Demo Data from EDBT 2026 / reviewers in the wild / expert
Marlon Rodríguez
dblp:423/4957
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0001-6698-2834ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Language models and text generation · 44% Learning theory · 44% Efficient and distributed learning · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › classification › multiclass classification
extreme classification |
1.0 | 1 | 2026 | Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework · AAAI 2026 |
Natural language and speech › Language models and text generation
large language model |
1.0 | 1 | 2026 | Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework · AAAI 2026 |
Machine learning › Efficient and distributed learning › large-scale learning
model scaling |
0.3 | 1 | 2026 | Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
multimodal learning · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework
Diego Ortego, Marlon Rodríguez, Mario Almagro-Cádiz, Kunal Dahiya, David Jiménez, Juan C. SanMiguel |
AAAI | 2 |
| 2026 | Multimodal Haptics for Realistic Texture Rendering in Extended RealityabstractRealistic texture perception in Extended Reality (XR) requires tactile feedback that simultaneously addresses the fundamental perceptual dimensions of hardness, roughness, and warmth. However, current haptic solutions often rely solely on vibrotactile feedback, which alone cannot convey the full spectrum of tactile sensations. This paper presents a multimodal texture rendering method that combines three distinct haptic modalities—force, vibration, and temperature—to provide all required perceptual dimensions. We developed a multimodal fingertip haptic display to deliver these stimuli, and evaluated its performance through perceptual user studies within a Virtual Reality (VR) experimental framework. This framework assesses the modalities, both in isolation and in combination, to examine the robustness of the mappings between stimuli and their corresponding dimensions. Furthermore, we demonstrate our multimodal method by rendering real-world materials such as wood, metal, and water. A subjective evaluation with 48 participants indicates that users can reliably identify most perceptual dimensions, and that the integration of more modalities consistently improved texture realism compared to representations with fewer. These findings validate the effectiveness of our multimodal texture rendering method and advance the design of immersive, texture-rich XR experiences. Vivian Tsang, Jonathan Valgaeren, Marlon Rodríguez, Carlos Rodriguez Guerrero, Maria Torres Vega |
IMX | 3 |