LCD Codes and Iterative Decoding by Projections, a First Step Towards an Intuitive Description of Iterative Decoding. - Jalal Etesami; Fangning Hu; Werner Henkel (GLOBECOM, 2011)

Iterative distributed channel probing for cognitive radios with power-controlled wireless links. - Jafar Mohammadi; Slawomir Stanczak; Renato L. G. Cavalcante; Jalal Etesami (ISWCS, 2011)

LDPC code construction for wireless physical-layer key reconciliation. - Jalal Etesami; Werner Henkel (ICCC, 2012)

Learning minimal latent directed information trees. - Jalal Etesami; Negar Kiyavash; Todd P. Coleman (ISIT, 2012)

Robust directed tree approximations for networks of stochastic processes. - Christopher J. Quinn; Jalal Etesami; Negar Kiyavash; Todd P. Coleman (ISIT, 2013)

Directed Information Graphs: A generalization of Linear Dynamical Graphs. - Jalal Etesami; Negar Kiyavash (ACC, 2014)

A novel collusion attack on finite alphabet digital fingerprinting systems. - Jalal Etesami; Negar Kiyavash (ISIT, 2014)

Efficient Neighborhood Selection for Gaussian Graphical Models. - Yingxiang Yang; Jalal Etesami; Negar Kiyavash (CoRR, 2015)

Interventional dependency graphs: An approach for discovering influence structure. - Jalal Etesami; Negar Kiyavash (ISIT, 2016)

Learning Network of Multivariate Hawkes Processes: A Time Series Approach. - Jalal Etesami; Negar Kiyavash; Kun Zhang 001; Kushagra Singhal (UAI, 2016)

On the Vulnerability of Digital Fingerprinting Systems to Finite Alphabet Collusion Attacks. - Jalal Etesami; Negar Kiyavash (CoRR, 2016)

Learning Minimal Latent Directed Information Polytrees. - Jalal Etesami; Negar Kiyavash; Todd P. Coleman (Neural Comput., 2016)

Efficient neighborhood selection for walk summable Gaussian graphical models. - Yingxang Yang; Jalal Etesami; Negar Kiyavash (ACSSC, 2017)

Identifying nonlinear 1-step causal influences in presence of latent variables. - Saber Salehkaleybar; Jalal Etesami; Negar Kiyavash (ISIT, 2017)

Online Learning for Multivariate Hawkes Processes. - Yingxiang Yang; Jalal Etesami; Niao He; Negar Kiyavash (NIPS, 2017)

A New Measure of Conditional Dependence for Causal Structural Learning. - Jalal Etesami; Kun Zhang 001; Negar Kiyavash (CoRR, 2017)

Learning Latent Networks in Vector Auto Regressive Models. - Saber Salehkaleybar; Jalal Etesami; Negar Kiyavash (CoRR, 2017)

Measuring Causal Relationships in Dynamical Systems through Recovery of Functional Dependencies. - Jalal Etesami; Negar Kiyavash (IEEE Trans. Signal Inf. Process. over Networks, 2017)

Learning Vector Autoregressive Models With Latent Processes. - Saber Salehkaleybar; Jalal Etesami; Negar Kiyavash; Kun Zhang 001 (AAAI, 2018)

Optimal Attack Strategies Against Predictors - Learning From Expert Advice. - Anh Truong; S. Rasoul Etesami 001; Jalal Etesami; Negar Kiyavash (IEEE Trans. Inf. Forensics Secur., 2018)

Learning Hawkes Processes Under Synchronization Noise. - William Trouleau; Jalal Etesami; Matthias Grossglauser; Negar Kiyavash; Patrick Thiran (ICML, 2019)

Wasserstein Adversarial Imitation Learning. - Huang Xiao; Michael Herman; Jörg Wagner; Sebastian Ziesche; Jalal Etesami; Thai Hong Linh (CoRR, 2019)

Causal Transfer for Imitation Learning and Decision Making under Sensor-Shift. - Jalal Etesami; Philipp Geiger (AAAI, 2020)

Non-cooperative Multi-agent Systems with Exploring Agents. - Jalal Etesami; Christoph-Nikolas Straehle (CoRR, 2020)

A Variational Inference Approach to Learning Multivariate Wold Processes. - Jalal Etesami; William Trouleau; Negar Kiyavash; Matthias Grossglauser; Patrick Thiran (AISTATS, 2021)

Cumulants of Hawkes Processes are Robust to Observation Noise. - William Trouleau; Jalal Etesami; Matthias Grossglauser; Negar Kiyavash; Patrick Thiran (ICML, 2021)

Learning Bayesian Networks in the Presence of Structural Side Information. - Ehsan Mokhtarian; Sina Akbari; Fateme Jamshidi; Jalal Etesami; Negar Kiyavash (AAAI, 2022)

Causal Effect Identification with Context-specific Independence Relations of Control Variables. - Ehsan Mokhtarian; Fateme Jamshidi; Jalal Etesami; Negar Kiyavash (AISTATS, 2022)

Minimum Cost Intervention Design for Causal Effect Identification. - Sina Akbari; Jalal Etesami; Negar Kiyavash (ICML, 2022)

Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality. - Ilyas Fatkhullin; Jalal Etesami; Niao He; Negar Kiyavash (NeurIPS, 2022)

Revisiting the general identifiability problem. - Yaroslav Kivva; Ehsan Mokhtarian; Jalal Etesami; Negar Kiyavash (UAI, 2022)

A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics. - Matthew J. Vowels; Sina Akbari; Jalal Etesami; Necati Cihan Camgöz; Richard Bowden (CoRR, 2022)

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect. - Sina Akbari; Fateme Jamshidi; Ehsan Mokhtarian; Matthew J. Vowels; Jalal Etesami; Negar Kiyavash (CoRR, 2022)

Novel Ordering-Based Approaches for Causal Structure Learning in the Presence of Unobserved Variables. - Ehsan Mokhtarian; Mohammadsadegh Khorasani; Jalal Etesami; Negar Kiyavash (AAAI, 2023)

Causal Effect Identification in Uncertain Causal Networks. - Sina Akbari; Fateme Jamshidi; Ehsan Mokhtarian; Matthew J. Vowels; Jalal Etesami; Negar Kiyavash (NeurIPS, 2023)

On Identifiability of Conditional Causal Effects. - Yaroslav Kivva; Jalal Etesami; Negar Kiyavash (UAI, 2023)

Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework. - Jalal Etesami; Ali Habibnia; Negar Kiyavash (CoRR, 2023)

Analysis of Large Market Data Using Neural Networks: A Causal Approach. - Marc-Aurèle Divernois; Jalal Etesami; Damir Filipovic; Negar Kiyavash (IEEE J. Sel. Areas Inf. Theory, 2023)

Confounded Budgeted Causal Bandits. - Fateme Jamshidi; Jalal Etesami; Negar Kiyavash (CLeaR, 2024)

Fast Proxy Experiment Design for Causal Effect Identification. - Sepehr Elahi; Sina Akbari; Jalal Etesami; Negar Kiyavash; Patrick Thiran (NeurIPS, 2024)

Causal Bandits without Graph Learning. - Mikhail Konobeev; Jalal Etesami; Negar Kiyavash (CLeaR, 2025)

Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations. - Larkin Liu; Kashif Rasul; Yutong Chao; Jalal Etesami (CoRR, 2025)

Recommendations from Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization. - Suryanarayana Sankagiri; Jalal Etesami; Matthias Grossglauser (CoRR, 2025)