Publications de Ilija Bogunovic

Overlapping Multi-Bandit Best Arm Identification

Volkan Cevher, Ilija Bogunovic, Jonathan Mark Scarlett

In the multi-armed bandit literature, the multi-bandit best-arm identification problem consists of determining each best arm in a number of disjoint groups of arms, with as few total arm pulls as possible. In this paper, we introduce a variant of the multi ...

IEEE2019

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

Ilija Bogunovic

The central task in many interactive machine learning systems can be formalized as the sequential optimization of a black-box function. Bayesian optimization (BO) is a powerful model-based framework for \emph{adaptive} experimentation, where the primary go ...

EPFL2019

High Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

Volkan Cevher, Ilija Bogunovic, Paul Thierry Yves Rolland, Jonathan Mark Scarlett

Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring, and more. One of the most important challenges in BO is the development of a ...

2018

Adversarially Robust Optimization with Gaussian Processes

Volkan Cevher, Ilija Bogunovic, Jonathan Mark Scarlett

In this paper, we consider the problem of Gaussian process (GP) optimization with an added robustness requirement: The returned point may be perturbed by an adversary, and we require the function value to remain as high as possible even after this perturba ...

2018

Adversarially Robust Optimization with Gaussian Processes

Volkan Cevher, Ilija Bogunovic, Jonathan Mark Scarlett

In this paper, we consider the problem of Gaussian process (GP) optimization with an added robustness requirement: The returned point may be perturbed by an adversary, and we require the function value to remain as high as possible even after this perturba ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Robust Maximization of Non-Submodular Objectives

Volkan Cevher, Ilija Bogunovic, Junyao Zhao

We study the problem of maximizing a monotone set function subject to a cardinality constraint k in the setting where some number of elements is deleted from the returned set. The focus of this work is on the worst-case adversarial setting. While there exi ...

2018

Learning-based subsampling

Volkan Cevher, Ilija Bogunovic, Baran Gözcü, Yen-Huan Li, Luca Baldassarre, Jonathan Mark Scarlett

The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, represe ...

2017

A Distributed Algorithm for Partitioned Robust Submodular Maximization

Volkan Cevher, Ilija Bogunovic, Slobodan Mitrovic, Jonathan Mark Scarlett

In this paper, we consider the problem of maximizing a monotone submodular function subject to a cardinality constraint, with two added twists: The computation is distributed across a number of machines, and we require the solution to be robust against adv ...

2017

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Volkan Cevher, Ilija Bogunovic, Jonathan Mark Scarlett

In this paper, we consider the problem of sequentially optimizing a black-box function

f

based on noisy samples and bandit feedback. We assume that

f

is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yield ...

2017

Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach

Volkan Cevher, Ilija Bogunovic, Slobodan Mitrovic, Ashkan Norouzi Fard, Jakub Tarnawski

We study the classical problem of maximizing a monotone submodular function subject to a cardinality constraint k, with two additional twists: (i) elements arrive in a streaming fashion and (ii) m items from the algorithm’s memory might be removed after th ...

2017

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

Volkan Cevher, Ilija Bogunovic, Slobodan Mitrovic, Jonathan Mark Scarlett

We study the problem of maximizing a monotone submodular function subject to a cardinality constraint

k

, with the added twist that a number of items

\tau

from the returned set may be removed. We focus on the worst-case setting considered in Orlin et al ...

2017

An Efficient Streaming Algorithm for the Submodular Cover Problem

Volkan Cevher, Ilija Bogunovic, Marwa El Halabi, Abbas Bazzi, Ashkan Norouzi Fard, Ya-Ping Hsieh

We initiate the study of the classical Submodular Cover (SC) problem in the data streaming model which we refer to as the Streaming Submodular Cover (SSC). We show that any single pass streaming algorithm using sublinear memory in the size of the stream wi ...

2016

Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation

Volkan Cevher, Ilija Bogunovic, Jonathan Mark Scarlett, Andreas Krause

We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set ...

2016

Time-Varying Gaussian Process Bandit Optimization

Volkan Cevher, Ilija Bogunovic, Jonathan Mark Scarlett

We consider the sequential Bayesian optimization problem with bandit feedback, adopting a formulation that allows for the reward function to vary with time. We model the reward function using a Gaussian process whose evolution obeys a simple Markov model. ...

2016

Active Learning of Self-concordant like Multi-index Functions

Volkan Cevher, Ilija Bogunovic, Jonathan Mark Scarlett

We study the problem of actively learning a multi-index function of the form f (x) = g_0 (A_0 x) from its point evaluations, where A_0 ∈ R_{k×d} with k ≪ d. We build on the assumptions and techniques of an existing approach based on low-rank matrix recover ...

2015