Mazda Moayeri
12 papers · 2021–2025 · 7 venues · across top CS/AI venues
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Achievements
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🐣 Hot Topic Early Bird 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🗺️ Taxonomy Completionist (28) 🤝 Dynamic Duo (11)
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Century Club · Bronze
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Unstoppable · Bronze
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Research topics
Deep Learning
(10)
Machine Learning
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Learning Types · Machine Learning
(7)
Artificial Intelligence
(6)
Core AI
(6)
Computer Vision
(4)
Analysis
(4)
Application Areas
(4)
Learning Types · Deep Learning
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Architectures
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Core Methods
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Learning Paradigms
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Models
(2)
Techniques
(2)
Optimization & Theory
(1)
Keywords
image classification
(5)
neural network
(2)
spurious correlation
(2)
adversarial robustness
(2)
adversarial training
(2)
deep neural network
(2)
model robustness
(1)
feature embedding
(1)
contrastive learning
(1)
sample complexity
(1)
model evaluation
(1)
benchmark evaluation
(1)
label noise
(1)
feature space
(1)
backdoor attack
(1)
data augmentation
(1)
distributional robustness
(1)
generative model
(1)
false positive rate
(1)
self-supervised learning
(1)
Papers
DyePack: Provably Flagging Test Set Contamination in LLMs Using Backdoors
EMNLP 2025
Rethinking Artistic Copyright Infringements In the Era Of Text-to-Image Generative Models
ICLR 2025
Unearthing Skill-level Insights for Understanding Trade-offs of Foundation Models
ICLR 2025
Data-Centric Debugging: Mitigating Model Failures via Targeted Image Retrieval
WACV 2024
PRIME: Prioritizing Interpretability in Failure Mode Extraction
ICLR 2024
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation
NeurIPS 2023
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases
NeurIPS 2023
Text-To-Concept (and Back) via Cross-Model Alignment
ICML 2023
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes
CVPR 2022
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
NeurIPS 2022
Hard ImageNet: Segmentations for Objects with Strong Spurious Cues
NeurIPS 2022
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings
ICCV 2021