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ORCIDRaúl Santos-Rodríguez
Raúl Santos-Rodriguez
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- affiliation: University of Bristol
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2020 – today
- 2023
[j18]Katarzyna Stawarz
, Dmitri S. Katz, Amid Ayobi, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter A. Flach, Aisling Ann O'Kane:
Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making. Int. J. Hum. Comput. Stud. 173: 103003 (2023)- [c52]Jonathan D. Thomas, Raúl Santos-Rodríguez, Mihai Anca, Robert J. Piechocki:
Multi-lingual agents through multi-headed neural networks. NLDL 2023 - [i48]Enrico Werner, Jeffrey N. Clark, Ranjeet S. Bhamber, Michael Ambler, Christopher P. Bourdeaux, Alexander Hepburn, Christopher J. McWilliams, Raúl Santos-Rodríguez:
Identification, explanation and clinical evaluation of hospital patient subtypes. CoRR abs/2301.08019 (2023) - [i47]Amarpal Sahota, Amber Roguski, Matthew W. Jones, Michal Rolinski, Alan L. Whone, Raúl Santos-Rodriguez, Zahraa S. Abdallah:
A Time Series Approach to Parkinson's Disease Classification from EEG. CoRR abs/2301.09568 (2023) - [i46]Maha M. Alwuthaynani, Zahraa S. Abdallah, Raúl Santos-Rodríguez:
Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease. CoRR abs/2301.13504 (2023) - [i45]Taku Yamagata, Emma L. Tonkin, Benjamin Arana Sanchez, Ian Craddock, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Weisong Yang, Peter A. Flach:
When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations. CoRR abs/2302.02706 (2023) - [i44]Nawid Keshtmand, Raúl Santos-Rodríguez, Jonathan Lawry:
Two-step counterfactual generation for OOD examples. CoRR abs/2302.05196 (2023) - [i43]Alexander Hepburn, Valero Laparra, Raúl Santos-Rodríguez, Jesús Malo:
Disentangling the Link Between Image Statistics and Human Perception. CoRR abs/2303.09874 (2023) - [i42]Tashi Namgyal, Alexander Hepburn, Raúl Santos-Rodríguez, Valero Laparra, Jesus Malo:
What You Hear Is What You See: Audio Quality Metrics From Image Quality Metrics. CoRR abs/2305.11582 (2023) - [i41]Tashi Namgyal, Peter A. Flach, Raúl Santos-Rodríguez:
MIDI-Draw: Sketching to Control Melody Generation. CoRR abs/2305.11605 (2023) - [i40]Alex Iacob, Pedro Porto Buarque de Gusmão, Nicholas D. Lane, Armand K. Koupai, Mohammud Junaid Bocus, Raúl Santos-Rodríguez, Robert J. Piechocki, Ryan McConville:
Privacy in Multimodal Federated Human Activity Recognition. CoRR abs/2305.12134 (2023) - 2022
- [j17]Xiaoyang Wang, Jonathan D. Thomas, Robert J. Piechocki, Shipra Kapoor, Raúl Santos-Rodríguez, Arjun Parekh:
Self-play learning strategies for resource assignment in Open-RAN networks. Comput. Networks 206: 108682 (2022) - [j16]Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency. Softw. Impacts 14: 100406 (2022) - [c51]Ricardo Kleinlein, Alexander Hepburn, Raúl Santos-Rodríguez, Fernando Fernández Martínez:
Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers. BMVC 2022: 1083 - [c50]Mohammud Junaid Bocus, Hok-Shing Lau, Ryan McConville, Robert J. Piechocki, Raúl Santos-Rodríguez:
Self-Supervised WiFi-Based Activity Recognition. GLOBECOM (Workshops) 2022: 552-557 - [c49]Taku Yamagata, Raúl Santos-Rodríguez, Robert J. Piechocki, Peter A. Flach:
Understanding Reinforcement Learning Based Localisation as a Probabilistic Inference Algorithm. ICANN (2) 2022: 111-122 - [c48]Jonas Paulavicius, Seifallah Jardak, Ryan McConville, Robert J. Piechocki, Raúl Santos-Rodríguez:
Temporal Self-Supervised Learning for RSSI-based Indoor Localization. ICC 2022: 3046-3051 - [c47]Alexander Hepburn, Valero Laparra, Raúl Santos-Rodríguez, Johannes Ballé, Jesus Malo:
On the relation between statistical learning and perceptual distances. ICLR 2022 - [c46]Jonas Schulz, Raúl Santos-Rodríguez, Rafael Poyiadzi:
Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach. NLDL 2022 - [c45]Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesús Cid-Sueiro, Miquel Perelló-Nieto, Peter A. Flach, Raúl Santos-Rodríguez:
The Weak Supervision Landscape. PerCom Workshops 2022: 218-223 - [c44]Rafael Poyiadzi, Weisong Yang, Niall Twomey, Raúl Santos-Rodríguez:
Hypothesis Testing for Class-Conditional Label Noise. ECML/PKDD (3) 2022: 171-186 - [p1]Weisong Yang, Rafael Poyiadzi, Yoav Ben-Shlomo, Ian Craddock, Liz Coulthard, Raúl Santos-Rodríguez, James Selwood, Niall Twomey:
Detecting and Monitoring Behavioural Patterns in Individuals with Cognitive Disorders in the Home Environment with Partial Annotations. Integrating Artificial Intelligence and IoT for Advanced Health Informatics 2022: 25-52 - [i39]Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesús Cid-Sueiro, Miquel Perelló-Nieto, Peter A. Flach, Raúl Santos-Rodríguez:
The Weak Supervision Landscape. CoRR abs/2203.16282 (2022) - [i38]Ricardo Kleinlein, Alexander Hepburn, Raúl Santos-Rodríguez, Fernando Fernández Martínez:
Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers. CoRR abs/2208.03961 (2022) - [i37]Armand K. Koupai, Mohammud Junaid Bocus, Raúl Santos-Rodríguez, Robert J. Piechocki, Ryan McConville:
Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition. CoRR abs/2209.03765 (2022) - [i36]Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. CoRR abs/2209.03805 (2022) - [i35]Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach:
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. CoRR abs/2209.03813 (2022) - [i34]Taku Yamagata, Ahmed Khalil, Raúl Santos-Rodríguez:
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL. CoRR abs/2209.03993 (2022) - [i33]Nawid Keshtmand, Raúl Santos-Rodríguez, Jonathan Lawry:
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection. CoRR abs/2211.03183 (2022) - 2021
- [j15]Ryan McConville, Gareth Archer, Ian Craddock, Michal Kozlowski, Robert J. Piechocki, James Pope, Raúl Santos-Rodríguez:
Vesta: A digital health analytics platform for a smart home in a box. Future Gener. Comput. Syst. 114: 106-119 (2021) - [j14]Bo Kang, Dario García-García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
Conditional t-SNE: more informative t-SNE embeddings. Mach. Learn. 110(10): 2905-2940 (2021) - [j13]Amid Ayobi, Katarzyna Stawarz, Dmitri S. Katz, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter A. Flach, Aisling Ann O'Kane:
Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies. Proc. ACM Hum. Comput. Interact. 5(CSCW2): 457:1-457:26 (2021) - [j12]Haixia Bi, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Peter A. Flach:
Human Activity Recognition Based on Dynamic Active Learning. IEEE J. Biomed. Health Informatics 25(4): 922-934 (2021) - [c43]Bo Kang, Dario García-García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
Conditional t-SNE: More informative t-SNE embeddings. DSAA 2021: 1-2 - [c42]Daniel Bacaicoa-Barber, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Jesús Cid-Sueiro:
On the Selection of Loss Functions Under Known Weak Label Models. ICANN (2) 2021: 332-343 - [c41]Alexander Hepburn, Raúl Santos-Rodríguez:
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions Through Perception. ICIP 2021: 3717-3721 - [c40]Amid Ayobi, Katarzyna Stawarz, Dmitri S. Katz, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter A. Flach, Aisling Ann O'Kane:
Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine Learning. IUI Workshops 2021 - [c39]Raúl Santos-Rodriguez:
Keynote: Training with imperfect and weak labels. PerCom Workshops 2021: 481 - [i32]Alexander Hepburn, Raúl Santos-Rodríguez:
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception. CoRR abs/2102.10951 (2021) - [i31]Rafael Poyiadzi, Weisong Yang, Niall Twomey, Raúl Santos-Rodríguez:
Statistical Hypothesis Testing for Class-Conditional Label Noise. CoRR abs/2103.02630 (2021) - [i30]Xiaoyang Wang, Jonathan D. Thomas, Robert J. Piechocki, Shipra Kapoor, Raúl Santos-Rodríguez, Arjun Parekh:
Self-play Learning Strategies for Resource Assignment in Open-RAN Networks. CoRR abs/2103.02649 (2021) - [i29]Hok-Shing Lau, Ryan McConville, Mohammud Junaid Bocus, Robert J. Piechocki, Raúl Santos-Rodríguez:
Self-Supervised WiFi-Based Activity Recognition. CoRR abs/2104.09072 (2021) - [i28]Alexander Hepburn, Valero Laparra, Raúl Santos-Rodríguez, Johannes Ballé, Jesús Malo:
On the relation between statistical learning and perceptual distances. CoRR abs/2106.04427 (2021) - [i27]Rafael Poyiadzi, Xavier Renard, Thibault Laugel, Raúl Santos-Rodríguez, Marcin Detyniecki:
On the overlooked issue of defining explanation objectives for local-surrogate explainers. CoRR abs/2106.05810 (2021) - [i26]Rafael Poyiadzi, Xavier Renard, Thibault Laugel, Raúl Santos-Rodríguez, Marcin Detyniecki:
Understanding surrogate explanations: the interplay between complexity, fidelity and coverage. CoRR abs/2107.04309 (2021) - [i25]Taku Yamagata, Ryan McConville, Raúl Santos-Rodríguez:
Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills. CoRR abs/2111.08596 (2021) - [i24]Jonas Schulz, Rafael Poyiadzi, Raúl Santos-Rodríguez:
Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach. CoRR abs/2111.09121 (2021) - [i23]Jonathan D. Thomas, Raúl Santos-Rodríguez, Robert J. Piechocki, Mihai Anca:
Multi-lingual agents through multi-headed neural networks. CoRR abs/2111.11129 (2021) - [i22]Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach:
Classifier Calibration: How to assess and improve predicted class probabilities: a survey. CoRR abs/2112.10327 (2021) - 2020
- [j11]Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Dario García-García, Jesús Cid-Sueiro:
Recycling weak labels for multiclass classification. Neurocomputing 400: 206-215 (2020) - [j10]Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. J. Open Source Softw. 5(49): 1904 (2020) - [c38]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodríguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. AIES 2020: 344-350 - [c37]Rafael Poyiadzi, Weisong Yang, Yoav Ben-Shlomo, Ian Craddock, Liz Coulthard, Raúl Santos-Rodríguez, James Selwood, Niall Twomey:
Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data. AAI4H@ECAI 2020: 23-27 - [c36]Taku Yamagata, Aisling Ann O'Kane, Amid Ayobi, Dmitri S. Katz, Katarzyna Stawarz, Paul Marshall, Peter A. Flach, Raúl Santos-Rodríguez:
Model-Based Reinforcement Learning for Type 1 Diabetes Blood Glucose Control. AAI4H@ECAI 2020: 72-77 - [c35]Niall Twomey, Michal Kozlowski, Raúl Santos-Rodríguez:
Neural ODEs with Stochastic Vector Field Mixtures. ECAI 2020: 1555-1562 - [c34]Mohammud Junaid Bocus, Jonas Paulavicius, Ryan McConville, Raúl Santos-Rodríguez, Robert J. Piechocki:
Low Cost Localisation in Residential Environments using High Resolution CIR Information. GLOBECOM 2020: 1-6 - [c33]Alexander Hepburn, Valero Laparra, Jesús Malo, Ryan McConville, Raúl Santos-Rodríguez:
Perceptnet: A Human Visual System Inspired Neural Network For Estimating Perceptual Distance. ICIP 2020: 121-125 - [c32]Ryan McConville, Raúl Santos-Rodríguez, Robert J. Piechocki, Ian Craddock:
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding. ICPR 2020: 5145-5152 - [c31]Mohammud Junaid Bocus, Wenda Li, Jonas Paulavicius, Ryan McConville, Raúl Santos-Rodríguez, Kevin Chetty, Robert J. Piechocki:
Translation Resilient Opportunistic WiFi Sensing. ICPR 2020: 5627-5633 - [c30]Haixia Bi, Raúl Santos-Rodríguez, Peter A. Flach:
Polsar Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field. IGARSS 2020: 708-711 - [c29]Alexander Hepburn, Valero Laparra, Ryan McConville, Raúl Santos-Rodríguez:
Enforcing perceptual consistency on Generative Adversarial Networks by using the Normalised Laplacian Pyramid Distance. NLDL 2020: 1-6 - [i21]Rafael Poyiadzi, Weisong Yang, Yoav Ben-Shlomo, Ian Craddock, Liz Coulthard, Raúl Santos-Rodríguez, James Selwood, Niall Twomey:
Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data. CoRR abs/2007.03615 (2020) - [i20]Valero Laparra, Juan Emmanuel Johnson, Gustau Camps-Valls, Raúl Santos-Rodríguez, Jesus Malo:
Information Theory Measures via Multidimensional Gaussianization. CoRR abs/2010.03807 (2020) - [i19]Taku Yamagata, Aisling Ann O'Kane, Amid Ayobi, Dmitri S. Katz, Katarzyna Stawarz, Paul Marshall, Peter A. Flach, Raúl Santos-Rodríguez:
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control. CoRR abs/2010.06266 (2020) - [i18]Juan Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls, Raúl Santos-Rodríguez, Jesús Malo:
Information Theory in Density Destructors. CoRR abs/2012.01012 (2020)
2010 – 2019
- 2019
- [j9]Michal Kozlowski, Raúl Santos-Rodríguez, Robert J. Piechocki:
Sensor Modalities and Fusion for Robust Indoor Localisation. EAI Endorsed Trans. Ambient Syst. 6(18): e5 (2019) - [j8]Pablo Morales-Alvarez, Pablo Ruiz, Raúl Santos-Rodríguez, Rafael Molina, Aggelos K. Katsaggelos:
Scalable and efficient learning from crowds with Gaussian processes. Inf. Fusion 52: 110-127 (2019) - [c28]Xiaoyang Wang, Ioannis Mavromatis, Andrea Tassi, Raúl Santos-Rodríguez, Robert J. Piechocki:
Location Anomalies Detection for Connected and Autonomous Vehicles. CAVS 2019: 1-5 - [c27]Rafael Poyiadzi, Raúl Santos-Rodríguez, Niall Twomey:
Active Learning with Label Proportions. ICASSP 2019: 3097-3101 - [i17]Niall Twomey, Michal Kozlowski, Raúl Santos-Rodríguez:
Neural ODEs with stochastic vector field mixtures. CoRR abs/1905.09905 (2019) - [i16]Bo Kang, Dario García-García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information. CoRR abs/1905.10086 (2019) - [i15]Niall Twomey, Rafael Poyiadzi, Callum Mann, Raúl Santos-Rodríguez:
Ordinal Regression as Structured Classification. CoRR abs/1905.13658 (2019) - [i14]Xiaoyang Wang, Ioannis Mavromatis, Andrea Tassi, Raúl Santos-Rodríguez, Robert J. Piechocki:
Location Anomalies Detection for Connected and Autonomous Vehicles. CoRR abs/1907.00811 (2019) - [i13]Alexander Hepburn, Valero Laparra, Ryan McConville, Raúl Santos-Rodríguez:
Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance. CoRR abs/1908.04347 (2019) - [i12]Ryan McConville, Raúl Santos-Rodríguez, Robert J. Piechocki, Ian Craddock:
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding. CoRR abs/1908.05968 (2019) - [i11]Taku Yamagata, Raúl Santos-Rodríguez, Ryan McConville, Atis Elsts:
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback. CoRR abs/1908.06134 (2019) - [i10]Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency. CoRR abs/1909.05167 (2019) - [i9]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodriguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. CoRR abs/1909.09369 (2019) - [i8]Alexander Hepburn, Valero Laparra, Jesús Malo, Ryan McConville, Raúl Santos-Rodríguez:
PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance. CoRR abs/1910.12548 (2019) - [i7]Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach:
bLIMEy: Surrogate Prediction Explanations Beyond LIME. CoRR abs/1910.13016 (2019) - 2018
- [j7]Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
SICA: subjectively interesting component analysis. Data Min. Knowl. Discov. 32(4): 949-987 (2018) - [j6]Luis Gómez-Chova, Raúl Santos-Rodríguez, Gustau Camps-Valls:
Signal-to-noise ratio in reproducing kernel Hilbert spaces. Pattern Recognit. Lett. 112: 75-82 (2018) - [c26]Ryan McConville, Raúl Santos-Rodríguez, Niall Twomey:
Person Identification and Discovery With Wrist Worn Accelerometer Data. ESANN 2018 - [c25]Raúl Santos-Rodríguez, Niall Twomey:
Efficient approximate representations for computationally expensive features. ESANN 2018 - [c24]Atis Elsts, Ryan McConville, Xenofon Fafoutis, Niall Twomey, Robert J. Piechocki, Raúl Santos-Rodriguez, Ian Craddock:
On-Board Feature Extraction from Acceleration Data for Activity Recognition. EWSN 2018: 163-168 - [c23]Rafael Poyiadzi, Raúl Santos-Rodríguez, Niall Twomey:
Label Propagation for Learning with Label proportions. MLSP 2018: 1-6 - [c22]Alexander Hepburn, Ryan McConville, Raúl Santos-Rodríguez, Jesús Cid-Sueiro, Dario García-García:
Proper Losses for Learning with Example-Dependent Costs. LIDTA@ECML/PKDD 2018: 52-66 - [c21]Rafael Poyiadzi, Raúl Santos-Rodríguez, Tijl De Bie:
Ordinal Label Proportions. ECML/PKDD (1) 2018: 306-321 - [c20]Michal Kozlowski, Dallan Byrne, Raúl Santos-Rodríguez, Robert J. Piechocki:
Data fusion for robust indoor localisation in digital health. WCNC Workshops 2018: 302-307 - [c19]Ryan McConville, Dallan Byrne, Ian Craddock, Robert J. Piechocki, James Pope, Raúl Santos-Rodríguez:
Understanding the quality of calibrations for indoor localisation. WF-IoT 2018: 676-681 - [i6]Ryan McConville, Gareth Archer, Ian Craddock, Herman J. ter Horst, Robert J. Piechocki, James Pope, Raúl Santos-Rodriguez:
Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable. CoRR abs/1807.04667 (2018) - [i5]Rafael Poyiadzi, Raúl Santos-Rodríguez, Niall Twomey:
Label Propagation for Learning with Label Proportions. CoRR abs/1810.10328 (2018) - [i4]Michal Kozlowski, Ryan McConville, Raúl Santos-Rodríguez, Robert J. Piechocki:
Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks. CoRR abs/1812.02538 (2018) - 2017
- [c18]Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Jesús Cid-Sueiro:
Adapting Supervised Classification Algorithms to Arbitrary Weak Label Scenarios. IDA 2017: 247-259 - [c17]Paolo Simeone, Raúl Santos-Rodríguez, Matt McVicar, Jefrey Lijffijt, Tijl De Bie:
Hierarchical Novelty Detection. IDA 2017: 310-321 - [c16]James Pope, Ryan McConville, Michal Kozlowski, Xenofon Fafoutis, Raúl Santos-Rodríguez, Robert J. Piechocki, Ian Craddock:
SPHERE in a Box: Practical and Scalable EurValve Activity Monitoring Smart Home Kit. LCN Workshops 2017: 128-135 - 2016
- [c15]Tijl De Bie, Jefrey Lijffijt, Raúl Santos-Rodríguez, Bo Kang:
Informative data projections: a framework and two examples. ESANN 2016 - [c14]Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
Learning to separate vocals from polyphonic mixtures via ensemble methods and structured output prediction. ICASSP 2016: 450-454 - [c13]Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodriguez, Tijl De Bie:
Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations. KDD 2016: 1615-1624 - [c12]Tijl De Bie, Jefrey Lijffijt, Cédric Mesnage, Raúl Santos-Rodriguez:
Detecting trends in twitter time series. MLSP 2016: 1-6 - 2015
- [c11]Valero Laparra, Raúl Santos-Rodríguez:
Spatial/spectral information trade-off in hyperspectral images. IGARSS 2015: 1124-1127 - [i3]Tijl De Bie, Jefrey Lijffijt, Raúl Santos-Rodriguez, Bo Kang:
Informative Data Projections: A Framework and Two Examples. CoRR abs/1511.08762 (2015) - 2014
- [j5]Matt McVicar, Raúl Santos-Rodriguez, Yizhao Ni, Tijl De Bie:
Automatic Chord Estimation from Audio: A Review of the State of the Art. IEEE ACM Trans. Audio Speech Lang. Process. 22(2): 556-575 (2014) - [c10]Jesús Cid-Sueiro, Dario García-García, Raúl Santos-Rodríguez:
Consistency of Losses for Learning from Weak Labels. ECML/PKDD (1) 2014: 197-210 - 2013
- [j4]Yizhao Ni, Matt McVicar, Raúl Santos-Rodríguez, Tijl De Bie:
Understanding Effects of Subjectivity in Measuring Chord Estimation Accuracy. IEEE ACM Trans. Audio Speech Lang. Process. 21(12): 2607-2615 (2013) - 2012
- [j3]Yizhao Ni, Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
An End-to-End Machine Learning System for Harmonic Analysis of Music. IEEE Trans. Speech Audio Process. 20(6): 1771-1783 (2012) - [j2]Raúl Santos-Rodríguez, Jesús Cid-Sueiro:
Cost-Sensitive Sequences of Bregman Divergences. IEEE Trans. Neural Networks Learn. Syst. 23(12): 1896-1904 (2012) - [c9]Yizhao Ni, Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
Using Hyper-genre Training to Explore Genre Information for Automatic Chord Estimation. ISMIR 2012: 109-114 - [c8]Dario García-García, Raúl Santos-Rodríguez, Emilio Parrado-Hernández:
Classifier-based affinities for clustering sets of vectors. MLSP 2012: 1-6 - 2011
- [c7]Dario García-García, Raúl Santos-Rodríguez:
Sphere packing for clustering sets of vectors in feature space. ICASSP 2011: 2092-2095 - [c6]Dario García-García, Ulrike von Luxburg, Raúl Santos-Rodríguez:
Risk-Based Generalizations of f-divergences. ICML 2011: 417-424 - [c5]Matt McVicar, Yizhao Ni, Tijl De Bie, Raúl Santos-Rodriguez:
Leveraging Noisy Online Databases for Use in Chord Recognition. ISMIR 2011: 639-644 - [i2]Yizhao Ni, Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
An end-to-end machine learning system for harmonic analysis of music. CoRR abs/1107.4969 (2011) - [i1]Yizhao Ni, Matt McVicar, Raúl Santos-Rodriguez, Tijl De Bie:
Meta-song evaluation for chord recognition. CoRR abs/1109.0420 (2011) - 2010
- [c4]Raúl Santos-Rodríguez, Dario García-García:
Cost-Sensitive Feature Selection Based on the Set Covering Machine. ICDM Workshops 2010: 740-746
2000 – 2009
- 2009
- [j1]Raúl Santos-Rodríguez, Alicia Guerrero-Curieses, Rocío Alaíz-Rodríguez, Jesús Cid-Sueiro:
Cost-sensitive learning based on Bregman divergences. Mach. Learn. 76(2-3): 271-285 (2009) - [c3]Dario García-García, Raúl Santos-Rodríguez:
Spectral Clustering and Feature Selection for Microarray Data. ICMLA 2009: 425-428 - [c2]Raúl Santos-Rodríguez, Dario García-García, Jesús Cid-Sueiro:
Cost-Sensitive Classification Based on Bregman Divergences for Medical Diagnosis. ICMLA 2009: 551-556 - [c1]Raúl Santos-Rodríguez, Alicia Guerrero-Curieses, Rocío Alaíz-Rodríguez, Jesús Cid-Sueiro:
Cost-Sensitive Learning Based on Bregman Divergences. ECML/PKDD (1) 2009: 12
Coauthor Index