Sara Magliacane
20 papers · 2016–2025 · 7 conferences · across top CS/AI conferences
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(21)
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Conferences
NIPS (7)
ICML (4)
ICLR (3)
CLEAR (2)
UAI (2)
AISTATS (1)
JMLR (1)
Top co-authors
Keywords
causal discovery
(5)
causal inference
(3)
representation learning
(2)
causal representation learning
(2)
transfer learning
(1)
domain adaptation
(1)
factor analysis
(1)
normalizing flow
(1)
temporal sequence
(1)
embodied ai
(1)
compositional generalization
(1)
time series
(1)
experimental design
(1)
causal structure
(1)
graphical model
(1)
conditional distribution
(1)
conditional probability
(1)
dynamical system
(1)
structure learning
(1)
reinforcement learning
(1)
Papers
SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph
AISTATS 2025
Combining Causal Models for More Accurate Abstractions of Neural Networks
CLEAR 2025
Multi-View Causal Representation Learning with Partial Observability
ICLR 2024
A Sparsity Principle for Partially Observable Causal Representation Learning
ICML 2024
Learning Causal Abstractions of Linear Structural Causal Models
UAI 2024
Amortized Equation Discovery in Hybrid Dynamical Systems
ICML 2024
Towards the Reusability and Compositionality of Causal Representations
CLEAR 2024
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
ICLR 2023
BISCUIT: Causal Representation Learning from Binary Interactions
UAI 2023
Graph Switching Dynamical Systems
ICML 2023
Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning
NIPS 2023
Modulated Neural ODEs
NIPS 2023
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
ICLR 2022
Factored Adaptation for Non-Stationary Reinforcement Learning
NIPS 2022
CITRIS: Causal Identifiability from Temporal Intervened Sequences
ICML 2022
Active Structure Learning of Causal DAGs via Directed Clique Trees
NIPS 2020
Joint Causal Inference from Multiple Contexts
JMLR 2020
Sample Efficient Active Learning of Causal Trees
NIPS 2019
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
NIPS 2018
Ancestral Causal Inference
NIPS 2016