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2020 – today
- 2023
[c100]Ehsan Mokhtarian, Mohammadsadegh Khorasani, Jalal Etesami, Negar Kiyavash:
Novel Ordering-Based Approaches for Causal Structure Learning in the Presence of Unobserved Variables. AAAI 2023: 12260-12268- [c99]Yaroslav Kivva, Jalal Etesami, Negar Kiyavash:
On Identifiability of Conditional Causal Effects. UAI 2023: 1078-1086 - [i75]Mikhail Konobeev, Jalal Etesami, Negar Kiyavash:
Causal Bandits without Graph Learning. CoRR abs/2301.11401 (2023) - [i74]Sina Akbari, Luca Ganassali, Negar Kiyavash:
Learning Causal Graphs via Monotone Triangular Transport Maps. CoRR abs/2305.18210 (2023) - [i73]Fateme Jamshidi, Sina Akbari, Negar Kiyavash:
Causal Imitability Under Context-Specific Independence Relations. CoRR abs/2306.00585 (2023) - [i72]Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash:
A Cross-Moment Approach for Causal Effect Estimation. CoRR abs/2306.06263 (2023) - [i71]Yaroslav Kivva, Jalal Etesami, Negar Kiyavash:
On Identifiability of Conditional Causal Effects. CoRR abs/2306.11755 (2023) - [i70]Osman Emre Dai, Daniel Cullina
, Negar Kiyavash:
Gaussian Database Alignment and Gaussian Planted Matching. CoRR abs/2307.02459 (2023) - [i69]Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash:
s-ID: Causal Effect Identification in a Sub-Population. CoRR abs/2309.02281 (2023) - [i68]Fateme Jamshidi, Luca Ganassali, Negar Kiyavash:
On sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery. CoRR abs/2310.13553 (2023) - [i67]Ishan Kumar, Zhijing Jin, Ehsan Mokhtarian, Siyuan Guo, Yuen Chen, Negar Kiyavash, Mrinmaya Sachan, Bernhard Schölkopf:
CausalCite: A Causal Formulation of Paper Citations. CoRR abs/2311.02790 (2023) - [i66]Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser:
Efficiently Escaping Saddle Points for Non-Convex Policy Optimization. CoRR abs/2311.08914 (2023) - 2022
- [c98]Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash:
Learning Bayesian Networks in the Presence of Structural Side Information. AAAI 2022: 7814-7822 - [c97]Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash:
Causal Effect Identification with Context-specific Independence Relations of Control Variables. AISTATS 2022: 11237-11246 - [c96]Yuqin Yang, Mohamed S. Nafea, AmirEmad Ghassami, Negar Kiyavash:
Causal Discovery in Linear Structural Causal Models with Deterministic Relations. CLeaR 2022: 944-993 - [c95]Sina Akbari, Jalal Etesami, Negar Kiyavash:
Minimum Cost Intervention Design for Causal Effect Identification. ICML 2022: 258-289 - [c94]Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash:
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality. NeurIPS 2022 - [c93]Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran:
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions. NeurIPS 2022 - [c92]Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser:
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. NeurIPS 2022 - [c91]Yaroslav Kivva, Ehsan Mokhtarian, Jalal Etesami, Negar Kiyavash:
Revisiting the general identifiability problem. UAI 2022: 1022-1030 - [i65]Sina Akbari, Jalal Etesami, Negar Kiyavash:
Minimum Cost Intervention Design for Causal Effect Identification. CoRR abs/2205.02232 (2022) - [i64]Saber Salehkaleybar, Sadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran:
Adaptive Momentum-Based Policy Gradient with Second-Order Information. CoRR abs/2205.08253 (2022) - [i63]Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash:
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models. CoRR abs/2205.10083 (2022) - [i62]Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran:
Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions. CoRR abs/2205.12856 (2022) - [i61]Yaroslav Kivva, Ehsan Mokhtarian, Jalal Etesami, Negar Kiyavash:
Revisiting the General Identifiability Problem. CoRR abs/2206.01081 (2022) - [i60]Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian, Matthew J. Vowels, Jalal Etesami, Negar Kiyavash:
Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect. CoRR abs/2208.04627 (2022) - [i59]Ehsan Mokhtarian, Mohammadsadegh Khorasani, Jalal Etesami, Negar Kiyavash:
Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables. CoRR abs/2208.06935 (2022) - [i58]Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser:
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. CoRR abs/2211.03984 (2022) - 2021
- [j37]Anshul Gandhi, Negar Kiyavash, Jia Wang:
Editorial. Proc. ACM Meas. Anal. Comput. Syst. 5(2): 13:1 (2021) - [j36]S. Rasoul Etesami, Negar Kiyavash, Vincent Léon, H. Vincent Poor:
Optimal Adversarial Policies in the Multiplicative Learning System With a Malicious Expert. IEEE Trans. Inf. Forensics Secur. 16: 2276-2287 (2021) - [c90]Jalal Etesami, William Trouleau, Negar Kiyavash, Matthias Grossglauser, Patrick Thiran:
A Variational Inference Approach to Learning Multivariate Wold Processes. AISTATS 2021: 2044-2052 - [c89]Anh Truong, S. Rasoul Etesami, Negar Kiyavash:
Selective Labeling in Learning with Expert Advice. ACC 2021: 2537-2542 - [c88]William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran:
Cumulants of Hawkes Processes are Robust to Observation Noise. ICML 2021: 10444-10454 - [c87]Sajad Khodadadian, AmirEmad Ghassami, Negar Kiyavash:
Impact of Data Processing on Fairness in Supervised Learning. ISIT 2021: 2643-2648 - [c86]Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu, Negar Kiyavash:
Preface: The 2021 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2021: 1-2 - [c85]Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami, Negar Kiyavash:
A Recursive Markov Boundary-Based Approach to Causal Structure Learning. CD@KDD 2021: 26-54 - [c84]Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafillou, Elias Bareinboim, Huan Liu, Negar Kiyavash:
The KDD 2021 Workshop on Causal Discovery (CD2021). KDD 2021: 4141-4142 - [c83]Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami, Negar Kiyavash:
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias. NeurIPS 2021: 10119-10130 - [c82]Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He:
The complexity of nonconvex-strongly-concave minimax optimization. UAI 2021: 482-492 - [e2]Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu, Negar Kiyavash:
The KDD 2021 Workshop on Causal Discovery, CD@KDD 2021, Singapore, August 15, 2021. Proceedings of Machine Learning Research 150, PMLR 2021 [contents] - [e1]Longbo Huang, Anshul Gandhi, Negar Kiyavash, Jia Wang:
SIGMETRICS '21: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Virtual Event, China, June 14-18, 2021. ACM 2021, ISBN 978-1-4503-8072-0 [contents] - [i57]Sajad Khodadadian, AmirEmad Ghassami, Negar Kiyavash:
Impact of Data Processing on Fairness in Supervised Learning. CoRR abs/2102.01867 (2021) - [i56]Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He:
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization. CoRR abs/2103.15888 (2021) - [i55]Sajad Khodadadian, Mohamed S. Nafea, AmirEmad Ghassami, Negar Kiyavash:
Information Theoretic Measures for Fairness-aware Feature Selection. CoRR abs/2106.00772 (2021) - [i54]Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami, Negar Kiyavash:
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias. CoRR abs/2110.12036 (2021) - [i53]Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash:
Causal Effect Identification with Context-specific Independence Relations of Control Variables. CoRR abs/2110.12064 (2021) - [i52]Yuqin Yang, Mohamed S. Nafea, AmirEmad Ghassami, Negar Kiyavash:
Causal Discovery in Linear Structural Causal Models with Deterministic Relations. CoRR abs/2111.00341 (2021) - [i51]Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash:
Learning Bayesian Networks in the Presence of Structural Side Information. CoRR abs/2112.10884 (2021) - 2020
- [j35]Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang:
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. J. Mach. Learn. Res. 21: 39:1-39:24 (2020) - [j34]Richard G. Baraniuk, Alex Dimakis, Negar Kiyavash, Sewoong Oh, Rebecca Willett:
Guest Editorial. IEEE J. Sel. Areas Inf. Theory 1(1): 4 (2020) - [j33]Antonio G. Marques, Negar Kiyavash, José M. F. Moura, Dimitri Van De Ville, Rebecca Willett:
Graph Signal Processing: Foundations and Emerging Directions [From the Guest Editors]. IEEE Signal Process. Mag. 37(6): 11-13 (2020) - [c81]Ali AhmadiTeshnizi, Saber Salehkaleybar, Negar Kiyavash:
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments. ICML 2020: 125-133 - [c80]AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang:
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs. ICML 2020: 3494-3504 - [c79]Osman Emre Dai, Daniel Cullina, Negar Kiyavash:
Achievability of nearly-exact alignment for correlated Gaussian databases. ISIT 2020: 1230-1235 - [c78]Junchi Yang, Negar Kiyavash, Niao He:
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems. NeurIPS 2020 - [c77]Yingxiang Yang, Negar Kiyavash, Le Song, Niao He:
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models. NeurIPS 2020 - [c76]Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He:
A Catalyst Framework for Minimax Optimization. NeurIPS 2020 - [c75]Daniel Cullina, Negar Kiyavash, Prateek Mittal, H. Vincent Poor:
Partial Recovery of Erdős-Rényi Graph Alignment via k-Core Alignment. SIGMETRICS (Abstracts) 2020: 99-100 - [c74]Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum:
Model-Augmented Conditional Mutual Information Estimation for Feature Selection. UAI 2020: 1139-1148 - [i50]S. Rasoul Etesami, Negar Kiyavash, H. Vincent Poor:
Adversarial Policies in Learning Systems with Malicious Experts. CoRR abs/2001.00543 (2020) - [i49]Junchi Yang, Negar Kiyavash, Niao He:
Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems. CoRR abs/2002.09621 (2020) - [i48]Ali AhmadiTeshnizi, Saber Salehkaleybar, Negar Kiyavash:
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments. CoRR abs/2006.09670 (2020) - [i47]Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami, Negar Kiyavash:
A Recursive Markov Blanket-Based Approach to Causal Structure Learning. CoRR abs/2010.04992 (2020)
2010 – 2019
- 2019
- [j32]Ge Yu, Sheldon H. Jacobson, Negar Kiyavash:
A bi-criteria multiple-choice secretary problem. IISE Trans. 51(6): 577-588 (2019) - [j31]Osman Emre Dai, Daniel Cullina, Negar Kiyavash, Matthias Grossglauser:
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdos-Rényi Graphs. Proc. ACM Meas. Anal. Comput. Syst. 3(2): 36:1-36:25 (2019) - [j30]Daniel Cullina, Negar Kiyavash, Prateek Mittal, H. Vincent Poor:
Partial Recovery of Erdðs-Rényi Graph Alignment via k-Core Alignment. Proc. ACM Meas. Anal. Comput. Syst. 3(3): 54:1-54:21 (2019) - [c73]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees. AAAI 2019: 3664-3671 - [c72]Osman Emre Dai, Daniel Cullina, Negar Kiyavash:
Database Alignment with Gaussian Features. AISTATS 2019: 3225-3233 - [c71]William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran:
Learning Hawkes Processes Under Synchronization Noise. ICML 2019: 6325-6334 - [c70]Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He:
Learning Positive Functions with Pseudo Mirror Descent. NeurIPS 2019: 14144-14154 - [c69]Chien-Ying Chen, Sibin Mohan, Rodolfo Pellizzoni, Rakesh B. Bobba, Negar Kiyavash:
A Novel Side-Channel in Real-Time Schedulers. RTAS 2019: 90-102 - [c68]Osman Emre Dai, Daniel Cullina, Negar Kiyavash, Matthias Grossglauser:
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs. SIGMETRICS (Abstracts) 2019: 97-98 - [i46]Osman Emre Dai, Daniel Cullina, Negar Kiyavash:
Database Alignment with Gaussian Features. CoRR abs/1903.01422 (2019) - [i45]Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang:
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. CoRR abs/1908.03932 (2019) - [i44]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash:
Interventional Experiment Design for Causal Structure Learning. CoRR abs/1910.05651 (2019) - [i43]AmirEmad Ghassami, Kun Zhang, Negar Kiyavash:
Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs. CoRR abs/1910.12993 (2019) - [i42]Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum:
Model-Augmented Nearest-Neighbor Estimation of Conditional Mutual Information for Feature Selection. CoRR abs/1911.04628 (2019) - 2018
- [j29]Lin Liu, Jiuyong Li, Kun Zhang, Emre Kiciman, Negar Kiyavash:
Guest Editorial: Special Issue on Causal Discovery 2017. Int. J. Data Sci. Anal. 6(1): 1-2 (2018) - [j28]Anh Truong, S. Rasoul Etesami, Jalal Etesami, Negar Kiyavash:
Optimal Attack Strategies Against Predictors - Learning From Expert Advice. IEEE Trans. Inf. Forensics Secur. 13(1): 6-19 (2018) - [j27]AmirEmad Ghassami, Negar Kiyavash:
A Covert Queueing Channel in FCFS Schedulers. IEEE Trans. Inf. Forensics Secur. 13(6): 1551-1563 (2018) - [j26]Anh Truong, S. Rasoul Etesami, Negar Kiyavash:
Learning From Sleeping Experts: Rewarding Informative, Available, and Accurate Experts. ACM Trans. Design Autom. Electr. Syst. 23(6): 77:1-77:18 (2018) - [c67]Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang:
Learning Vector Autoregressive Models With Latent Processes. AAAI 2018: 4000-4007 - [c66]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim:
Budgeted Experiment Design for Causal Structure Learning. ICML 2018: 1719-1728 - [c65]AmirEmad Ghassami, Sajad Khodadadian, Negar Kiyavash:
Fairness in Supervised Learning: An Information Theoretic Approach. ISIT 2018: 176-180 - [c64]Daniel Cullina, Prateek Mittal, Negar Kiyavash:
Fundamental Limits of Database Alignment. ISIT 2018: 651-655 - [c63]AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang:
Multi-domain Causal Structure Learning in Linear Systems. NeurIPS 2018: 6269-6279 - [c62]Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He:
Predictive Approximate Bayesian Computation via Saddle Points. NeurIPS 2018: 10282-10291 - [c61]Anh Truong, Negar Kiyavash, Seyed Rasoul Etesami:
Adversarial Machine Learning: The Case of Recommendation Systems. SPAWC 2018: 1-5 - [i41]AmirEmad Ghassami, Sajad Khodadadian, Negar Kiyavash:
Fairness in Supervised Learning: An Information Theoretic Approach. CoRR abs/1801.04378 (2018) - [i40]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash:
Counting and Uniform Sampling from Markov Equivalent DAGs. CoRR abs/1802.01239 (2018) - [i39]Osman Emre Dai, Daniel Cullina, Negar Kiyavash, Matthias Grossglauser:
On the Performance of a Canonical Labeling for Matching Correlated Erdős-Rényi Graphs. CoRR abs/1804.09758 (2018) - [i38]Daniel Cullina, Prateek Mittal, Negar Kiyavash:
Fundamental Limits of Database Alignment. CoRR abs/1805.03829 (2018) - [i37]Chien-Ying Chen, Monowar Hasan, AmirEmad Ghassami, Sibin Mohan, Negar Kiyavash:
REORDER: Securing Dynamic-Priority Real-Time Systems Using Schedule Obfuscation. CoRR abs/1806.01393 (2018) - [i36]Chien-Ying Chen, Sibin Mohan, Rakesh B. Bobba, Rodolfo Pellizzoni, Negar Kiyavash:
ScheduLeak: A Novel Scheduler Side-Channel Attack Against Real-Time Autonomous Control Systems. CoRR abs/1806.01814 (2018) - [i35]Daniel Cullina, Negar Kiyavash, Prateek Mittal, H. Vincent Poor:
Partial Recovery of Erdős-Rényi Graph Alignment via k-Core Alignment. CoRR abs/1809.03553 (2018) - 2017
- [j25]Stephan Heuser, Bradley Reaves, Praveen Kumar Pendyala, Henry Carter, Alexandra Dmitrienko, William Enck, Negar Kiyavash, Ahmad-Reza Sadeghi, Patrick Traynor:
Phonion: Practical Protection of Metadata in Telephony Networks. Proc. Priv. Enhancing Technol. 2017(1): 170-187 (2017) - [j24]Christopher J. Quinn, Ali Pinar, Negar Kiyavash:
Bounded-Degree Connected Approximations of Stochastic Networks. IEEE Trans. Mol. Biol. Multi Scale Commun. 3(2): 79-88 (2017) - [j23]Jalal Etesami, Negar Kiyavash:
Measuring Causal Relationships in Dynamical Systems through Recovery of Functional Dependencies. IEEE Trans. Signal Inf. Process. over Networks 3(4): 650-659 (2017) - [c60]Yingxang Yang, Jalal Etesami, Negar Kiyavash:
Efficient neighborhood selection for walk summable Gaussian graphical models. ACSSC 2017: 263-267 - [c59]AmirEmad Ghassami, Negar Kiyavash:
Interaction information for causal inference: The case of directed triangle. ISIT 2017: 1326-1330 - [c58]Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash:
Identifying nonlinear 1-step causal influences in presence of latent variables. ISIT 2017: 1341-1345 - [c57]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Learning Causal Structures Using Regression Invariance. NIPS 2017: 3011-3021 - [c56]Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash:
Online Learning for Multivariate Hawkes Processes. NIPS 2017: 4937-4946 - [i34]Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash:
Identifying Nonlinear 1-Step Causal Influences in Presence of Latent Variables. CoRR abs/1701.06605 (2017) - [i33]AmirEmad Ghassami, Negar Kiyavash:
Interaction Information for Causal Inference: The Case of Directed Triangle. CoRR abs/1701.08868 (2017) - [i32]AmirEmad Ghassami, Ali Yekkehkhany, Negar Kiyavash, Yi Lu:
A Covert Queueing Channel in Round Robin Schedulers. CoRR abs/1701.08883 (2017) - [i31]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash:
Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments. CoRR abs/1702.08567 (2017) - [i30]Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash:
Learning Latent Networks in Vector Auto Regressive Models. CoRR abs/1702.08575 (2017) - [i29]Jalal Etesami, Kun Zhang, Negar Kiyavash:
A New Measure of Conditional Dependence for Causal Structural Learning. CoRR abs/1704.00607 (2017) - [i28]Chien-Ying Chen, AmirEmad Ghassami, Sibin Mohan, Negar Kiyavash, Rakesh B. Bobba, Rodolfo Pellizzoni, Man-Ki Yoon:
A Reconnaissance Attack Mechanism for Fixed-Priority Real-Time Systems. CoRR abs/1705.02561 (2017) - [i27]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Learning Causal Structures Using Regression Invariance. CoRR abs/1705.09644 (2017) - [i26]Kushagra Singhal, Daniel Cullina, Negar Kiyavash:
Significance of Side Information in the Graph Matching Problem. CoRR abs/1706.06936 (2017) - [i25]AmirEmad Ghassami, Negar Kiyavash:
A Covert Queueing Channel in FCFS Schedulers. CoRR abs/1707.07234 (2017) - [i24]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim:
Budgeted Experiment Design for Causal Structure Learning. CoRR abs/1709.03625 (2017) - [i23]Daniel Cullina, Negar Kiyavash:
Exact alignment recovery for correlated Erdos Renyi graphs. CoRR abs/1711.06783 (2017) - 2016
- [j22]Jalal Etesami, Negar Kiyavash, Todd P. Coleman:
Learning Minimal Latent Directed Information Polytrees. Neural Comput. 28(9): 1723-1768 (2016) - [j21]Daniel Cullina, Negar Kiyavash:
Generalized Sphere-Packing Bounds on the Size of Codes for Combinatorial Channels. IEEE Trans. Inf. Theory 62(8): 4454-4465 (2016) - [j20]Daniel Cullina