activity
20182021
most citedAdapting User Interfaces with Model-based Reinforcement Learning

90 citations · 170 across the 4 of their papers we have counts for

collaborators

6 papers

cs.HC202190 cited

Adapting User Interfaces with Model-based Reinforcement Learning

Kashyap Todi, Gilles Bailly, Luis A. Leiva +1

Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs to…

cs.HC202141 cited

"Can I Touch This?": Survey of Virtual Reality Interactions via Haptic Solutions

Elodie Bouzbib, Gilles Bailly, Sinan Haliyo +1

Haptic feedback has become crucial to enhance the user experiences in Virtual Reality (VR). This justifies the sudden burst of novel haptic solutions proposed these past years in t…

cs.HC202023 cited

CoVR: A Large-Scale Force-Feedback Robotic Interface for Non-Deterministic Scenarios in VR

Elodie Bouzbib, Gilles Bailly, Sinan Haliyo +1

We present CoVR, a novel robotic interface providing strong kinesthetic feedback (100 N) in a room-scale VR arena. It consists of a physical column mounted on a 2D Cartesian ceilin…

cs.HC2019

Glass+Skin: An Empirical Evaluation of the Added Value of Finger Identification to Basic Single-Touch Interaction on Touch Screens

Quentin Roy, Yves Guiard, Gilles Bailly +2

The usability of small devices such as smartphones or interactive watches is often hampered by the limited size of command vocabularies. This paper is an attempt at better understa…

cs.HC201916 cited

SAM: A Modular Framework for Self-Adapting Web Menus

Camille Gobert, Kashyap Todi, Gilles Bailly +1

This paper presents SAM, a modular and extensible JavaScript framework for self-adapting menus on webpages. SAM allows control of two elementary aspects for adapting web menus: (1)…

cs.HC2018

Predicting Human Performance in Vertical Menu Selection Using Deep Learning

Yang Li, Samy Bengio, Gilles Bailly

Predicting human performance in interaction tasks allows designers or developers to understand the expected performance of a target interface without actually testing it with real…