activity
20242026
collaborators

9 papers

math.OC2026

Rate-Optimal Regret for the Safe Learning-based Control of the Constrained Linear Quadratic Regulator

Spencer Hutchinson, Nanfei Jiang, Mahnoosh Alizadeh

We study the problem of adaptive control of the stochastic linear quadratic regulator (LQR) with constraints that must be satisfied at every time step. Prior work on the multidimen…

math.OC2026

Steady-state Based Approach to Online Non-stochastic Control

Vijeth Hebbar, Spencer Hutchinson, Mahnoosh Alizadeh +1

We study the problem of online non-stochastic control (ONC), which is the control of a linear system under adversarial disturbances and adversarial cost functions, with the aim of…

cs.LG2025

Constrained Online Convex Optimization with Polyak Feasibility Steps

Spencer Hutchinson, Mahnoosh Alizadeh

In this work, we study online convex optimization with a fixed constraint function . Prior work on this problem has shown reg…

math.OC2025

The Safety-Privacy Tradeoff in Linear Bandits

Arghavan Zibaie, Spencer Hutchinson, Ramtin Pedarsani +1

We consider a collection of linear stochastic bandit problems, each modeling the random response of different agents to proposed interventions, coupled together by a global safety…

math.OC2025

Online Nonstochastic Control with Convex Safety Constraints

Nanfei Jiang, Spencer Hutchinson, Mahnoosh Alizadeh

This paper considers the online nonstochastic control problem of a linear time-invariant system under convex state and input constraints that need to be satisfied at all times. We…

cs.LG2024

Optimistic Safety for Online Convex Optimization with Unknown Linear Constraints

Spencer Hutchinson, Tianyi Chen, Mahnoosh Alizadeh

We study the problem of online convex optimization (OCO) under unknown linear constraints that are either static, or stochastically time-varying. For this problem, we introduce an…