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  • 2024

    Agile Design-Space Exploration of Dynamic Layer-skipping in Neural Receivers

    van Bolderik, B., Sarkar, S., Menkovski, V., Heemstra de Groot, S. M. & Gomony, M. D., 6 Nov 2024, 2024 27th Euromicro Conference on Digital System Design, DSD2024. Kryjak, T. & Petrot, F. (eds.). Institute of Electrical and Electronics Engineers, p. 122-128 7 p. 10741732

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    1 Citation (Scopus)
    37 Downloads (Pure)
  • An Analysis of Evolutionary Migration Models for Multi-Objective, Multi-Fidelity AutoML

    Campero Jurado, I. (Corresponding author) & Vanschoren, J., 29 Jan 2024, 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023. Institute of Electrical and Electronics Engineers, p. 2940-2945 6 p. 10394059

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  • Are Sparse Neural Networks Better Hard Sample Learners?

    Xiao, Q., Wu, B., Yin, L., Gadzinski, C. N., Huang, T., Pechenizkiy, M. & Mocanu, D. C., 2024, 35th British Machine Vision Conference 2024, (BMVC) 2024, Glasgow, UK, November 25-28, 2024. BMVA, 20 p.

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  • A Supervised Learning Approach to Robust Reinforcement Learning for Job Shop Scheduling

    Schmidl, C., Simão, T. D. & Jansen, N., 26 Feb 2024, Proceedings of the 16th International Conference on Agents and Artificial Intelligence: Volume 3: ICAART. Rocha, A. P., Steels, L. & van den Herk, J. (eds.). SciTePress Digital Library, p. 1324-1335 12 p.

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  • Beyond the Dimensions: A Structured Evaluation of Multivariate Time Series Distance Measures

    d'Hondt, J. E., Papapetrou, O. & Paparrizos, J., 17 Jun 2024, 2024 IEEE 40th International Conference on Data Engineering Workshops, ICDEW 2024. Institute of Electrical and Electronics Engineers, p. 107-112 6 p. 10555114

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    2 Citations (Scopus)
  • Can We Break Free from Strong Data Augmentations in Self-Supervised Learning?

    Gowda, S., Arani, E. & Zonooz, B., 15 Apr 2024, (Accepted/In press) 3rd Conference on Lifelong Learning Agents - CoLLAs 2024. PMLR

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates

    Yildirim, M. O., Gok Yildirim, E. C., Sokar, G., Mocanu, D. C. & Vanschoren, J., 6 Jan 2024, Conference on Parsimony and Learning, 3-6 January 2024, Hongkong, China. Chi, Y., Dziugaite, G. K., Qu, Q., Wang Wang, A. & Zhu, Z. (eds.). PMLR, p. 94-107 14 p. (Proceedings of Machine Learning Research; vol. 234).

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    4 Citations (Scopus)
  • Croissant: A Metadata Format for ML-Ready Datasets.

    Akhtar, M., Benjelloun, O., Conforti, C., Gijsbers, P., Giner-Miguelez, J., Jain, N., Kuchnik, M., Lhoest, Q., Marcenac, P., Maskey, M., Mattson, P., Oala, L., Ruyssen, P., Shinde, R., Simperl, E., Thomas, G., Tykhonov, S., Vanschoren, J., van der Velde, J. & Vogler, S. & 1 others, Wu, C.-J., 9 Jun 2024, DEEM '24: Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning. Association for Computing Machinery, Inc, p. 1-6 6 p.

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    3 Citations (Scopus)
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  • Dempster-Shafer Credal Probabilistic Circuits

    Montalvan Hernandez, D., Krak, T. & de Campos, C. P., 24 Aug 2024, Belief Functions: Theory and Applications. BELIEF 2024: 8th International Conference, BELIEF 2024, Belfast, UK, September 2–4, 2024, Proceedings. Bi, Y., Jousselme, A.-L. & Denoeux, T. (eds.). Springer Nature, p. 31-39 9 p. (Lecture Notes in Computer Science; vol. 14909)(Lecture Notes in Artificial Intelligence; vol. 14909).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • Dynamic Data Pruning for Automatic Speech Recognition

    Xiao, Q. (Corresponding author), Ma, P. (Corresponding author), Fernandez-Lopez, A., Wu, B., Yin, L., Petridis, S., Pechenizkiy, M., Pantic, M., Mocanu, D. C. & Liu, S., 2024, Interspeech 2024: Kos, Greece 1-5 September 2024. ISCA, p. 4488-4492 5 p.

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  • E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

    Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C., van Keulen, M. & Mocanu, E., 15 Dec 2024, Proceedings of the 38th Conference on Neural Information Processing Systems, NeurIPS 2024. Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J. & Zhang, C. (eds.). Curran Associates, p. 118483-118512 30 p. (Advances in Neural Information Processing Systems; vol. 37).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • Equivariant Parameter Sharing for Porous Crystalline Materials

    Petković, M., Romero Marimon, P., Menkovski, V. & Calero, S., 16 Apr 2024, Advances in Intelligent Data Analysis XXII: 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I. Miliou, I., Piatkowski, N. & Papapetrou, P. (eds.). Cham: Springer, p. 129-140 12 p. (Lecture Notes in Computer Science (LNCS); vol. 14641).

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    1 Citation (Scopus)
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  • Factored Online Planning in Many-Agent POMDPs

    Galesloot, M. F. L., Simão, T. D., Junges, S. & Jansen, N., 24 Mar 2024, Proceedings of the AAAI Conference on Artificial Intelligence: Thirty-Eighth AAAI Conference on Artificial Intelligence Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence Fourteenth Symposium on Educational Advances in Artificial Intelligence. Wooldridge, M., Dy, J. & Natarajan, S. (eds.). Vancouver: AAAI Press, p. 17407-17415 9 p.

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  • Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs Difficult Downstream Tasks in LLMs

    Yin, L., Jaiswal, A., Liu, S., Kundu, S. & Wang, Z. (Corresponding author), 2024, Proceedings of the 41st International Conference on Machine Learning. Salakhutdinov, R., Kolter, Z., Heller, K., Weller, A., Oliver, N., Scarlett, J. & Berkenkamp, F. (eds.). PMLR, p. 57053-57068 16 p. (Proceedings of Machine Learning Research (PMLR); vol. 235).

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  • Large Learning Agents: Towards Continually Aligned Robots with Scale in RL

    Grooten, B. (Corresponding author), 6 May 2024, AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. Dastani, M., Sichman, J. S., Alechina, N. & Dignum, V. (eds.). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), p. 2746-2748 3 p.

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  • MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning

    Grooten, B., Taylor, M. E., Tomilin, T., Mahmood, A. R., Vasan, G., Fang, M., Pechenizkiy, M. & Mocanu, D. C., 6 May 2024, AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. Dastani, M., Sichman, J. S., Alechina, N. & Dignum, V. (eds.). Richland: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), p. 733-742 10 p.

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    2 Citations (Scopus)
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  • MALIBO: Meta-learning for Likelihood-free Bayesian Optimization

    Pan, J., Falkner, S., Berkenkamp, F. & Vanschoren, J., 2024, International Conference on Machine Learning, 21-27 July 2024, Vienna, Austria. PMLR, p. 39102-39134 33 p. (Proceedings of Machine Learning Research; vol. 235).

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  • Model-Based Meta-reinforcement Learning for Hyperparameter Optimization

    Albrechts, J., Martin, H. & Tavakol, M. (Corresponding author), 14 Nov 2024, Intelligent Data Engineering and Automated Learning – IDEAL 2024: 25th International Conference, Valencia, Spain, November 20–22, 2024, Proceedings, Part I. Julian, V., Camacho, D., Yin, H., Alberola, J. M., Nogueira, V. B., Novais, P. & Tallón-Ballesteros, A. (eds.). Springer, p. 27-39 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15346 LNCS).

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  • Multivariate Similarity Search - A Call for a New Breed of Similarity Search Algorithms

    Papapetrou, O. & d'Hondt, J. E., 23 Jul 2024, 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024. Institute of Electrical and Electronics Engineers, p. 5662 1 p. 10597715

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  • Neural Langevin Dynamics: Towards Interpretable Neural Stochastic Differential Equations

    Koop, S. M. (Corresponding author), Peletier, M. A., Portegies, J. W. & Menkovski, V., 2024, Proceedings of the 5th Northern Lights Deep Learning Conference. Lutchyn, T., Ramírez Rivera, A. & Ricaud, B. (eds.). PMLR, p. 130-137 8 p. (Proceedings of Machine Learning Research (PMLR); vol. 233).

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  • NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization

    Li, G., Yin, L., Ji, J., Niu, W., Qin, M., Ren, B., Guo, L., Liu, S. & Ma, X., 11 May 2024, The Twelfth International Conference on Learning Representations, ICLR 2024 . 16 p.

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  • Node Classification in Random Trees

    Nuijten, W. W. L. & Menkovski, V., 16 Apr 2024, Advances in Intelligent Data Analysis XXII: 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I. Miliou, I., Papapetrou, P. & Piatkowski, N. (eds.). Springer, Vol. 1. p. 105-116 12 p. (Lecture Notes in Computer Science; vol. 14641).

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  • Ontology-guided Knowledge Graph Construction from Maintenance Short Texts

    van Cauter, Z. & Yakovets, N., 15 Aug 2024, Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024). Biswas, R., Kaffee, L.-A., Agarwal, O., Minervini, P., Singh, S. & de Melo, G. (eds.). Association for Computational Linguistics (ACL), p. 75-84 10 p.

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    1 Citation (Scopus)
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  • Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

    Yin, L., Wu, Y., Zhang, Z., Hsieh, C.-Y., Wang, Y., Jia, Y., Li, G., Jaiswal, A., Pechenizkiy, M., Liang, Y., Bendersky, M., Wang, Z. & Liu, S. (Corresponding author-nrf), 2024, Proceedings of the 41st International Conference on Machine Learning. Salakhutdinov, R., Kolter, Z., Heller, K., Weller, A., Oliver, N., Scarlett, J. & Berkenkamp, F. (eds.). PMLR, p. 57101-57115 15 p. (Proceedings of Machine Learning Research (PMLR); vol. 235).

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  • Probabilistic Integral Circuits

    Gala, G., de Campos, C., Peharz, R., Vergari, A. & Quaeghebeur, E., 4 May 2024, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Dasgupta, S., Mandt, S. & Li, Y. (eds.). PMLR, p. 2143-2151 9 p. (Proceedings of Machine Learning Research (PMLR); vol. 238).

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    2 Citations (Scopus)
  • Q-Hitter: A Better Token Oracle for Efficient LLM Inference via Sparse-Quantized KV Cache

    Zhang, Z., Liu, S., Chen, R., Kailkhura, B., Chen, B. & Wang, Z. (Corresponding author), 13 May 2024, Proceedings of Machine Learning and Systems 6 (MLSys 2024). Gibbons, P., Pekhimenko, G. & De Sa, C. (eds.). MLSys, p. 381-394 14 p.

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  • Robust Active Measuring under Model Uncertainty

    Krale, M., Simão, T. D., Tumova, J. & Jansen, N., 24 Mar 2024, Proceedings of the AAAI Conference on Artificial Intelligence. Wooldridge, M., Dy, J. & Natarajan, S. (eds.). Vancouver: AAAI Press, p. 21276-21284 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 38, no. 19).

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  • Scalable Safe Policy Improvement for Factored Multi-Agent MDPs

    Bianchi, F., Zorzi, E., Castellini, A., Simão, T. D., Spaan, M. T. J. & Farinelli, A., 27 Jul 2024, Proceedings of the 41st International Conference on Machine Learning. Salakhutdinov, R., Kolter, Z., Heller, K., Weller, A., Oliver, N., Scarlett, J. & Berkenkamp, F. (eds.). PMLR, p. 3952-2973 22 p. (Proceedings of Machine Learning Research (PMLR); vol. 235).

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  • Soft Learning Probabilistic Circuits

    Ghandi, S., Quost, B. & de Campos, C. P., 2024, 12th International Conference on Probabilistic Graphical Models, 11-13 September 2024, De Lindenberg, Nijmegen, the Netherlands. Kwisthout, J. & Renooij, S. (eds.). PMLR, p. 273-294 (Proceedings of Machine Learning Research; vol. 246).

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  • Subgroup Harm Assessor: Identifying Potential Fairness-Related Harms and Predictive Bias

    Dubowski, A., Weerts, H., Wolters, A. & Pechenizkiy, M., 22 Aug 2024, Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track: European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VIII. Bifet, A., Daniušis, P., Davis, J., Krilavičius, T., Kull, M., Ntoutsi, E., Puolamäki, K. & Žliobaitė, I. (eds.). Cham: Springer, p. 413-417 5 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14948 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • Synopses for Summarizing Spatial Data Streams

    Kiezebrink, J. J. E., Punter, W. R., Papapetrou, O. & Verbeek, K. A. B., 11 Nov 2024, Proceedings 28th International Conference on Extending Database Technology, Proceedings, EDBT 2025: Barcelona, Spain, March 25-March 28. OpenProceedings.org, p. 284-296 13 p. (Advances in Database Technology; vol. 28, no. 2).

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  • The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action

    Weerts, H., Xenidis, R., Tarissan, F., Olsen, H. P. & Pechenizkiy, M., 5 Jun 2024, FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, Inc, p. 2060-2070 11 p.

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  • TRUSTLLM: Trustworthiness in Large Language Models

    Huang, Y. (Corresponding author), Sun, L., Wang, H., Wu, S., Zhang, Q., Li, Y., Gao, C., Huang, Y., Lyu, W., Zhang, Y., Li, X., Sun, H., Liu, Z., Liu, Y., Wang, Y., Zhang, Z., Vidgen, B., Kailkhura, B., Xiong, C. & Xiao, C. & 51 others, Li, C., Xing, E., Huang, F., Liu, H., Ji, H., Wang, H., Zhang, H., Yao, H., Kellis, M., Zitnik, M., Jiang, M., Bansal, M., Zou, J., Pei, J., Liu, J., Gao, J., Han, J., Zhao, J., Tang, J., Wang, J., Vanschoren, J., Mitchell, J. C., Shu, K., Xu, K., Chang, K. W., He, L., Huang, L., Backes, M., Gong, N. Z., Yu, P. S., Chen, P. Y., Gu, Q., Xu, R., Ying, R., Ji, S., Jana, S., Chen, T., Liu, T., Zhou, T., Wang, W., Li, X., Zhang, X., Wang, X., Xie, X., Chen, X., Wang, X., Liu, Y., Ye, Y., Cao, Y., Chen, Y. & Zhao, Y., 27 Jul 2024, Proceedings of the 41st International Conference on Machine Learning. Salakhutdinov, R., Kolter, Z., Heller, K., Weller, A., Oliver, N., Scarlett, J. & Berkenkamp, F. (eds.). PMLR, p. 20166-20270 105 p. (Proceedings of Machine Learning Research (PMLR); vol. 235).

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    3 Citations (Scopus)
  • Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms

    Weerts, H., Kelly-Lyth, A., Binns, R. & Adams-Prassl, J., 5 Jun 2024, FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, Inc, p. 1850-1860 11 p.

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    1 Citation (Scopus)
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  • 2023

    AdaCL: Adaptive Continual Learning

    Yildirim, E. C. G., Yildirim, M. O., Kilickaya, M. & Vanschoren, J., 2023, Proceedings of the 1st ContinualAI Unconference, 2023. PMLR, p. 15-24 10 p. (Proceedings of Machine Learning Research; vol. 249).

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  • Adaptive Distributed Streaming Similarity Joins

    Siachamis, G., Psarakis, K., Fragkoulis, M., Papapetrou, O., Van Deursen, A. & Katsifodimos, A., 27 Jun 2023, DEBS 2023 - Proceedings of the 17th ACM International Conference on Distributed and Event-based Systems. Association for Computing Machinery, Inc, p. 25-36 12 p.

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    1 Citation (Scopus)
  • Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree

    Weerts, H., Xenidis, R., Tarissan, F., Olsen, H. P. & Pechenizkiy, M., 12 Jun 2023, Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023. Association for Computing Machinery, Inc, p. 805-816 12 p.

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    14 Citations (Scopus)
  • Algorithmic Unfairness Through the Lens of EU Non-Discrimination Law Or Why the Law is Not a Decision Tree

    Weerts, H., Xenidis, R., Tarissan, F., Olsen, H. P. & Pechenizkiy, M., 2023, European Workshop on Algorithmic Fairness, EWAF 2023: Proceedings of the 2nd European Workshop on Algorithmic Fairness Winterthur, Switzerland, June 7th to 9th, 2023. Alvarez, J. M. (ed.). CEUR-WS.org, (CEUR Workshop Proceedings; vol. 3442).

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  • Are Labels Needed for Incremental Instance Learning?

    Kilickaya, M. & Vanschoren, J., 14 Aug 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023. Institute of Electrical and Electronics Engineers, p. 2401-2409 9 p. 10208445

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    1 Citation (Scopus)
  • Are Large Kernels Better Teachers than Transformers for ConvNets?

    Huang, T., Yin, L., Zhang, Z., Shen, L., Fang, M., Pechenizkiy, M., Wang, Z. & Liu, S., 30 May 2023, 40th International Conference on Machine Learning, ICML 2023.

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  • Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning

    Grooten, B., Sokar, G., Dohare, S., Mocanu, E., Taylor, M. E., Pechenizkiy, M. & Mocanu, D. C., 2023, AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. Richland: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), p. 1932-1941 10 p.

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    6 Citations (Scopus)
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  • AutoML for Outlier Detection with Optimal Transport Distances

    Singh, P. & Vanschoren, J., 2023, Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023. Elkind, E. (ed.). International Joint Conferences on Artificial Intelligence (IJCAI), p. 7175-7178 4 p.

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  • Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks.

    Sokar, G., Mocanu, D. C. & Pechenizkiy, M., 2023, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings. Amini, M.-R., Canu, S., Fischer, A., Guns, T., Kralj Novak, P. & Tsoumakas, G. (eds.). p. 85-101 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13715 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • Bayesian Structure Scores for Probabilistic Circuits

    Yang, Y., Gala, G. & Peharz, R., 2023, International Conference on Artificial Intelligence and Statistics. PMLR, p. 563-575 13 p. (Proceedings of Machine Learning Research; vol. 206).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    2 Citations (Scopus)
  • Continuous mixtures of tractable probabilistic models

    Correia, A. H. C., Gala, G., Quaeghebeur, E., de Campos, C. P. & Peharz, R., 26 Jun 2023, Proceedings of the 37th AAAI Conference on Artificial Intelligence. Williams, B., Chen, Y. & Neville, J. (eds.). AAAI Press, p. 7244-7252 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 37, no. 6).

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    10 Citations (Scopus)
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  • DataPerf: Benchmarks for Data-Centric AI Development

    Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Rojas, W. G., Diamos, S., Diamos, G., He, L., Parrish, A., Kirk, H. R., Quaye, J., Rastogi, C., Kiela, D., Jurado, D., Kanter, D., Mosquera, R., Ciro, J., Aroyo, L., Acun, B. & Chen, L. & 24 others, Raje, M. S., Bartolo, M., Eyuboglu, S., Ghorbani, A., Goodman, E., Inel, O., Kane, T., Kirkpatrick, C. R., Kuo, T. S., Mueller, J., Thrush, T., Vanschoren, J., Warren, M., Williams, A., Yeung, S., Ardalani, N., Paritosh, P., Zhang, C., Zou, J., Wu, C. J., Coleman, C., Ng, A., Mattson, P. & Reddi, V. J., 2023, 37th Conference on Neural Information Processing Systems, NeurIPS 2023. (Advances in Neural Information Processing Systems; vol. 36).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    9 Citations (Scopus)
  • Efficient-DASH: Automated Radar Neural Network Design Across Tasks and Datasets

    Boot, T., Cazin, N., Sanberg, W. & Vanschoren, J., 27 Jul 2023, 2023 IEEE Intelligent Vehicles Symposium, IV 2023. Institute of Electrical and Electronics Engineers, 7 p. 10186807

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  • Engaging Databases for Data Systems Education.

    Taipalus, T., Miedema, D. & Aivaloglou, E., 29 Jun 2023, ITiCSE 2023 - Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery, Inc, p. 334-340 7 p.

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    4 Citations (Scopus)
  • Evaluating Quadratic Weighted Kappa as the Standard Performance Metric for Automated Essay Scoring

    Doewes, A., Kurdhi, N. & Saxena, A., 11 Jul 2023, Proceedings of the 16th International Conference on Educational Data Mining. Feng, M., Käser, T. & Talukdar, P. (eds.). International Educational Data Mining Society (IEDMS), p. 103-113 11 p.

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  • Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training

    Nowak, A. I. (Corresponding author), Grooten, B., Mocanu, D. C. & Tabor, J., 16 Dec 2023, Proceedings of the 37th International Conference on Neural Information Processing Systems, NeurIPS 2023. Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M. & Levine, S. (eds.). Curran Associates, p. 55160-55192 33 p. (Advances in Neural Information Processing Systems; vol. 36).

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