Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Usmanova, Ilnura"'
Optimizing noisy functions online, when evaluating the objective requires experiments on a deployed system, is a crucial task arising in manufacturing, robotics and many others. Often, constraints on safe inputs are unknown ahead of time, and we only
Externí odkaz:
http://arxiv.org/abs/2207.10415
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 approach for policy optimization in safety critical tasks modeled via c
Externí odkaz:
http://arxiv.org/abs/2201.09802
Publikováno v:
Advances in Neural Information Processing Systems, 2021
Many black-box optimization tasks arising in high-stakes applications require risk-averse decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the expected value only. We generalize BO to trade mean and input-dependent vari
Externí odkaz:
http://arxiv.org/abs/2111.03637
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 computationally demanding. In this work, we focus on projection problems
Externí odkaz:
http://arxiv.org/abs/2109.09835
We address the problem of minimizing a smooth function $f^0(x)$ over a compact set $D$ defined by smooth functional constraints $f^i(x)\leq 0,~ i = 1,\ldots, m$ given noisy value measurements of $f^i(x)$. This problem arises in safety-critical applic
Externí odkaz:
http://arxiv.org/abs/1912.09478
For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points. We propose an approach based on log barriers to find a local solution of a non-convex non-
Externí odkaz:
http://arxiv.org/abs/1912.09466
We address the problem of minimizing a convex smooth function $f(x)$ over a compact polyhedral set $D$ given a stochastic zeroth-order constraint feedback model. This problem arises in safety-critical machine learning applications, such as personaliz
Externí odkaz:
http://arxiv.org/abs/1903.04626
Autor:
Gasnikov, Alexander, Krymova, Ekaterina, Lagunovskaya, Anastasia, Usmanova, Ilnura, Fedorenko, Fedor
In the paper we consider one point and two point multiarmed bamdit problems. In other words we consider the online stochastic convex optimization problems with oracle that return the value (realization) of the function at one point or at two points.
Externí odkaz:
http://arxiv.org/abs/1509.01679
We show how one can obtain nonaccelerated randomized coordinate descent method (Yu. Nesterov, 2010) and nonaccelerated method of randomization of sum-type functional (Le Roux-Schmidt-Bach, 2012) from the optimal method for the stochastic optimization
Externí odkaz:
http://arxiv.org/abs/1508.02182
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