11 citations · 19 across the 6 of their papers we have counts for
8 papers
Safe Primal-Dual Optimization with a Single Smooth Constraint
Ilnura Usmanova, Kfir Yehuda Levy
This paper addresses the problem of safe optimization under a single smooth constraint, a scenario that arises in diverse real-world applications such as robotics and autonomous na…
Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints
Bassel Hamoud, Ilnura Usmanova, Kfir Y. Levy
We present the first theoretical guarantees for zero constraint violation in Online Convex Optimization (OCO) across all rounds, addressing dynamic constraint changes. Unlike exist…
Constrained Policy Optimization via Bayesian World Models
Yarden As, Ilnura Usmanova, Sebastian Curi +1
Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications. We propose LAMBDA, a novel model-based a…
Risk-averse Heteroscedastic Bayesian Optimization
Anastasiia Makarova, Ilnura Usmanova, Ilija Bogunovic +1
Many black-box optimization tasks arising in high-stakes applications require risk-averse decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the expect…
Fast Projection Onto Convex Smooth Constraints
Ilnura Usmanova, Maryam Kamgarpour, Andreas Krause +1
The Euclidean projection onto a convex set is an important problem that arises in numerous constrained optimization tasks. Unfortunately, in many cases, computing projections is co…
Log Barriers for Safe Non-convex Black-box Optimization
Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour
We address the problem of minimizing a smooth function over a compact set defined by smooth functional constraints given noisy value m…