| Implicit Surface Modelling with a Globally Regularised Basis of Compact Support. C Walder, B Schölkopf, O Chapelle Computer Graphics Forum 25 (3), 2006 | 87* | 2006 |
| Sparse multiscale Gaussian process regression C Walder, KI Kim, B Schölkopf Proceedings of the 25th international conference on Machine learning, 1112-1119, 2008 | 77 | 2008 |
| Chord2vec: Learning musical chord embeddings S Madjiheurem, L Qu, C Walder Proceedings of the constructive machine learning workshop at 30th conference …, 2016 | 59 | 2016 |
| Support vector machines for business applications BC Lovell, CJ Walder Business Applications and Computational Intelligence, 267, 2006 | 48 | 2006 |
| Tacticzero: Learning to prove theorems from scratch with deep reinforcement learning M Wu, M Norrish, C Walder, A Dezfouli Advances in Neural Information Processing Systems 34, 9330-9342, 2021 | 46 | 2021 |
| Efficient non-parametric Bayesian Hawkes processes R Zhang, C Walder, MA Rizoiu, L Xie arXiv preprint arXiv:1810.03730, 2018 | 43 | 2018 |
| Diffeomorphic dimensionality reduction C Walder, B Schölkopf Advances in Neural Information Processing Systems 21, 2008 | 40 | 2008 |
| Variational inference for sparse Gaussian process modulated Hawkes process R Zhang, C Walder, MA Rizoiu Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6803-6810, 2020 | 39 | 2020 |
| Modelling symbolic music: Beyond the piano roll C Walder Asian conference on machine learning, 174-189, 2016 | 38 | 2016 |
| EditVAE: Unsupervised parts-aware controllable 3D point cloud shape generation S Li, M Liu, C Walder Proceedings of the AAAI Conference on Artificial Intelligence 36 (2), 1386-1394, 2022 | 35 | 2022 |
| Fast Bayesian intensity estimation for the permanental process CJ Walder, AN Bishop International Conference on Machine Learning, 3579-3588, 2017 | 30 | 2017 |
| Implicit surface modelling as an eigenvalue problem C Walder, O Chapelle, B Schölkopf Proceedings of the 22nd international conference on Machine learning, 936-939, 2005 | 30 | 2005 |
| Learning with transformation invariant kernels C Walder, O Chapelle Advances in Neural Information Processing Systems 20, 2007 | 24 | 2007 |
| Monge blunts Bayes: Hardness results for adversarial training Z Cranko, A Menon, R Nock, CS Ong, Z Shi, C Walder International Conference on Machine Learning, 1406-1415, 2019 | 20 | 2019 |
| Markerless 3d face tracking C Walder, M Breidt, H Bülthoff, B Schölkopf, C Curio Pattern Recognition: 31st DAGM Symposium, Jena, Germany, September 9-11 …, 2009 | 18 | 2009 |
| Determinantal point process likelihoods for sequential recommendation Y Liu, C Walder, L Xie Proceedings of the 45th international ACM SIGIR conference on research and …, 2022 | 15 | 2022 |
| Neural dynamic programming for musical self similarity C Walder, D Kim International conference on machine learning, 5105-5113, 2018 | 13 | 2018 |
| Dense uncertainty estimation J Zhang, Y Dai, M Xiang, DP Fan, P Moghadam, M He, C Walder, ... arXiv preprint arXiv:2110.06427, 2021 | 11 | 2021 |
| Learning to continually learn rapidly from few and noisy data I Nicholas, H Kuo, M Harandi, N Fourrier, C Walder, G Ferraro, ... AAAI Workshop on Meta-Learning and MetaDL Challenge, 65-76, 2021 | 10 | 2021 |
| Improved classification using hidden Markov averaging from multiple observation sequences RIA Davis, CJ Walder, BC Lovell Queensland University of Technology 4, 89-92, 2002 | 9 | 2002 |