Publications related to Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

A Catalyst Framework for Minimax Optimization

We introduce a generic two-loop scheme for smooth minimax optimization with strongly-convex-concave objectives. Our approach applies the accelerated proximal point framework (or Catalyst) to the associated dual problem and takes full advantage of existing ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2020

Monogenic Filtering Based Automatic Defect Detection from a Single Fringe Pattern

Pramod Rastogi, Rishikesh Dilip Kulkarni

We propose a fringe analysis algorithm for the automatic detection of defects from a single fringe pattern (FP). Typically, the surface defects exhibit high fringe density areas in the FP. Consequently, high fringe density regions can be utilized as a sign ...

Springer, Cham2019

The (h, k)-Server Problem on Bounded Depth Trees

Marek Elias

We study the k-server problem in the resource augmentation setting, i.e., when the performance of the online algorithm with k servers is compared to the offline optimal solution with h

ASSOC COMPUTING MACHINERY2019

Delay-Power-Rate-Distortion Optimization of Video Representations for Dynamic Adaptive Streaming

Pascal Frossard, Laura Toni, Chenglin Li

Dynamic adaptive streaming addresses user heterogeneity by providing multiple encoded representations at different rates and/or resolutions for the same video content. For delay-sensitive applications, such as live streaming, there is however a stringent r ...

IEEE2018

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Steven Adriaan Roelofsen

The field of Unmanned Aerial Vehicles (UAVs), also known as drones, is rapidly growing, both in terms of size and of number of applications. Civil applications range from mapping, inspection, search and rescue, taking aerial footage, to art show, entertain ...

EPFL2017

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

A Comment on “Computational Complexity of Stochastic Programming Problems”

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Mathematical Programming A, 106(3):423–432, 200 ...

Springer Heidelberg2016

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

We propose to approximate two-stage distributionally robust programs with binary recourse decisions by their associated K-adaptability problems, which pre-select K candidate second-stage policies here-and-now and implement the best of these policies once t ...

Elsevier Science Bv2016

Collision-Free Pattern Formation

Rachid Guerraoui, Alexandre David Olivier Maurer

Shoals of small fishes can change their collective shape and form a specific pattern. They do so efficiently (in parallel) and without collision. In this paper, we study the analog problem of distributed pattern formation. A set of processes needs to move ...

2016

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

Aurélien Gallice, Sebastiano Piccolroaz, Marco Toffolon, Elisa Calamita

River water temperature is a key physical variable controlling several chemical, biological and ecological processes. Its reliable prediction is a main issue in many environmental applications, which however is hampered by data scarcity, when using data-de ...

Wiley-Blackwell2016

Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

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A Catalyst Framework for Minimax Optimization

Monogenic Filtering Based Automatic Defect Detection from a Single Fringe Pattern

The (h, k)-Server Problem on Bounded Depth Trees

Delay-Power-Rate-Distortion Optimization of Video Representations for Dynamic Adaptive Streaming

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

A Comment on “Computational Complexity of Stochastic Programming Problems”

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

Collision-Free Pattern Formation

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

A Catalyst Framework for Minimax Optimization

Delay-Power-Rate-Distortion Optimization of Video Representations for Dynamic Adaptive Streaming

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

A Comment on “Computational Complexity of Stochastic Programming Problems”

The (h, k)-Server Problem on Bounded Depth Trees

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Monogenic Filtering Based Automatic Defect Detection from a Single Fringe Pattern

Collision-Free Pattern Formation

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals