Miguel Rodrigues
14 papers · 2013–2025 · 6 venues · across top CS/AI venues
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Achievements
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๐บ๏ธ Taxonomy Completionist (38) ๐ Academic Marathon ยท Half ๐ Conference Polyglot ยท Silver ๐ Interdisciplinary Bridge ยท Silver ๐ฃ Hot Topic Early Bird
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Keyword Champion
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Keyword Pioneer
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Trend Setter
๐ฅ
Unstoppable ยท Bronze
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The Questioner
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Century Club ยท Bronze
๐๏ธ
Keyword Collector
(81)
Top co-authors
Research topics
Machine Learning
(14)
Optimization & Theory
(9)
Application Areas
(7)
Artificial Intelligence
(6)
Learning Types
(6)
Mathematics & Optimization
(4)
Core AI
(4)
Deep Learning
(3)
Mathematics
(3)
Optimization
(3)
Natural Language Processing
(2)
Bayesian & Probabilistic
(2)
Core Methods
(2)
Learning Paradigms
(2)
Applications
(1)
Keywords
generalization error
(5)
gibbs algorithm
(3)
compressive sensing
(2)
kl divergence
(2)
learning theory
(2)
semi-supervised learning
(2)
mutual information
(2)
information theory
(2)
adversarial learning
(1)
domain adaptation
(1)
compressed sensing
(1)
entropy minimization
(1)
active learning
(1)
feature selection
(1)
transfer learning
(1)
multiple instance learning
(1)
adversarial robustness
(1)
language model evaluation
(1)
topic modeling
(1)
optimal transport
(1)
Papers
Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstedeโs Cultural Dimensions
COLING 2025
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
AAAI 2025
ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data
AISTATS 2024
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
AISTATS 2023
Retrieval-Augmented Multiple Instance Learning
NeurIPS 2023
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
AISTATS 2022
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
AISTATS 2022
An Exact Characterization of the Generalization Error for the Gibbs Algorithm
NeurIPS 2021
Blind Pareto Fairness and Subgroup Robustness
ICML 2021
On the effects of quantisation on model uncertainty in Bayesian neural networks
UAI 2021
Adversarially Learned Representations for Information Obfuscation and Inference
ICML 2019
Generalization Error of Invariant Classifiers
AISTATS 2017
Nonlinear Information-Theoretic Compressive Measurement Design
ICML 2014
Designed Measurements for Vector Count Data
NeurIPS 2013