← Authors

Negar Kiyavash

55 papers · 2017–2026 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (8) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (43) πŸ”¬ Deep Specialist (28) πŸ† Keyword Champion (2) 🀝 Dynamic Duo (16) πŸ”₯ Unstoppable (9) πŸ’Ž Century Club (54) πŸ—ƒοΈ Keyword Collector (159) ⚑ Prolific Year (7) πŸš€ Conference Pioneer

Conferences

NIPS (18) ICML (11) UAI (8) AAAI (5) CLEAR (5) AISTATS (4) JMLR (4)

Top co-authors

Jalal Etesami (16) Saber Salehkaleybar (15) Sina Akbari (13) Ehsan Mokhtarian (12) AmirEmad Ghassami (11) Niao He (10) Fateme Jamshidi (7) Matthias Grossglauser (6) Yaroslav Kivva (6) Kun Zhang (6)

Keywords

causal inference (14) causal discovery (11) causal graph (7) graphical model (6) causal structure (5) experiment design (5) causal structure learning (5) causal effect identification (5) structure learning (4) variance reduction (4) structural causal model (4) structural equation model (3) stochastic optimization (3) bayesian network (3) latent variable (3) minimax optimization (3) nonconvex optimization (2) convex optimization (2) stochastic gradient descent (2) reinforcement learning (2)

Papers

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