Jalal Etesami

Orcid: 0000-0002-8655-5028

According to our database1, Jalal Etesami authored at least 41 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Fast Proxy Experiment Design for Causal Effect Identification.
CoRR, 2024

Confounded Budgeted Causal Bandits.
Proceedings of the Causal Learning and Reasoning, 2024

2023
Analysis of Large Market Data Using Neural Networks: A Causal Approach.
IEEE J. Sel. Areas Inf. Theory, 2023

Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework.
CoRR, 2023

Causal Bandits without Graph Learning.
CoRR, 2023

On Identifiability of Conditional Causal Effects.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Causal Effect Identification in Uncertain Causal Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Novel Ordering-Based Approaches for Causal Structure Learning in the Presence of Unobserved Variables.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect.
CoRR, 2022

A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics.
CoRR, 2022

Revisiting the general identifiability problem.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Minimum Cost Intervention Design for Causal Effect Identification.
Proceedings of the International Conference on Machine Learning, 2022

Causal Effect Identification with Context-specific Independence Relations of Control Variables.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Learning Bayesian Networks in the Presence of Structural Side Information.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Cumulants of Hawkes Processes are Robust to Observation Noise.
Proceedings of the 38th International Conference on Machine Learning, 2021

A Variational Inference Approach to Learning Multivariate Wold Processes.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Non-cooperative Multi-agent Systems with Exploring Agents.
CoRR, 2020

Causal Transfer for Imitation Learning and Decision Making under Sensor-Shift.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Wasserstein Adversarial Imitation Learning.
CoRR, 2019

Learning Hawkes Processes Under Synchronization Noise.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Optimal Attack Strategies Against Predictors - Learning From Expert Advice.
IEEE Trans. Inf. Forensics Secur., 2018

Learning Vector Autoregressive Models With Latent Processes.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Measuring Causal Relationships in Dynamical Systems through Recovery of Functional Dependencies.
IEEE Trans. Signal Inf. Process. over Networks, 2017

Learning Latent Networks in Vector Auto Regressive Models.
CoRR, 2017

A New Measure of Conditional Dependence for Causal Structural Learning.
CoRR, 2017

Online Learning for Multivariate Hawkes Processes.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Identifying nonlinear 1-step causal influences in presence of latent variables.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

Efficient neighborhood selection for walk summable Gaussian graphical models.
Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016
Learning Minimal Latent Directed Information Polytrees.
Neural Comput., 2016

On the Vulnerability of Digital Fingerprinting Systems to Finite Alphabet Collusion Attacks.
CoRR, 2016

Learning Network of Multivariate Hawkes Processes: A Time Series Approach.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Interventional dependency graphs: An approach for discovering influence structure.
Proceedings of the IEEE International Symposium on Information Theory, 2016

2015
Efficient Neighborhood Selection for Gaussian Graphical Models.
CoRR, 2015

2014
A novel collusion attack on finite alphabet digital fingerprinting systems.
Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Directed Information Graphs: A generalization of Linear Dynamical Graphs.
Proceedings of the American Control Conference, 2014

2013
Robust directed tree approximations for networks of stochastic processes.
Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

2012
Learning minimal latent directed information trees.
Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

LDPC code construction for wireless physical-layer key reconciliation.
Proceedings of the 2012 1st IEEE International Conference on Communications in China (ICCC), 2012

2011
Iterative distributed channel probing for cognitive radios with power-controlled wireless links.
Proceedings of the 8th International Symposium on Wireless Communication Systems, 2011

LCD Codes and Iterative Decoding by Projections, a First Step Towards an Intuitive Description of Iterative Decoding.
Proceedings of the Global Communications Conference, 2011


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