(10)
Learning Types Β· Machine Learning
(9)
Optimization
(9)
Core Methods
(6)
Artificial Intelligence
(3)
Core AI
(3)
Reinforcement Learning
(2)
Methods
(2)
Computer Science
(1)
Deep Learning
(1)
Foundations
(1)
Learning Types Β· Deep Learning
(1)
Mathematics
(1)
Keywords
sample complexity
(4)
pac learning
(4)
online learning
(4)
generalization bound
(4)
adversarial robustness
(3)
fat-shattering dimension
(3)
binary classification
(2)
regret bound
(2)
zero-sum game
(2)
minimax regret
(2)
sample compression
(2)
vc dimension
(1)
empirical risk minimization
(1)
majority vote
(1)
minimax optimization
(1)
adversarial learning
(1)
linear regression
(1)
sample compression scheme
(1)
robust classification
(1)
rademacher complexity
(1)
Papers
On the Hardness of Learning Regular Expressions
ALT 2026
Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs
COLT 2025
PAC Learning with Improvements
ICML 2025
Sample Compression Scheme Reductions
ALT 2025
Agnostic Sample Compression Schemes for Regression
ICML 2024
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
ICML 2024
Fat-Shattering Dimension of k-fold Aggregations
JMLR 2024
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing
ICML 2024
Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood
NeurIPS 2024
Universal Rates for Regression: Separations between Cut-Off and Absolute Loss
COLT 2024
Adversarially Robust PAC Learnability of Real-Valued Functions
ICML 2023
Learning Revenue Maximization Using Posted Prices for Stochastic Strategic Patient Buyers
AAAI 2023
Online Learning and Solving Infinite Games with an ERM Oracle
COLT 2023
Optimal Learners for Realizable Regression: PAC Learning and Online Learning
NeurIPS 2023
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability
NeurIPS 2022
Improved Generalization Bounds for Adversarially Robust Learning
JMLR 2022
Prediction with Corrupted Expert Advice
NeurIPS 2020
Improved Generalization Bounds for Robust Learning
ALT 2019