50 citations · 123 across the 36 of their papers we have counts for
37 papers
Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
Yutong Chao, Resat Gökhan, Jalal Etesami +1
We study a nonlinear factor model in which observed responses depend on low-rank latent factors through an unknown monotone link function. This setting is challenging and largely u…
Active Context Selection Improves Simple Regret in Contextual Bandits
Mohammad Shahverdikondori, Jalal Etesami, Negar Kiyavash
We study the contextual multi-armed bandit problem with a finite context space (a.k.a. subpopulations), where the learner recommends a best action for each context and is evaluated…
Convergence of Consensus-Based Particle Methods for Nonconvex Bi-Level Optimization
Yutong Chao, Xudong Sun, Konstantin Riedl +2
In this paper, we study a consensus-based optimization method for nonconvex bi-level optimization, where the objective is to minimize an upper-level function over the set of global…
Optimal Experiments for Partial Causal Effect Identification
Tobias Maringgele, Jalal Etesami
Causal queries are often only partially identifiable from observational data, and experiments that could tighten the resulting bounds are typically costly. We study the problem of…
Last-iterate Convergence of ADMM on Multi-affine Quadratic Equality Constrained Problem
Yutong Chao, Michal Ciebielski, Jalal Etesami +1
In this paper, we study a class of non-convex optimization problems known as multi-affine quadratic equality constrained problems, which appear in various applications--from genera…
Clinically Meaningful Explainability for NeuroAI: An ethical, technical, and clinical perspective
Laura Schopp, Ambra DImperio, Jalal Etesami +1
While explainable AI (XAI) is often heralded as a means to enhance transparency and trustworthiness in closed-loop neurotechnology for psychiatric and neurological conditions, its…
Global Convergence to Nash Equilibrium in Nonconvex General-Sum Games under the -Sided PL Condition
Yutong Chao, Jalal Etesami
We consider the problem of finding a Nash equilibrium (NE) in a general-sum game, where player 's objective is , with denoting…
Consensus-based optimization (CBO): Towards Global Optimality in Robotics
Xudong Sun, Armand Jordana, Massimo Fornasier +2
Zero-order optimization has recently received significant attention for designing optimal trajectories and policies for robotic systems. However, most existing methods (e.g., MPPI,…
Online Mixture of Experts: No-Regret Learning for Optimal Collective Decision-Making
Larkin Liu, Jalal Etesami
We explore the use of expert-guided bandit learning, which we refer to as online mixture-of-experts (OMoE). In this setting, given a context, a candidate committee of experts must…
Graph Learning Is Suboptimal in Causal Bandits
Mohammad Shahverdikondori, Jalal Etesami, Negar Kiyavash
We study regret minimization in causal bandits under causal sufficiency where the underlying causal structure is not known to the agent. Previous work has focused on identifying th…
Recycling History: Efficient Recommendations from Contextual Dueling Bandits
Suryanarayana Sankagiri, Jalal Etesami, Pouria Fatemi +1
The contextual duelling bandit problem models adaptive recommender systems, where the algorithm presents a set of items to the user, and the user's choice reveals their preference.…
Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization
Suryanarayana Sankagiri, Jalal Etesami, Matthias Grossglauser
This paper provides a theoretical analysis of a new learning problem for recommender systems where users provide feedback by comparing pairs of items instead of rating them individ…
Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations
Larkin Liu, Kashif Rasul, Yutong Chao +1
We present a novel framework for online learning in Stackelberg general-sum games, where two agents, the leader and follower, engage in sequential turn-based interactions. At the c…
Fast Proxy Experiment Design for Causal Effect Identification
Sepehr Elahi, Sina Akbari, Jalal Etesami +2
Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (ran…
Parameter identification in linear non-Gaussian causal models under general confounding
Daniele Tramontano, Mathias Drton, Jalal Etesami
Linear non-Gaussian causal models postulate that each random variable is a linear function of parent variables and non-Gaussian exogenous error terms. We study identification of th…
Confounded Budgeted Causal Bandits
Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
We study the problem of learning 'good' interventions in a stochastic environment modeled by its underlying causal graph. Good interventions refer to interventions that maximize re…
Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework
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…
On Identifiability of Conditional Causal Effects
Yaroslav Kivva, Jalal Etesami, Negar Kiyavash
We address the problem of identifiability of an arbitrary conditional causal effect given both the causal graph and a set of any observational and/or interventional distributions o…
Causal Bandits without Graph Learning
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. We…
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Łojasiewicz Inequality
Ilyas Fatkhullin, Jalal Etesami, Niao He +1
We study the complexity of finding the global solution to stochastic nonconvex optimization when the objective function satisfies global Kurdyka-Lojasiewicz (KL) inequality and the…
Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables
Ehsan Mokhtarian, Mohammadsadegh Khorasani, Jalal Etesami +1
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…
Causal Effect Identification in Uncertain Causal Networks
Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian +3
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these…
Revisiting the General Identifiability Problem
Yaroslav Kivva, Ehsan Mokhtarian, Jalal Etesami +1
We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of obs…
Experimental Design for Causal Effect Identification
Sina Akbari, Jalal Etesami, Negar Kiyavash
Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to p…