Dr. H.C. (Herke) van Hoof
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Informatics Institute
Bezoekadres
- Science Park 904
- Kamernummer: L4.05
Postadres
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Postbus 94323
1090 GH Amsterdam
Contactgegevens
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Publicaties
- refereed (55)
- wetenschappelijk (1)
- congresbijdragen (1)
- erkenning (1)
- supervised theses (12)
- proefschrift (1)
2023
Bakker, T., van Hoof, H., & Welling, M. (2023). Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases : Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023 : proceedings (Vol. I, pp. 3-19). (Lecture Notes in Computer Science; Vol. 14169), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.48550/arXiv.2309.05477, https://doi.org/10.1007/978-3-031-43412-9_1 [details]- Bondesan, R., Gagrani, M., Jeon, W., Lott, C., Rainone, C., Teague, H., Van Hoof, H., Yang, Y., Zappi, P., & Zeng, W. (2023). Neural Topological Ordering for Computation Graphs. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 23, pp. 17327-17339). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/6ef586bdf0af0b609b1d0386a3ce0e4b-Abstract-Conference.html [details]
- Kuric, D., & van Hoof, H. (2023). Reusable Options through Gradient-based Meta Learning. Transactions on Machine Learning Research, (3), Article 717. https://openreview.net/forum?id=qdDmxzGuzu [details]
- Van Hoof, H., & Wang, Q. (2023). Learning Expressive Meta-Representations with Mixture of Expert Neural Processes. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 34, pp. 26242-26255). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/a815fe7cad6af20a6c118f2072a881d2-Abstract-Conference.html [details]
- Wang, Q., & van Hoof, H. C. (2023). Bridge the inference gap of neural processes via expectation maximization. In International Conference on Learning Representations https://openreview.net/pdf?id=A7v2DqLjZdq
- Wang, Q., Federici, M., & van Hoof, H. C. (2023). Bridge the Inference Gaps of Neural Processes via Expectation Maximization. In International Conference on Learning Representations https://openreview.net/forum?id=A7v2DqLjZdq
- Wöhlke, J., Schmitt, F., & van Hoof, H. (2023). Learning Hierarchical Planning-Based Policies from Offline Data. In D. Koutra, C. Plant, M. Gomes Rodriguez, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases: Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023 : proceedings (Vol. IV, pp. 489–505). (Lecture Notes in Computer Science; Vol. 14172), ( Lecture Notes in Artificial Intelligence ). Springer. https://doi.org/10.1007/978-3-031-43421-1_29 [details]
2022
- Giri, C., Granmo, O-C., van Hoof, H., & Blakely, C. D. (2022). Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine. In 2022 International Joint Conference on Neural Networks (IJCNN): 2022 conference proceedings (pp. 5528-5536). IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892796 [details]
- Höpner, N., Tiddi, I., & van Hoof, H. (2022). Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods. In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence: IJCAI 2022, Vienna, Austria, 23-29 July 2022 (pp. 3050-3056). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/423 [details]
- Kool, W., van Hoof, H., Gromicho, J., & Welling, M. (2022). Deep Policy Dynamic Programming for Vehicle Routing Problems. In P. Schaus (Ed.), Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022 : proceedings (pp. 190–213). (Lecture Notes in Computer Science; Vol. 13292). Springer. https://doi.org/10.48550/arXiv.2102.11756, https://doi.org/10.1007/978-3-031-08011-1_14 [details]
- Long, A., Blair, A., & van Hoof, H. (2022). Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation. In K. Sycara, V. Honavar, & M. Spaan (Eds.), Proceedings of the 36th AAAI Conference on Artificial Intelligence: AAAI-22 : virtual conference, Vancouver, Canada, February 22-March 1, 2022 (Vol. 7, pp. 7620-7627). AAAI Press. https://doi.org/10.1609/aaai.v36i7.20728 [details]
- Mansoury, M., Mobasher, B., & van Hoof, H. C. (2022). Exposure-Aware Recommendation using Contextual Bandits. In 5th FAccTRec Workshop on Responsible Recommendation in conjunction with ACM RecSys 2022 ACM.
- Wang, Q., & van Hoof, H. (2022). Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search. Proceedings of Machine Learning Research, 162, 23055-23077. https://proceedings.mlr.press/v162/wang22z.html [details]
- Wöhlke, J., Schmitt, F., & van Hoof, H. (2022). Value Refinement Network (VRN). In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence: IJCAI 2022, Vienna, Austria, 23-29 July 2022 (pp. 3558-3565). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/494 [details]
- van der Pol, E., van Hoof, H., Oliehoek, F., & Welling, M. (2022). Multi-Agent MDP Homomorphic Networks. In Proceedings of the International Conference on Learning Representations OpenReview. https://doi.org/10.48550/arXiv.2110.04495
2021
- Bakker, T., Van Hoof, H., & Welling, M. (2021). Experimental design for MRI by greedy policy search. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 23, pp. 18954-18966). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/daed210307f1dbc6f1dd9551408d999f-Abstract.html [details]
- Van Der Pol, E., Worrall, D., Van Hoof, H., Oliehoek, F., & Welling, M. (2021). MDP homomorphic networks: Group symmetries in reinforcement learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 6, pp. 4199-4210). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2be5f9c2e3620eb73c2972d7552b6cb5-Abstract.html [details]
- 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), Article 6059. Advance online publication. https://doi.org/10.3390/ijerph18116059 [details]
- Wang, S., Zhang, C., Kröse, B., & van Hoof, H. (2021). Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator. Journal of medical systems, 45(12), Article 102. Advance online publication. https://doi.org/10.1007/s10916-021-01773-0 [details]
- Wöhlke, J., Schmitt, F., & van Hoof, H. (2021). Hierarchies of Planning and Reinforcement Learning for Robot Navigation. In 2021 IEEE International Conference on Robotics and Automation (ICRA 2021): May 31-June 4, 2021, Xi'an, China (pp. 10682-10688). IEEE. https://doi.org/10.48550/arXiv.2109.11178, https://doi.org/10.1109/ICRA48506.2021.9561151 [details]
- Zhang, Y., & van Hoof, H. (2021). Deep Coherent Exploration For Continuous Control. Proceedings of Machine Learning Research, 139, 12567-12577. https://proceedings.mlr.press/v139/zhang21t.html [details]
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]
- 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, Article 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 (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]
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
- Barbaros, V., van Hoof, H., Abdolmaleki, A., & Meger, D. (2018). Eager and Memory-Based Non-Parametric Stochastic Search Methods for Learning Control. In 2018 IEEE International Conference on Robotics and Automation (ICRA): May 21-25, 2018, Brisbane, Australia (pp. 5090-5096). IEEE. https://doi.org/10.1109/ICRA.2018.8460633 [details]
- Dong, Y., Shen, Y., Crawford, E., van Hoof, H., & Cheung, J. C. K. (2018). BanditSum: Extractive Summarization as a Contextual Bandit. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing : EMNLP 2018: Brussels, Belgium, Oct. 31-Nov. 4 (pp. 3739-3748). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1409 [details]
- Fujimoto, S., van Hoof, H., & Meger, D. (2018). Addressing function approximation error in actor-critic methods. Proceedings of Machine Learning Research, 80, 1587-1596. http://proceedings.mlr.press/v80/fujimoto18a.html [details]
- Manjanna, S., van Hoof, H., & Dudek, G. (2018). Reinforcement Learning with Non-uniform State Representations for Adaptive Search. In 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics: SSRR 2018 : Philadelphia, PA, USA on August 6-8, 2018 IEEE. https://doi.org/10.1109/SSRR.2018.8468649 [details]
- Smith, M. J. A., Van Hoof, H., & Pineau, J. (2018). An Inference-Based Policy Gradient Method for Learning Options. Proceedings of Machine Learning Research, 80, 4703-4712. http://proceedings.mlr.press/v80/smith18a.html [details]
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. http://www.ausy.tu-darmstadt.de/uploads/Site/EditPublication/tangkaratt2017policy.pdf
- van Hoof, H., Neumann, G., & Peters, J. (2017). Non-parametric Policy Search with Limited Information Loss. Journal of Machine Learning Research, 18, Article 73. http://jmlr.org/papers/v18/16-142.html
- 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). 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). 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. http://www.jmlr.org/proceedings/papers/v38/vanhoof15.pdf
2014
- Bischoff, B., Nguyen-Tuong, D., van Hoof, H. C., McHutchon, A., Rasmussen, C. E., Knoll, A. C., Peters, J., & 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) http://www.ias.informatik.tu-darmstadt.de/uploads/Publications/hoof-HUMANOIDS.pdf
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 IEEE. https://doi.org/10.1109/IROS.2012.6386008
2023
- Chen, N., Mayol-Cuevas, W. W., Karl, M., Aljalbout, E., Zeng, A., Cortese, A., Burgard, W., & van Hoof, H. (2023). Editorial: Language, affordance and physics in robot cognition and intelligent systems. Frontiers in Robotics and AI, 10, Article 1355576. https://doi.org/10.3389/frobt.2023.1355576 [details]
2020
- Mollinga, J., & van Hoof, H. C. (2020). An autonomous free airspace en-route controller using deep reinforcement learning techniques. Paper presented at 9th International Conference for Research in Air Transportation, Tampa, Florida, United States. http://www.icrat.org/ICRAT/seminarContent/2020/papers/ICRAT2020_paper_29.pdf
Prijs / subsidie
- Bolhuis, P., van Hoof, H., Jabbari Farouji, S., Quattrocchio, F. M., Schall, P. & Perez de Alba Ortiz, A. (2023). AI4SMM postdoctoral fellowship.
2024
- Bakker, T. B. (2024). Learning adaptive sensing and active learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- García Satorras, V. (2024). Inductive biases for graph neural networks. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Huang, J. (2024). Learning recommender systems from biased user interactions. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Löwe, S. (2024). Learning structured representations of objects and relations. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Wöhlke, J. G. (2024). Reinforcement learning and planning for autonomous agent navigation: With a focus on sparse reward settings. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2023
- Hoogeboom, E. (2023). Normalizing flows and diffusion models for discrete and geometric data. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Keller, T. A. (2023). Natural inductive biases for artificial intelligence. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- van der Pol, E. (2023). Symmetry and structure in deep reinforcement learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2022
- Gulshad, S. (2022). Explainable robustness for visual classification. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Kool, W. (2022). Learning and optimization in combinatorial spaces: With a focus on deep learning for vehicle routing. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Wang, Q. (2022). Functional representation learning for uncertainty quantification and fast skill transfer. [Thesis, fully internal, Universiteit van Amsterdam]. [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
Geen nevenwerkzaamheden
Bakker, T., van Hoof, H., & Welling, M. (2023). Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases : Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023 : proceedings (Vol. I, pp. 3-19). (Lecture Notes in Computer Science; Vol. 14169), (Lecture Notes in Artificial Intelligence). Springer.