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Dr. H.C. (Herke) van Hoof

Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Informatics Institute

Bezoekadres
  • Science Park 904
  • Kamernummer: C3.227
Postadres
  • Postbus 94323
    1090 GH Amsterdam
Contactgegevens
  • Publicaties

    2021

    • Wang, S., Sporrel, K., van Hoof, H., Simons, M., de Boer, R. D. D., Ettema, D., Nibbeling, N., Deutekom, M., & Kröse, B. (2021). Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study. International Journal of Environmental Research and Public Health, 18(11). https://doi.org/10.3390/ijerph18116059

    2020

    • Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L., van Harmelen, F., ... Welling, M. (2020). A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer, 53(8), 18-28. https://doi.org/10.1109/MC.2020.2996587 [details]
    • Bakker, T. B., van Hoof, H. C., & Welling, M. (2020). Experimental design for MRI by greedy policy search. In Advances in Neural Information Processing Systems 33 proceedings (NeurIPS 2020) NIPS. https://papers.nips.cc/paper/2020/hash/daed210307f1dbc6f1dd9551408d999f-Abstract.html
    • Huang, J., Oosterhuis, H., de Rijke, M., & van Hoof, H. (2020). Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. In RECSYS 2020: 14th ACM Conference on Recommender Systems : Virtual Event, Brazil, September 22-26, 2020 (pp. 190–199). The Association for Computing Machinery. https://doi.org/10.1145/3383313.3412252 [details]
    • Kool, W., van Hoof, H., & Welling, M. (2020). Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement. Journal of Machine Learning Research, 21, [47]. https://jmlr.csail.mit.edu/papers/v21/19-985.html [details]
    • Kool, W., van Hoof, H., & Welling, M. (2020). Estimating Gradients for Discrete Random Variables by Sampling without Replacement. In International Conference on Learning Representations
    • Manjanna, S., Van Hoof, H., & Dudek, G. (2020). Policy Search on Aggregated State Space for Active Sampling. In J. Xiao, T. Kröger, & O. Khatib (Eds.), Proceedings of the 2018 International Symposium on Experimental Robotics (Vol. Cham, pp. 211-221). (Springer Proceedings in Advanced Robotics; Vol. 11). Springer. https://doi.org/10.1007/978-3-030-33950-0_19 [details]
    • Shang, W., van der Wal, D., van Hoof, H., & Welling, M. (2020). Stochastic Activation Actor Critic Methods. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019 : proceedings (Vol. III, pp. 103-117). (Lecture Notes in Computer Science; Vol. 11908), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-46133-1_7 [details]
    • Wang, Q., & van Hoof, H. (2020). Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables. Proceedings of Machine Learning Research, 119, 10018-10028. http://proceedings.mlr.press/v119/wang20s.html [details]
    • Wöhlke, J., Schmitt, F., & van Hoof, H. (2020). A Performance-Based Start State Curriculum Framework for Reinforcement Learning. In AAMAS'20: proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems : May 9-13, 2020, Auckland, New Zealand (pp. 1503-1511). International Foundation for Autonomous Agents and Multiagent Systems. https://dl.acm.org/doi/10.5555/3398761.3398934 [details]
    • van der Heiden, T., Mirus, F., & van Hoof, H. (2020). Social Navigation with Human Empowerment Driven Deep Reinforcement Learning. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020 : proceedings (Vol. II, pp. 395-407). (Lecture Notes in Computer Science; Vol. 12397). Springer. https://doi.org/10.1007/978-3-030-61616-8_32 [details]
    • van der Pol, E. E., Worrall, D. E., van Hoof, H. C., Oliehoek, F. A., & Welling, M. (2020). MDP homomorphic networks: Group symmetries in reinforcement learning. In Neural Information Processing Systems NIPS.

    2019

    • Caccia, L., van Hoof, H., Courville, A., & Pineau, J. (2019). Deep Generative Modeling of LiDAR Data. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Macau, China, 3-8 November 2019 (pp. 5034-5040). IEEE. https://doi.org/10.1109/IROS40897.2019.8968535 [details]
    • Kool, W., van Hoof, H., & Welling, M. (2019). Attention, learn to solve routing problems! In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://arxiv.org/abs/1803.08475 [details]
    • Kool, W., van Hoof, H., & Welling, M. (2019). Buy 4 REINFORCE Samples, Get a Baseline for Free! In Deep RL Meets Structured Prediction Workshop at ICLR https://openreview.net/forum?id=r1lgTGL5DE
    • Kool, W., van Hoof, H., & Welling, M. (2019). Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. Proceedings of Machine Learning Research, 97, 3499-3508. http://proceedings.mlr.press/v97/kool19a.html [details]
    • Thakur, S., van Hoof, H., Gamboa Higuera, J. C., Precup, D., & Meger, D. (2019). Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. In 2019 International Conference on Robotics and Automation (ICRA) : Montreal, Quebec, Canada, 20-24 May 2019 (Vol. 1, pp. 768-774). IEEE. https://doi.org/10.1109/ICRA.2019.8794328 [details]

    2018

    2017

    • Tangkaratt, V., van Hoof, H., Parisi, S., Neumann, G., Peters, J., & Sugiyama, M. (2017). Policy Search with High-Dimensional Context Variables. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (pp. 2632-2638). AAAI Press.
    • van Hoof, H., Neumann, G., & Peters, J. (2017). Non-parametric Policy Search with Limited Information Loss. Journal of Machine Learning Research, 18, [73].
    • van Hoof, H., Tanneberg, D., & Peters, J. (2017). Generalized Exploration in Policy Search. Machine Learning, 106(9-10), 1705-1724. https://doi.org/10.1007/s10994-017-5657-1

    2016

    • Daniel, C., Hoof, H. V., Neumann, G., & Peters, J. (2016). Probabilistic Inference for Determining Options in Reinforcement Learning. Machine Learning, 104(2-3), 337-357. https://doi.org/10.1007/s10994-016-5580-x
    • Yi, Z., Calandra, R., Veiga, F., van Hoof, H., Hermans, T., Zhang, Y., & Peters, J. (2016). Active Tactile Object Exploration with Gaussian Processes. In IROS 2016: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems : October 9-14, 2016, Daejeon Convention Center, Daejeon, Korea (pp. 4925-4930). Piscataway, NJ: IEEE. https://doi.org/10.1109/IROS.2016.7759723
    • van Hoof, H., Chen, N., Karl, M., Smart, P. V. D., & Peters, J. (2016). Stable Reinforcement Learning with Auto-Encoders for Tactile and Visual Data. In International Conference on Intelligent Robots and Systems (pp. 3928-3934). IEEE. https://doi.org/10.1109/IROS.2016.7759578

    2015

    • Kroemer, O., Daniel, C., Neumann, G., van Hoof, H., & Peters, J. (2015). Towards Learning Hierarchical Skills for Multi-Phase Manipulation Tasks. In Proceedings of the International Conference on Robotics and Automation (ICRA) IEEE. https://doi.org/10.1109/ICRA.2015.7139389
    • Veiga, F. F., van Hoof, H., Peters, J., & Hermans, T. (2015). Stabilizing Novel Objects by Learning to Predict Tactile Slip. In W. Burgard (Ed.), IROS Hamburg 2015 conference digest: IEEE/RSJ International Conference on Intelligent Robots and Systems : September 28-October 02, 2015, Hamburg, Germany (pp. 5065-5072). Piscataway, NJ: IEEE. https://doi.org/10.1109/IROS.2015.7354090
    • van Hoof, H., Hermans, T., Neumann, G., & Peters, J. (2015). Learning Robot In-Hand Manipulation with Tactile Features. In Proceedings of the International Conference on Humanoid Robots (HUMANOIDS) IEEE. https://doi.org/10.1109/HUMANOIDS.2015.7363524
    • van Hoof, H., Peters, J., & Neumann, G. (2015). Learning of Non-Parametric Control Policies with High-Dimensional State Features. Proceedings of Machine Learning Research, 38, 1004-1012.

    2014

    • Bischoff, B., Nguyen-Tuong, D., van Hoof, H. C., McHutchon, A., Rasmussen, C. E., Knoll, A. C., ... Deisenroth, M. P. (2014). Policy Search For Learning Robot Control Using Sparse Data. In Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA) IEEE. https://doi.org/10.1109/ICRA.2014.6907422
    • Kroemer, O., van Hoof, H., Neumann, G., & Peters, J. (2014). Learning to Predict Phases of Manipulation Tasks as Hidden States. In Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA) IEEE. https://doi.org/10.1109/ICRA.2014.6907441
    • van Hoof, H., Kroemer, O., & Peters, J. (2014). Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments. IEEE Transactions on Robotics, 5, 1198-1209. https://doi.org/10.1109/TRO.2014.2334912

    2013

    • van Hoof, H., Kroemer, O., & Peters, J. (2013). Probabilistic Interactive Segmentation for Anthropomorphic Robots in Cluttered Environments. In Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)

    2012

    • van Hoof, H., Kroemer, O., Ben Amor, H., & Peters, J. (2012). Maximally Informative Interaction Learning for Scene Exploration. In IEEE/RSJ International Conference on Intelligent Robots and Systems : IROS 2012 : 7-12 Oct. 2012, Vilamoura, Algarve, Portugal Piscataway, NJ: IEEE. https://doi.org/10.1109/IROS.2012.6386008

    2020

    2021

    • Shang, W. (2021). Crafting deep learning models for reinforcement learning and computer vision applications. [details]

    2016

    • van Hoof, H. (2016). Machine Learning through Exploration for Perception-Driven Robotics.
    This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list.
  • Nevenwerkzaamheden
    • H. van Hoof AI Consulting
      Advisering op het gebied van machinaal leren