Saber Salehkaleybar
20 papers · 2017–2026 · 8 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (8) π£ Hot Topic Early Bird π Interdisciplinary Bridge π§ Keyword Pioneer π Academic Marathon (8)
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Hot Topic Early Bird
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Cross-Pollinator
(10)
πΊοΈ
Taxonomy Completionist
(23)
π€
Dynamic Duo
(15)
π
Grand Slam
π₯
Unstoppable
(9)
π
Century Club
(19)
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Conference Pioneer
β‘
Prolific Year
(6)
ποΈ
Keyword Collector
(60)
Conferences
ICML (6)
NIPS (4)
JMLR (3)
AAAI (2)
UAI (2)
AISTATS (1)
CLEAR (1)
ICLR (1)
Top co-authors
Keywords
causal inference
(5)
causal discovery
(5)
structural causal model
(3)
experiment design
(3)
one-shot learning
(2)
directed acyclic graph
(2)
multi-resolution estimator
(2)
communication efficiency
(2)
distributed optimization
(2)
stochastic gradient descent
(1)
causal effect estimation
(1)
sample complexity
(1)
distributed learning
(1)
causal structure learning
(1)
reinforcement learning
(1)
cycle detection
(1)
parameter estimation
(1)
variance reduction
(1)
causal structure
(1)
structure learning
(1)
Papers
Learning Subgroups with Maximum Treatment Effects Without Causal Heuristics
AAAI 2026
Hierarchical Reinforcement Learning with Targeted Causal Interventions
ICML 2025
Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
UAI 2025
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
ICML 2025
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
ICML 2025
Multi-Domain Causal Discovery in Bijective Causal Models
CLEAR 2025
Efficiently Escaping Saddle Points for Policy Optimization
UAI 2025
Causal Effect Identification in LiNGAM Models with Latent Confounders
ICML 2024
Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
AISTATS 2024
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
JMLR 2023
A Cross-Moment Approach for Causal Effect Estimation
NIPS 2023
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions
NIPS 2022
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them
JMLR 2021
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
ICML 2020
Bounds on Over-Parameterization for Guaranteed Existence of Descent Paths in Shallow ReLU Networks
ICLR 2020
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
JMLR 2020
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
AAAI 2019
Order Optimal One-Shot Distributed Learning
NIPS 2019
Budgeted Experiment Design for Causal Structure Learning
ICML 2018
Learning Causal Structures Using Regression Invariance
NIPS 2017