Notice détaillée
Titre
BAN
Formal Name (French)
Chair of Business Analytics
Formal Name (English)
Chair of Business Analytics
Lab Manager
Kiyavash, Negar
Group ID
U13718
Auteurs affilié
Aboueimehrizi, Amir Mohammad
Akbari, Sina
Borges de Melo, Pedro Henrique
Devenoge, Angela
Elahi, Sepehr
Etesami, Jalal
Ganassali, Luca
Jamshidi, Fateme
Khorasani, Mohammadsadegh
Kivva, Yaroslav
Kiyavash, Negar
Konobeev, Mikhail
Masiha, Mohammadsaeed
Mokhtarian, Ehsan
Pourkamali, Farzad
Salehkaleybar, Saber
Shahverdikondori, Mohammad
Von Schack Füzesi, Alexandra
Akbari, Sina
Borges de Melo, Pedro Henrique
Devenoge, Angela
Elahi, Sepehr
Etesami, Jalal
Ganassali, Luca
Jamshidi, Fateme
Khorasani, Mohammadsadegh
Kivva, Yaroslav
Kiyavash, Negar
Konobeev, Mikhail
Masiha, Mohammadsaeed
Mokhtarian, Ehsan
Pourkamali, Farzad
Salehkaleybar, Saber
Shahverdikondori, Mohammad
Von Schack Füzesi, Alexandra
Institut
MTEI
Faculté
CDM
Note
BAN members
Lien extérieur
https://www.epfl.ch/schools/cdm/college-of-management-of-technology/management-of-technology-institute/
Publications
(Dis)assortative partitions on random regular graphs
A Catalyst Framework for Minimax Optimization
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
A Wasserstein-based measure of conditional dependence
Causal Effect Identification with Context-specific Independence Relations of Control Variables
Graph Signal Processing: Foundations and Emerging Directions [From the Guest Editors]
Learning Hawkes Processes Under Synchronization Noise
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables
The KDD 2021 Workshop on Causal Discovery (CD2021)
Voir toutes les publications (28)
A Catalyst Framework for Minimax Optimization
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
A Wasserstein-based measure of conditional dependence
Causal Effect Identification with Context-specific Independence Relations of Control Variables
Graph Signal Processing: Foundations and Emerging Directions [From the Guest Editors]
Learning Hawkes Processes Under Synchronization Noise
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables
The KDD 2021 Workshop on Causal Discovery (CD2021)
Voir toutes les publications (28)
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Authorities > Lab