Negar Kiyavash

Recursive Causal Discovery (Abstract Reprint) AAAI 2026 Causal Bandits without Graph Learning CLEAR 2025 Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding CLEAR 2025 Multi-Domain Causal Discovery in Bijective Causal Models CLEAR 2025 Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants ICML 2025 Hierarchical Reinforcement Learning with Targeted Causal Interventions ICML 2025 Recursive Causal Discovery JMLR 2025 Optimal Experiment Design for Causal Effect Identification JMLR 2025 Efficiently Escaping Saddle Points for Policy Optimization UAI 2025 Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments UAI 2025 Multi-armed Bandits with Missing Outcomes UAI 2025 On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery ICML 2024 Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence – Preface UAI 2024 Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data AISTATS 2024 Confounded Budgeted Causal Bandits CLEAR 2024 Triple Changes Estimator for Targeted Policies ICML 2024 Causal Effect Identification in a Sub-Population with Latent Variables NIPS 2024 Fast Proxy Experiment Design for Causal Effect Identification NIPS 2024 QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs NIPS 2024 Causal Effect Identification in LiNGAM Models with Latent Confounders ICML 2024 s-ID: Causal Effect Identification in a Sub-population AAAI 2024 On Identifiability of Conditional Causal Effects UAI 2023 Causal Effect Identification in Uncertain Causal Networks NIPS 2023 A Cross-Moment Approach for Causal Effect Estimation NIPS 2023 Causal Imitability Under Context-Specific Independence Relations NIPS 2023 A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models JMLR 2023 Novel Ordering-Based Approaches for Causal Structure Learning in the Presence of Unobserved Variables AAAI 2023 Revisiting the general identifiability problem UAI 2022 Causal Discovery in Linear Latent Variable Models Subject to Measurement Error NIPS 2022 Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions NIPS 2022 Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality NIPS 2022 Learning Bayesian Networks in the Presence of Structural Side Information AAAI 2022 Causal Effect Identification with Context-specific Independence Relations of Control Variables AISTATS 2022 Causal Discovery in Linear Structural Causal Models with Deterministic Relations CLEAR 2022 Minimum Cost Intervention Design for Causal Effect Identification ICML 2022 Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias NIPS 2021 A Variational Inference Approach to Learning Multivariate Wold Processes AISTATS 2021 Cumulants of Hawkes Processes are Robust to Observation Noise ICML 2021 The complexity of nonconvex-strongly-concave minimax optimization UAI 2021 Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs ICML 2020 LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments ICML 2020 Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables JMLR 2020 The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models NIPS 2020 Model-Augmented Conditional Mutual Information Estimation for Feature Selection UAI 2020 A Catalyst Framework for Minimax Optimization NIPS 2020 Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems NIPS 2020 Learning Positive Functions with Pseudo Mirror Descent NIPS 2019 Learning Hawkes Processes Under Synchronization Noise ICML 2019 Database Alignment with Gaussian Features AISTATS 2019 Counting and Sampling from Markov Equivalent DAGs Using Clique Trees AAAI 2019 Budgeted Experiment Design for Causal Structure Learning ICML 2018 Predictive Approximate Bayesian Computation via Saddle Points NIPS 2018 Multi-domain Causal Structure Learning in Linear Systems NIPS 2018 Online Learning for Multivariate Hawkes Processes NIPS 2017 Learning Causal Structures Using Regression Invariance NIPS 2017