collaborators

14 papers

cs.RO2026

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,…

stat.ML2026

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…

cs.LG2026

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…

math.OC2026

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…

cs.AI2026

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…

cs.LG2026

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…