Miguel Rodrigues
Professor of Information Theory and Processing
Leads the Information, Inference and Machine Learning Lab, combining information theory and machine learning for signal and data processing.
Miguel Rodrigues's lab at UCL works on learning theory & optimization, signal processing & information theory and efficient training & inference. 29 papers since 2023, 8 at top venues, mostly AISTATS and ICLR. 7 PhD students. 6 alumni.
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 28%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 6
Papers by yearfrom DBLP, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 8 papers2025 · T1 2
- 10 papers2024 · T1 1
- 10 papers2023 · T1 4
T1 venuesOtherVenues: AISTATS, ICLR, AAAI, ACM MM, BMVC
Research focuspaper titles that use each topic's words
- Learning theory & optimization10 papers
- Signal processing & information theory4 papers
- information4 papers
- Efficient training & inference1 paper
- data processing0 papers
Paper list
From DBLP.
20261 paper
20258 papers
- Adv. Intell. Syst.A Novel Semi-Automated Pipeline for Optimizing 3D-Printed Drug Formulations (opens in a new tab)
- NeurocomputingBorrowing treasures from neighbors: In-context learning for multimodal learning with missing modalities and data scarcity (opens in a new tab)
- COLING (T1)Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions (opens in a new tab)
- IEEE Trans. Control. Syst. Technol.Online End-to-End Learning-Based Predictive Control for Microgrid Energy Management (opens in a new tab)
- AAAI (T1)PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks (opens in a new tab)
- Trans. Mach. Learn. Res.TFAR: A Training-Free Framework for Autonomous Reliable Reasoning in Visual Question Answering (opens in a new tab)
- ACM MMUnveiling Open-set Noise: Theoretical Insights into Label Noise (opens in a new tab)
- Trans. Mach. Learn. Res.Wasserstein Modality Alignment Makes Your Multimodal Transformer More Robust (opens in a new tab)
202410 papers
- AISTATS (T1)ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data (opens in a new tab)
- EntropyExploring the Impact of Additive Shortcuts in Neural Networks via Information Bottleneck-like Dynamics: From ResNet to Transformer (opens in a new tab)
- IEEE AccessHgbNet: Predicting Hemoglobin Level/Anemia Degree From Irregular EHR (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Hyperspectral Blind Unmixing Using a Double Deep Image Prior (opens in a new tab)
- IEEE Trans. Inf. TheoryInformation-Theoretic Characterizations of Generalization Error for the Gibbs Algorithm (opens in a new tab)
- IEEE J. Sel. Areas Inf. TheoryLearning Algorithm Generalization Error Bounds via Auxiliary Distributions (opens in a new tab)
- Trans. Mach. Learn. Res.Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks (opens in a new tab)
- IEEE Trans. Signal Process.Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding (opens in a new tab)
- BMVCSAE: Single Architecture Ensemble Neural Networks (opens in a new tab)
- Tiny Papers @ ICLRSynthetic Labeling: A Novel Approach to Advancing Few-Shot Learning (opens in a new tab)
202310 papers
- MEDAn Online Learning Method for Microgrid Energy Management Control* (opens in a new tab)
- ICIPBITS-Net: Blind Image Transparency Separation Network (opens in a new tab)
- IEEE Trans. Cogn. Commun. Netw.Deep Joint Source-Channel Coding for Image Transmission With Visual Protection (opens in a new tab)
- ICLR (T1)Generalization and Estimation Error Bounds for Model-based Neural Networks (opens in a new tab)
- AISTATS (T1)How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm? (opens in a new tab)
- TIP (T1)Image Separation With Side Information: A Connected Auto-Encoders Based Approach (opens in a new tab)
- CVPR WorkshopMIMMO: Multi-Input Massive Multi-Output Neural Network (opens in a new tab)
- EntropyOn Neural Networks Fitting, Compression, and Generalization Behavior via Information-Bottleneck-like Approaches (opens in a new tab)
- ISITOn the Generalization Error of Meta Learning for the Gibbs Algorithm (opens in a new tab)
- NeurIPS (T1)Retrieval-Augmented Multiple Instance Learning (opens in a new tab)
Where alumni went6 listed
Apply to UCLFall 2027
Not announced yet
Computer Science MPhil/PhD
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- USUT Austin
Joydeep Ghosh
- Trustworthy & safe ML
- DETUM
Klaus Diepold
- Signal processing & information theory
- USUSC
Salman Avestimehr
- Trustworthy & safe ML
- CNPeking
Tao Xie
- Trustworthy & safe ML
- GBCambridge
Hatice Gunes
- Social computing & responsible AI
- DETUM
Massimo Fornasier
- Learning theory & optimization
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- DBLP
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.