Yaniv Gurwicz
9 papers · 2018–2026 · 3 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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Conference Polyglot Β· Bronze
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Academic Marathon Β· Sprint
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Interdisciplinary Bridge Β· Bronze
πΊοΈ
Taxonomy Completionist
(13)
π£
Hot Topic Early Bird
Venues
Top co-authors
Research topics
Artificial Intelligence
(7)
Deep Learning
(5)
Core AI
(5)
Architectures
(4)
Knowledge & Reasoning
(3)
Machine Learning
(3)
Reasoning
(3)
Data Science & Analytics
(2)
Bayesian & Probabilistic
(2)
Application Areas
(1)
Applications
(1)
Core Methods
(1)
Learning Types
(1)
Methods
(1)
Optimization & Theory
(1)
Keywords
causal discovery
(4)
latent confounder
(2)
structure learning
(1)
uncertainty quantification
(1)
attention mechanism
(1)
time series
(1)
machine learning
(1)
out-of-distribution detection
(1)
conditional independence
(1)
bayesian network
(1)
latent variable model
(1)
graphical model
(1)
structural equation model
(1)
disentangled representation
(1)
convolutional network
(1)
causal structure
(1)
world model
(1)
bayesian neural network
(1)
causal representation learning
(1)
zero-shot learning
(1)
Papers
Learning a Spatial Partitioning and its Causal Relations from Temporal Data
CLeaR 2026
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
ICML 2025
Causal Interpretation of Self-Attention in Pre-Trained Transformers
NeurIPS 2023
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
NeurIPS 2023
From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
ICML 2023
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias
NeurIPS 2021
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
NeurIPS 2019
Bayesian Structure Learning by Recursive Bootstrap
NeurIPS 2018
Constructing Deep Neural Networks by Bayesian Network Structure Learning
NeurIPS 2018