Christos Louizos
22 papers · 2016–2025 · 7 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+15 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (12) π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge π£ Hot Topic Early Bird
π
Cross-Pollinator
(11)
π
Renaissance Researcher
(5)
π
Interdisciplinary Bridge
π€
Dynamic Duo
(13)
π
Triple Crown
π±
Topic Pioneer
π
Grand Slam
π
Keyword Champion
(2)
π
Conference Pioneer
β‘
Prolific Year
(5)
π₯
Unstoppable
(6)
β
The Questioner
π
Trend Setter
π
Century Club
(22)
ποΈ
Keyword Collector
(92)
Conferences
ICLR (7)
NIPS (6)
ICML (4)
AISTATS (2)
AAAI (1)
INTERSPEECH (1)
MIDL (1)
Top co-authors
Research topics
Keywords
federated learning
(4)
model compression
(4)
variational inference
(2)
neural network quantization
(2)
bayesian inference
(2)
user verification
(2)
bayesian neural network
(2)
variational autoencoder
(2)
contact tracing
(2)
neural network
(2)
weight quantization
(2)
uncertainty estimation
(2)
causal inference
(2)
neural network pruning
(2)
sampling strategy
(1)
embedding learning
(1)
post-training quantization
(1)
domain generalization
(1)
distributed optimization
(1)
conformal prediction
(1)
Papers
Multi-Draft Speculative Sampling: Canonical Decomposition and Theoretical Limits
ICLR 2025
Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence
ICLR 2025
On Sampling Strategies for Spectral Model Sharding
NIPS 2024
An Information Theoretic Perspective on Conformal Prediction
NIPS 2024
A Mutual Information Perspective on Federated Contrastive Learning
ICLR 2024
Protect Your Score: Contact-Tracing with Differential Privacy Guarantees
AAAI 2024
Importance Matching Lemma for Lossy Compression with Side Information
AISTATS 2024
No time to waste: practical statistical contact tracing with few low-bit messages
AISTATS 2023
Hyperparameter Optimization through Neural Network Partitioning
ICLR 2023
Federated Learning Toolkit with Voice-based User Verification Demo
INTERSPEECH 2023
Federated Learning of User Verification Models Without Sharing Embeddings
ICML 2021
DIVA: Domain Invariant Variational Autoencoders
MIDL 2020
Bayesian Bits: Unifying Quantization and Pruning
NIPS 2020
Gradient $\ell_1$ Regularization for Quantization Robustness
ICLR 2020
Up or Down? Adaptive Rounding for Post-Training Quantization
ICML 2020
The Functional Neural Process
NIPS 2019
Relaxed Quantization for Discretized Neural Networks
ICLR 2019
Learning Sparse Neural Networks through L_0 Regularization
ICLR 2018
Bayesian Compression for Deep Learning
NIPS 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
ICML 2017
Causal Effect Inference with Deep Latent-Variable Models
NIPS 2017
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
ICML 2016