1 citations · 1 across the 26 of their papers we have counts for
31 papers
Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders
Sadegh Khorasani, Ali Najar, Saber Salehkaleybar +1
We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unknown number of exogenous latent…
Sharp First-Order Lower Bounds under -Polyak-Lojasiewicz Conditions
Saeed Masiha, Negar Kiyavash, Patrick Thiran
We study first-order oracle complexity under the -Polyak-Lojasiewicz condition for . For , we first show that global -sm…
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…
Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
Saeed Masiha, Zebang Shen, Negar Kiyavash +1
We study optimistic bilevel optimization when the lower-level problem has a non-isolated manifold of minimizers. In this setting, the hyper-objective may be non-differentiable beca…
Inference Time Causal Probing in LLMs
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash +1
Causal probing methods aim to test and control how internal representations influence the behavior of generative models. In causal probing, an intervention modifies hidden states s…
Neighborhood-Aware Graph Labeling Problem
Mohammad Shahverdikondori, Sepehr Elahi, Patrick Thiran +1
Motivated by optimization oracles in bandits with network interference, we study the Neighborhood-Aware Graph Labeling (NAGL) problem. Given a graph , a label set of siz…
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…
Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
Ehsan Sharifian, Saber Salehkaleybar, Negar Kiyavash
We study the problem of causal structure learning from a combination of observational and interventional data generated by a linear non-Gaussian structural equation model that migh…
Fusing Rewards and Preferences in Reinforcement Learning
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash +1
We present Dual-Feedback Actor (DFA), a reinforcement learning algorithm that fuses both individual rewards and pairwise preferences (if available) into a single update rule. DFA u…
Hierarchical Reinforcement Learning with Targeted Causal Interventions
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash +1
Hierarchical reinforcement learning (HRL) improves the efficiency of long-horizon reinforcement-learning tasks with sparse rewards by decomposing the task into a hierarchy of subgo…
Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
Yaroslav Kivva, Sina Akbari, Saber Salehkaleybar +1
We investigate the estimation of the causal effect of a treatment variable on an outcome in the presence of a latent confounder. We first show that the causal effect is identifiabl…
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar +2
This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups…
Best Group Identification in Multi-Objective Bandits
Mohammad Shahverdikondori, Mohammad Reza Badri, Negar Kiyavash
We introduce the Best Group Identification problem in a multi-objective multi-armed bandit setting, where an agent interacts with groups of arms with vector-valued rewards. The per…
Superquantile-Gibbs Relaxation for Minima-selection in Bilevel Optimization
Saeed Masiha, Zebang Shen, Negar Kiyavash +1
Bilevel optimization (BLO) becomes more challenging when the lower-level objective admits multiple minimizers. Compared with the commonly studied unique-minimizer setting, this int…
Multi-Domain Causal Discovery in Bijective Causal Models
Kasra Jalaldoust, Saber Salehkaleybar, Negar Kiyavash
We consider the problem of causal discovery (a.k.a., causal structure learning) in a multi-domain setting. We assume that the causal functions are invariant across the domains, whi…
Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding
Fateme Jamshidi, Sina Akbari, Negar Kiyavash
We study the problem of closeness testing for continuous distributions and its implications for causal discovery. Specifically, we analyze the sample complexity of distinguishing w…
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference
Fateme Jamshidi, Mohammad Shahverdikondori, Negar Kiyavash
We study multi-armed bandits under network interference, where each unit's reward depends on its own treatment and those of its neighbors in a given graph. This induces an exponent…
Pure Exploration Beyond Reward Feedback: The Role of Post-Action Context
Mohammad Shahverdikondori, Amir Mohammad Abouei, Alireza Rezaeimoghadam +1
We introduce the problem of best arm identification (BAI) with post-action context, a new BAI problem in a stochastic multi-armed bandit environment and the fixed-confidence settin…
Multi-armed Bandits with Missing Outcome
Ilia Mahrooghi, Mahshad Moradi, Sina Akbari +1
While significant progress has been made in designing algorithms that minimize regret in online decision-making, real-world scenarios often introduce additional complexities, perha…
QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
Mohammad Shahverdikondori, Ehsan Mokhtarian, Negar Kiyavash
Causal discovery is essential for understanding relationships among variables of interest in many scientific domains. In this paper, we focus on permutation-based methods for learn…
Optimal Local Convergence Rates of Stochastic First-Order Methods under Local -PL
Saeed Masiha, Saber Salehkaleybar, Niao He +2
We study the local convergence rate of stochastic first-order methods under a local -Polyak-Lojasiewicz (-PL) condition in a neighborhood of a target connected component $\ma…
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…
Causal Effect Identification in LiNGAM Models with Latent Confounders
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar +2
We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the…
Causal Effect Identification in a Sub-Population with Latent Variables
Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash +1
The s-ID problem seeks to compute a causal effect in a specific sub-population from the observational data pertaining to the same sub population (Abouei et al., 2023). This problem…