1 code implementation • 14 Mar 2024 • Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash
Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively.
no code implementations • 15 Jan 2024 • Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
This algorithm generalizes the state-of-the-art methods by allowing non-uniform costs and hidden confounders in the causal graph.
no code implementations • 27 Dec 2023 • Jalal Etesami, Ali Habibnia, Negar Kiyavash
We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series.
1 code implementation • 11 Dec 2023 • Yuqin Yang, Saber Salehkaleybar, Negar Kiyavash
We provide a candidate intervention target set which is a superset of the true intervention targets.
no code implementations • 15 Nov 2023 • Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser
Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance.
no code implementations • 20 Oct 2023 • Fateme Jamshidi, Luca Ganassali, Negar Kiyavash
This, in turn, allows us to characterize the sample complexity of any constraint-based causal discovery algorithm that uses VM-CI for CI tests.
1 code implementation • 5 Sep 2023 • Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash
We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population).
no code implementations • 5 Jul 2023 • Osman Emre Dai, Daniel Cullina, Negar Kiyavash
We study an instance of the database alignment problem with multivariate Gaussian features and derive results that apply both for database alignment and for planted matching, demonstrating the connection between them.
no code implementations • 19 Jun 2023 • Yaroslav Kivva, Jalal Etesami, Negar Kiyavash
It extends the results of [Lee et al., 2019, Kivva et al., 2022] on general identifiability (gID) which studied the problem for unconditional causal effects and Shpitser and Pearl [2006b] on identifiability of conditional causal effects given merely the observational distribution $P(\mathbf{V})$ as our algorithm generalizes the algorithms proposed in [Kivva et al., 2022] and [Shpitser and Pearl, 2006b].
1 code implementation • 26 Jan 2023 • Mikhail Konobeev, Jalal Etesami, Negar Kiyavash
We study the causal bandit problem when the causal graph is unknown and develop an efficient algorithm for finding the parent node of the reward node using atomic interventions.
1 code implementation • 8 Nov 2022 • Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser
We demonstrate a somewhat surprising connection between this problem and causal discovery in the presence of unobserved parentless causes, in the sense that there is a mapping, given by the mixing matrix, between the underlying models to be inferred in these problems.
no code implementations • 14 Aug 2022 • Ehsan Mokhtarian, Mohammadsadegh Khorasani, Jalal Etesami, Negar Kiyavash
We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables.
no code implementations • 2 Jun 2022 • Yaroslav Kivva, Ehsan Mokhtarian, Jalal Etesami, Negar Kiyavash
A nice property of this new algorithm is that it establishes a connection between general identifiability and classical identifiability by Pearl [1995] through decomposing the general identifiability problem into a series of classical identifiability sub-problems.
no code implementations • 25 May 2022 • Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran
We prove that the total sample complexity of SCRN in achieving $\epsilon$-global optimum is $\mathcal{O}(\epsilon^{-7/(2\alpha)+1})$ for $1\le\alpha< 3/2$ and $\mathcal{\tilde{O}}(\epsilon^{-2/(\alpha)})$ for $3/2\le\alpha\le 2$.
1 code implementation • 20 May 2022 • Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles.
no code implementations • 17 May 2022 • Saber Salehkaleybar, Sadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran
SHARP algorithm is parameter-free, achieving $\epsilon$-approximate first-order stationary point with $O(\epsilon^{-3})$ number of trajectories, while using a batch size of $O(1)$ at each iteration.
no code implementations • 4 May 2022 • Sina Akbari, Jalal Etesami, Negar Kiyavash
When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the causal effect.
1 code implementation • 30 Oct 2021 • Yuqin Yang, Mohamed Nafea, AmirEmad Ghassami, Negar Kiyavash
Linear structural causal models (SCMs)-- in which each observed variable is generated by a subset of the other observed variables as well as a subset of the exogenous sources-- are pervasive in causal inference and casual discovery.
1 code implementation • NeurIPS 2021 • Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami, Negar Kiyavash
The upper bound of our proposed approach and the lower bound at most differ by a factor equal to the number of variables in the worst case.
1 code implementation • 22 Oct 2021 • Ehsan Mokhtarian, Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations.
no code implementations • 1 Jun 2021 • Sajad Khodadadian, Mohamed Nafea, AmirEmad Ghassami, Negar Kiyavash
In particular, we first propose information theoretic measures which quantify the impact of different subsets of features on the accuracy and discrimination of the decision outcomes.
no code implementations • 29 Mar 2021 • Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He
In the averaged smooth finite-sum setting, our proposed algorithm improves over previous algorithms by providing a nearly-tight dependence on the condition number.
no code implementations • 3 Feb 2021 • Sajad Khodadadian, AmirEmad Ghassami, Negar Kiyavash
Finally, we show that by appropriate choice of the discrimination measure, the optimization problem for both pre and post processing approaches will reduce to a linear program and hence can be solved efficiently.
no code implementations • NeurIPS 2020 • Yingxiang Yang, Negar Kiyavash, Le Song, Niao He
Macroscopic data aggregated from microscopic events are pervasive in machine learning, such as country-level COVID-19 infection statistics based on city-level data.
no code implementations • NeurIPS 2020 • Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He
We introduce a generic \emph{two-loop} scheme for smooth minimax optimization with strongly-convex-concave objectives.
no code implementations • NeurIPS 2020 • Junchi Yang, Negar Kiyavash, Niao He
Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning.
1 code implementation • 10 Oct 2020 • Ehsan Mokhtarian, Sina Akbari, AmirEmad Ghassami, Negar Kiyavash
In this paper, we propose a novel recursive constraint-based method for causal structure learning that significantly reduces the required number of CI tests compared to the existing literature.
1 code implementation • ICML 2020 • Ali AhmadiTeshnizi, Saber Salehkaleybar, Negar Kiyavash
We utilize the proposed method for computing MEC sizes and experiment design in active and passive learning settings.
no code implementations • 22 Feb 2020 • Junchi Yang, Negar Kiyavash, Niao He
Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning.
no code implementations • 2 Jan 2020 • S. Rasoul Etesami, Negar Kiyavash, Vincent Leon, H. Vincent Poor
We consider a learning system based on the conventional multiplicative weight (MW) rule that combines experts' advice to predict a sequence of true outcomes.
no code implementations • NeurIPS 2019 • Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He
The nonparametric learning of positive-valued functions appears widely in machine learning, especially in the context of estimating intensity functions of point processes.
1 code implementation • 12 Nov 2019 • Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum
In the second step, CI testing is performed by applying the $k$-NN conditional mutual information estimator to the learned feature maps.
1 code implementation • ICML 2020 • AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang
The main approach to defining equivalence among acyclic directed causal graphical models is based on the conditional independence relationships in the distributions that the causal models can generate, in terms of the Markov equivalence.
no code implementations • 12 Oct 2019 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash
For this case, we propose an efficient exact algorithm for the worst-case gain setup, as well as an approximate algorithm for the average gain setup.
no code implementations • 11 Aug 2019 • Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang
It can be shown that causal effects among observed variables cannot be identified uniquely even under the assumptions of faithfulness and non-Gaussianity of exogenous noises.
no code implementations • 4 Mar 2019 • Osman Emre Dai, Daniel Cullina, Negar Kiyavash
We consider the problem of aligning a pair of databases with jointly Gaussian features.
no code implementations • NeurIPS 2018 • Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable.
no code implementations • NeurIPS 2018 • Amiremad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang
We study the problem of causal structure learning in linear systems from observational data given in multiple domains, across which the causal coefficients and/or the distribution of the exogenous noises may vary.
no code implementations • 10 Sep 2018 • Daniel Cullina, Negar Kiyavash, Prateek Mittal, H. Vincent Poor
This estimator searches for an alignment in which the intersection of the correlated graphs using this alignment has a minimum degree of $k$.
no code implementations • 4 Jun 2018 • Chien-Ying Chen, Monowar Hasan, AmirEmad Ghassami, Sibin Mohan, Negar Kiyavash
The deterministic (timing) behavior of real-time systems (RTS) can be used by adversaries - say, to launch side channel attacks or even destabilize the system by denying access to critical resources.
Cryptography and Security
no code implementations • 25 Apr 2018 • Osman Emre Dai, Daniel Cullina, Negar Kiyavash, Matthias Grossglauser
Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs.
no code implementations • 5 Feb 2018 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang
In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class.
no code implementations • 25 Jan 2018 • Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
In this paper, we design a nonparametric online algorithm for estimating the triggering functions of multivariate Hawkes processes.
no code implementations • 13 Jan 2018 • AmirEmad Ghassami, Sajad Khodadadian, Negar Kiyavash
To ensure fairness and generalization simultaneously, we compress the data to an auxiliary variable, which is used for the prediction task.
no code implementations • NeurIPS 2017 • Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
We develop a nonparametric and online learning algorithm that estimates the triggering functions of a multivariate Hawkes process (MHP).
no code implementations • 18 Nov 2017 • Daniel Cullina, Negar Kiyavash
We consider the problem of perfectly recovering the vertex correspondence between two correlated Erd\H{o}s-R\'enyi (ER) graphs on the same vertex set.
no code implementations • ICML 2018 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim
We study the problem of causal structure learning when the experimenter is limited to perform at most $k$ non-adaptive experiments of size $1$.
no code implementations • NeurIPS 2017 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang
We study causal inference in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary.
no code implementations • 31 Mar 2017 • Jalal Etesami, Kun Zhang, Negar Kiyavash
Measuring conditional dependencies among the variables of a network is of great interest to many disciplines.
no code implementations • 27 Feb 2017 • AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash
We study the problem of causal structure learning over a set of random variables when the experimenter is allowed to perform at most $M$ experiments in a non-adaptive manner.
no code implementations • 27 Feb 2017 • Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang
We show that the support of transition matrix among the observed processes and lengths of all latent paths between any two observed processes can be identified successfully under some conditions on the VAR model.
no code implementations • 30 Jan 2017 • AmirEmad Ghassami, Negar Kiyavash
Interaction information is one of the multivariate generalizations of mutual information, which expresses the amount information shared among a set of variables, beyond the information, which is shared in any proper subset of those variables.
no code implementations • 23 Jan 2017 • Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash
We propose an approach for learning the causal structure in stochastic dynamical systems with a $1$-step functional dependency in the presence of latent variables.
no code implementations • 25 Mar 2016 • Daniel Cullina, Kushagra Singhal, Negar Kiyavash, Prateek Mittal
We ask the question "Does there exist a regime where the network cannot be deanonymized perfectly, yet the community structure could be learned?."
no code implementations • 14 Mar 2016 • Jalal Etesami, Negar Kiyavash, Kun Zhang, Kushagra Singhal
This paper studies the problem of recovering the causal structure in network of multivariate linear Hawkes processes.
no code implementations • 2 Feb 2016 • Daniel Cullina, Negar Kiyavash
For a pair of correlated graphs on the same vertex set, the correspondence between the vertices can be obscured by randomly permuting the vertex labels of one of the graphs.
no code implementations • 22 Sep 2015 • Yingxiang Yang, Jalal Etesami, Negar Kiyavash
This paper addresses the problem of neighborhood selection for Gaussian graphical models.
no code implementations • 9 Apr 2012 • Christopher J. Quinn, Negar Kiyavash, Todd P. Coleman
We show that under appropriate conditions, it is unique and consistent with another type of graphical model, the directed information graph, which is based on a generalization of Granger causality.