CUED Publications database

Items where Division is "Div F > Computational and Biological Learning" and Year is

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Number of items: 151.

A

Adams, RP and Ghahramani, Z and Jordan, MI Tree-Structured Stick Breaking Processes for Hierarchical Data. (Unpublished)

Adams, RP and Wallach, HM and Ghahramani, Z Learning the Structure of Deep Sparse Graphical Models. (Unpublished)

Afzal, AM and Mussa, HY and Turner, RE and Bender, A and Glen, RC Target Fishing: A Single-Label or Multi-Label Problem? arXiv. (Unpublished)

Ahn, S and Chertkov, M and Shin, J and Weller, A Gauged Mini-Bucket Elimination for Approximate Inference. (Unpublished)

Ahn, S and Chertkov, M and Weller, A and Shin, J Bucket Renormalization for Approximate Inference. (Unpublished)

Antorán, J and Bhatt, U and Adel, T and Weller, A and Hernández-Lobato, JM Getting a CLUE: A Method for Explaining Uncertainty Estimates. (Unpublished)

August, M and Hernández-Lobato, JM Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control. In: International Conference on High Performance Computing 2018, 2018-6-24 to 2018-6-28, Frankfurt pp. 591-693.. (Unpublished)

B

Bakker, MA and Tu, DP and Valdés, HR and Gummadi, KP and Varshney, KR and Weller, A and Pentland, A DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning. (Unpublished)

Balog, M and Merriënboer, BV and Moitra, S and Li, Y and Tarlow, D Fast Training of Sparse Graph Neural Networks on Dense Hardware. (Unpublished)

Balog, M and Singh, R and Maniatis, P and Sutton, C Neural Program Synthesis with a Differentiable Fixer. (Unpublished)

Balog, M and Teh, YW The Mondrian Process for Machine Learning. (Unpublished)

Balog, M and Tolstikhin, I and Schölkopf, B Differentially Private Database Release via Kernel Mean Embeddings. In: International Conference on Machine Learning, 2018-7-10 to 2018-7-15, Stockholm pp. 423-431.. (Unpublished)

Balog, M and Tripuraneni, N and Ghahramani, Z and Weller, A Lost Relatives of the Gumbel Trick. In: ICML 2017, 2017-8-6 to 2017-8-11, International Conference Centre, Sydney, Australia. (Unpublished)

Berkovich, P and Perim, E and Bruinsma, W GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models. (Unpublished)

Bhatt, U and Andrus, M and Weller, A and Xiang, A Machine Learning Explainability for External Stakeholders. (Unpublished)

Bhatt, U and Weller, A and Moura, JMF Evaluating and Aggregating Feature-based Model Explanations. (Unpublished)

Bhatt, U and Xiang, A and Sharma, S and Weller, A and Taly, A and Jia, Y and Ghosh, J and Puri, R and Moura, JMF and Eckersley, P Explainable Machine Learning in Deployment. (Unpublished)

Borgwardt, KM and Ghahramani, Z Bayesian two-sample tests. (Unpublished)

Bradshaw, J and Matthews, AGDG and Ghahramani, Z Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks. (Unpublished)

Bratieres, S and Quadrianto, N and Ghahramani, Z Bayesian Structured Prediction Using Gaussian Processes. (Unpublished)

Bronskill, J and Gordon, J and Requeima, J and Nowozin, S and Turner, RE TaskNorm: Rethinking Batch Normalization for Meta-Learning. Proceedings of Machine Learning and Systems, 2020. 4683-. (Unpublished)

Bruinsma, WP and Perim, E and Tebbutt, W and Hosking, JS and Solin, A and Turner, RE Scalable Exact Inference in Multi-Output Gaussian Processes. (Unpublished)

Budzianowski, P and Mihail, E and Rahul, G and Shachi, P and Sethi, A and Agarwal, S and Gao, S and Hakkani-Tur, D Research data supporting "MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling". (Unpublished)

Budzianowski, PF and Ramadan, O and Gasic, M Research data supporting "Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing". (Unpublished)

Budzianowski, PF and Wen, T-H and Gasic, M Research data supporting "MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling". (Unpublished)

Bui, TD and Hernández-Lobato, JM and Li, Y and Hernández-Lobato, D and Turner, RE Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation. In: UNSPECIFIED. (Unpublished)

Burgess, J and Lloyd, JR and Ghahramani, Z One-Shot Learning in Discriminative Neural Networks. (Unpublished)

Burt, DR and Rasmussen, CE and van der Wilk, M Rates of Convergence for Sparse Variational Gaussian Process Regression. In: ICML Thirty-sixth International Conference on Machine Learning, 2019-6-10 to 2019-6-15, Long Beach CA pp. 862-871.. (Unpublished)

C

Calliess, J-P and Papachristodoulou, A and Roberts, SJ Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems. (Unpublished)

Chen, Y and Mansinghka, V and Ghahramani, Z Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs. (Unpublished)

Choromanski, K and Cheikhi, D and Davis, J and Likhosherstov, V and Nazaret, A and Bahamou, A and Song, X and Akarte, M and Parker-Holder, J and Bergquist, J and Gao, Y and Pacchiano, A and Sarlos, T and Weller, A and Sindhwani, V Stochastic Flows and Geometric Optimization on the Orthogonal Group. (Unpublished)

Choromanski, K and Davis, JQ and Likhosherstov, V and Song, X and Slotine, J-J and Varley, J and Lee, H and Weller, A and Sindhwani, V An Ode to an ODE. (Unpublished)

Choromanski, K and Likhosherstov, V and Dohan, D and Song, X and Gane, A and Sarlos, T and Hawkins, P and Davis, J and Belanger, D and Colwell, L and Weller, A Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers. (Unpublished)

Choromanski, K and Likhosherstov, V and Dohan, D and Song, X and Gane, A and Sarlos, T and Hawkins, P and Davis, J and Mohiuddin, A and Kaiser, L and Belanger, D and Colwell, L and Weller, A Rethinking Attention with Performers. (Unpublished)

D

Davies, A and Ghahramani, Z The Random Forest Kernel and other kernels for big data from random partitions. (Unpublished)

Depeweg, S and Hernández-Lobato, JM and Doshi-Velez, F and Udluft, S Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables. (Unpublished)

Dieng, AB and Ruiz, FJR and Blei, DM The Dynamic Embedded Topic Model. (Unpublished)

Dieng, AB and Ruiz, FJR and Blei, DM Topic Modeling in Embedding Spaces. (Unpublished)

Dieng, AB and Ruiz, FJR and Blei, DM and Titsias, MK Prescribed Generative Adversarial Networks. (Unpublished)

Donnelly, R and Ruiz, FR and Blei, D and Athey, S Counterfactual Inference for Consumer Choice Across Many Product Categories. (Unpublished)

Duvenaud, D and Lloyd, JR and Grosse, R and Tenenbaum, JB and Ghahramani, Z Structure Discovery in Nonparametric Regression through Compositional Kernel Search. (Unpublished)

Dziugaite, GK and Ghahramani, Z and Roy, DM A study of the effect of JPG compression on adversarial images. (Unpublished)

Dziugaite, GK and Roy, DM and Ghahramani, Z Training generative neural networks via Maximum Mean Discrepancy optimization. In: Association for Uncertainty in Artificial Intelligence UAI 2015, 2018-7-13 to 2018-5-15. (Unpublished)

E

Echeveste, R and Hennequin, G and Lengyel, M Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model. (Unpublished)

F

Fiser, J and Lengyel, M and Savin, C and Orbán, G and Berkes, P How (not) to assess the importance of correlations for the matching of spontaneous and evoked activity. (Unpublished)

Foong, AYK and Bruinsma, WP and Gordon, J and Dubois, Y and Requeima, J and Turner, RE Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. (Unpublished)

Foong, AYK and Li, Y and Hernández-Lobato, JM and Turner, RE 'In-Between' Uncertainty in Bayesian Neural Networks. (Unpublished)

G

Gal, Y and Ghahramani, Z Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. (Unpublished)

Gal, Y and Islam, R and Ghahramani, Z Deep Bayesian Active Learning with Image Data. (Unpublished)

Garriga-Alonso, A and Aitchison, L and Rasmussen, CE Deep Convolutional Networks as shallow Gaussian Processes. arXiv. (Unpublished)

Ge, H and Gal, Y and Ghahramani, Z Dirichlet Fragmentation Processes. (Unpublished)

Gordon, J and Bronskill, J and Bauer, M and Nowozin, S and Turner, RE Meta-Learning Probabilistic Inference For Prediction. In: UNSPECIFIED. (Unpublished)

Gordon, J and Bruinsma, WP and Foong, AYK and Requeima, J and Dubois, Y and Turner, RE Convolutional Conditional Neural Processes. In: UNSPECIFIED. (Unpublished)

Gordon, J and Hernández-Lobato, JM Bayesian Semisupervised Learning with Deep Generative Models. (Unpublished)

Gordon, J and Hernández-Lobato, JM Combining Deep Generative and Discriminative Models for Bayesian Semi-Supervised Learning. Pattern Recognition. ISSN 0031-3203 (Unpublished)

Grgic-Hlaca, N and Zafar, MB and Gummadi, KP and Weller, A Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning. In: AAAI 2018, 2018-2-2 to --. (Unpublished)

Grgić-Hlača, N and Redmiles, EM and Gummadi, KP and Weller, A Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction. (Unpublished)

Grgić-Hlača, N and Weller, A and Redmiles, EM Dimensions of Diversity in Human Perceptions of Algorithmic Fairness. (Unpublished)

Grgić-Hlača, N and Zafar, MB and Gummadi, KP and Weller, A On Fairness, Diversity and Randomness in Algorithmic Decision Making. (Unpublished)

Griffiths, R-R and Hernández-Lobato, JM Constrained Bayesian Optimization for Automatic Chemical Design. (Unpublished)

Grosse, RB and Ghahramani, Z and Adams, RP Sandwiching the marginal likelihood using bidirectional Monte Carlo. (Unpublished)

H

Havasi, M and Hernández-Lobato, JM and Murillo-Fuentes, JJ Deep Gaussian Processes with Decoupled Inducing Inputs. (Unpublished)

Heaukulani, C and Knowles, DA and Ghahramani, Z Beta diffusion trees and hierarchical feature allocations. (Unpublished)

Hennequin, G and Aitchison, L and Lengyel, M Fast sampling for Bayesian inference in neural circuits. (Unpublished)

Hennequin, G and Lengyel, M Characterizing variability in nonlinear recurrent neuronal networks. (Unpublished)

Hernández-Lobato, D and Hernández-Lobato, JM and Li, Y and Bui, T and Turner, RE Stochastic Expectation Propagation for Large Scale Gaussian Process Classification. (Unpublished)

Hernández-Lobato, JM and Hernández-Lobato, D Convergent Expectation Propagation in Linear Models with Spike-and-slab Priors. (Unpublished)

Houlsby, N and Huszár, F and Ghahramani, Z and Lengyel, M Bayesian Active Learning for Classification and Preference Learning. Technical Report. UNSPECIFIED. (Unpublished)

Hron, J and Matthews, AGDG and Ghahramani, Z Variational Gaussian Dropout is not Bayesian. (Unpublished)

I

Iwata, T and Duvenaud, D and Ghahramani, Z Warped Mixtures for Nonparametric Cluster Shapes. (Unpublished)

Iwata, T and Duvenaud, D and Ghahramani, Z Warped Mixtures for Nonparametric Cluster Shapes. (Unpublished)

Iwata, T and Ghahramani, Z Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes. (Unpublished)

J

Janz, D and Westhuizen, JVD and Hernández-Lobato, JM Actively Learning what makes a Discrete Sequence Valid. In: UNSPECIFIED. (Unpublished)

K

Khajehnejad, M and Rezaei, AA and Babaei, M and Hoffmann, J and Jalili, M and Weller, A Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks. (Unpublished)

Kilbertus, N and Ball, PJ and Kusner, MJ and Weller, A and Silva, R The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. (Unpublished)

Kilbertus, N and Gascón, A and Kusner, MJ and Veale, M and Gummadi, KP and Weller, A Blind Justice: Fairness with Encrypted Sensitive Attributes. In: 35th International Conference on Machine Learning, 2018-7-10 to 2018-7-15, Stockholmsmässan, Stockholm Sweden pp. 2630-2639.. (Unpublished)

Kim, B and Varshney, KR and Weller, A Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018). (Unpublished)

Knowles, D and Ghahramani, Z Nonparametric Bayesian sparse factor models with application to gene expression modeling. Annals of Applied Statistics, 5. pp. 1534-1552. (Unpublished)

Knowles, DA and Ghahramani, Z Pitman-Yor Diffusion Trees. (Unpublished)

Kusner, MJ and Hernández-Lobato, JM GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution. (Unpublished)

L

Lazaroiu, CI and McNamee, D and Saemann, C and Zejak, A Strong Homotopy Lie Algebras, Generalized Nahm Equations and Multiple M2-branes. (Unpublished)

Lee, J and Heaukulani, C and Ghahramani, Z and James, LF and Choi, S Bayesian inference on random simple graphs with power law degree distributions. In: ICML 2017, 2017-8-6 to 2017-8-11, International Conference Centre, Sydney Australia. (Unpublished)

Lengyel, M and Koblinger, Á and Popović, M and Fiser, J On the role of time in perceptual decision making. (Unpublished)

Leung, G and Quadrianto, N and Smola, AJ and Tsioutsiouliklis, K Optimal Web-Scale Tiering as a Flow Problem. In: Advances in Neural Information Processing Systems, 2010-12-6 to 2010-12-9, British Columbia, Canada pp. 1333-1341.. (Unpublished)

Likhosherstov, V and Davis, J and Choromanski, K and Weller, A CWY Parametrization: a Solution for Parallelized Learning of Orthogonal and Stiefel Matrices. (Unpublished)

Likhosherstov, V and Song, X and Choromanski, K and Davis, J and Weller, A UFO-BLO: Unbiased First-Order Bilevel Optimization. (Unpublished)

Lloyd, JR and Orbanz, P and Ghahramani, Z and Roy, D Random function priors for exchangeable arrays with applications to graphs and relational data. In: Neural Information Processing Systems, 2012-12-3 to 2012-12-10, South Lake Tahoe, Nevada. (Unpublished)

Lopez-Paz, D and Hernández-Lobato, JM and Ghahramani, Z Gaussian Process Vine Copulas for Multivariate Dependence. (Unpublished)

Lu, C and Schölkopf, B and Hernández-Lobato, JM Deconfounding Reinforcement Learning in Observational Settings. (Unpublished)

M

Mahmood, O and Hernández-Lobato, JM A COLD Approach to Generating Optimal Samples. (Unpublished)

Matthews, AGDG and Ghahramani, Z Variational Bayesian dropout: pitfalls and fixes. In: ICML 2018: 35th International Conference on Machine Learning, 2018-7-10 to 2018-7-15, Stockholm, Sweden pp. 2024-2033.. (Unpublished)

Matthews, AGDG and Ghahramani, Z Classification using log Gaussian Cox processes. (Unpublished)

McNamee, D Characterizing optimal hierarchical policy inference on graphs via non-equilibrium thermodynamics. (Unpublished)

Mohamed, S and Heller, K and Ghahramani, Z Bayesian and L1 Approaches to Sparse Unsupervised Learning. (Unpublished)

Molina, A and Vergari, A and Stelzner, K and Peharz, R and Subramani, P and Mauro, ND and Poupart, P and Kersting, K SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks. (Unpublished)

N

Nguyen, CV and Ho, LST and Xu, H and Dinh, V and Nguyen, B Bayesian Pool-based Active Learning With Abstention Feedbacks. (Unpublished)

Nguyen, CV and Li, Y and Bui, TD and Turner, RE Variational Continual Learning. (Unpublished)

O

Orbanz, P Projective Limit Random Probabilities on Polish Spaces. Electronic Journal of Statistics, 5. pp. 4-1373. (Unpublished)

Overweg, H and Popkes, A-L and Ercole, A and Li, Y and Hernández-Lobato, JM and Zaykov, Y and Zhang, C Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. (Unpublished)

P

Palla, K and Knowles, DA and Ghahramani, Z A dependent partition-valued process for multitask clustering and time evolving network modelling. (Unpublished)

Peharz, R and Vergari, A and Stelzner, K and Molina, A and Trapp, M and Kersting, K and Ghahramani, Z Probabilistic Deep Learning using Random Sum-Product Networks. (Unpublished)

Pernkopf, F and Roth, W and Zoehrer, M and Pfeifenberger, L and Schindler, G and Froening, H and Tschiatschek, S and Peharz, R and Mattina, M and Ghahramani, Z Efficient and Robust Machine Learning for Real-World Systems. (Unpublished)

Q

Quadrianto, N and Buntine, WL Linear Discriminant. In: Encyclopedia of Machine Learning. Springer. (Unpublished)

Quadrianto, N and Buntine, WL Linear Regression. In: Encyclopedia of Machine Learning. Springer. (Unpublished)

Quadrianto, N and Buntine, WL Regression. In: Encyclopedia of Machine Learning. Springer. (Unpublished)

Quadrianto, N and Kersting, K and Xu, Z Gaussian Process. In: Encyclopedia of Machine Learning. Springer. (Unpublished)

Quadrianto, N and Sharmanska, V and Knowles, DA and Ghahramani, Z The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models. (Unpublished)

qwe, and Wolpert, DM Training videos for subjective decision time. (Unpublished)

R

Ranca, R and Ghahramani, Z Slice Sampling for Probabilistic Programming. (Unpublished)

Rasmussen, CE and Nickisch, H Gaussian Processes for Machine Learning. (Unpublished)

Reed, C and Ghahramani, Z Scaling the Indian Buffet Process via Submodular Maximization. In ICML 2013: JMLR W&CP 28 (3): 1013-1021, 2013. (Unpublished)

Requeima, J and Gordon, J and Bronskill, J and Nowozin, S and Turner, RE Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes. In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019-12-8 to 2019-12-14, Vancouver, Canada. (Unpublished)

Rowland, M and Hron, J and Tang, Y and Choromanski, K and Sarlos, T and Weller, A Orthogonal Estimation of Wasserstein Distances. (Unpublished)

Ruiz, FJR and Athey, S and Blei, DM SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements. (Unpublished)

Ruiz, FJR and Titsias, MK A Contrastive Divergence for Combining Variational Inference and MCMC. (Unpublished)

Ruiz, FJR and Valera, I and Svensson, L and Perez-Cruz, F Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation. June, 2. pp. 177-191. (Unpublished)

S

Savage, RS and Ghahramani, Z and Griffin, JE and Kirk, P and Wild, DL Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data. International Conference on Machine Learning (ICML) 2012: Workshop on Machine Learning in Genetics and Genomics. (Unpublished)

Sengupta, U and Amos, M and Hosking, JS and Rasmussen, CE and Juniper, M and Young, PJ Ensembling geophysical models with Bayesian Neural Networks. Advances in Neural Information Processing Systems (NeurIPS) 2020. (Unpublished)

Shah, A and Ghahramani, Z Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering. (Unpublished)

Shao, X and Molina, A and Vergari, A and Stelzner, K and Peharz, R and Liebig, T and Kersting, K Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. (Unpublished)

Simm, GNC and Hernández-Lobato, JM A Generative Model for Molecular Distance Geometry. (Unpublished)

Speicher, T and Heidari, H and Grgic-Hlaca, N and Gummadi, KP and Singla, A and Weller, A and Zafar, MB A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices. (Unpublished)

T

Tarek, M and Xu, K and Trapp, M and Ge, H and Ghahramani, Z DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models. (Unpublished)

Teng, Y and Gao, W and Chalus, F and Choromanska, A and Goldfarb, D and Weller, A Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models. (Unpublished)

Titsias, MK and Ruiz, FJR Unbiased Implicit Variational Inference. Artificial Intelligence and Statistics (AISTATS 2019). (Unpublished)

Tran, D and Ruiz, FJR and Athey, S and Blei, DM Model Criticism for Bayesian Causal Inference. (Unpublished)

Trapp, M and Peharz, R and Pernkopf, F Optimisation of Overparametrized Sum-Product Networks. (Unpublished)

Trapp, M and Peharz, R and Pernkopf, F and Rasmussen, CE Deep Structured Mixtures of Gaussian Processes. (Unpublished)

Trapp, M and Peharz, R and Rasmussen, CE and Pernkopf, F Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks. In: 35th International Conference on Machine Learning, -- to --, Stockholm, Sweden. (Unpublished)

Tripuraneni, N and Gu, S and Ge, H and Ghahramani, Z A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models. (Unpublished)

Tripuraneni, N and Rowland, M and Ghahramani, Z and Turner, R Magnetic Hamiltonian Monte Carlo. In: ICML 2017, 2017-8-6 to 2017-8-11, International Conference Centre, Sydney Australia. (Unpublished)

Turner, RE and Bui, T and Li, Y and Cuong, N Variational continual learning. In: International Conference on Learning Representations, -- to --. (Unpublished)

Turner, RE and Bui, T and Yan, J A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation. Journal of Machine Learning Research. (Unpublished)

V

Valera, I and Pradier, MF and Ghahramani, Z General Latent Feature Modeling for Data Exploration Tasks. (Unpublished)

Vergari, A and Molina, A and Peharz, R and Ghahramani, Z and Kersting, K and Valera, I Automatic Bayesian Density Analysis. (Unpublished)

Vergari, A and Molina, A and Peharz, R and Kersting, K and Mauro, ND and Esposito, F Sum-product autoencoding: Encoding and decoding representations using sum-product networks. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, -- to -- pp. 4163-4170.. (Unpublished)

Vogels, TP and Froemke, RC and Doyon, N and Gilson, M and Haas, JS and Liu, R and Maffei, A and Miller, P and Wierenga, C and Woodin, MA and Zenke, F and Sprekeler, H Inhibitory Synaptic Plasticity - Spike timing dependence and putative network function. Frontiers in Neural Circuits, 7. (Unpublished)

Von Kügelgen, B and Loog, M and Mey, A Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. In: Artificial Intelligence and Statistics (AISTATS), 2019-4-16 to 2019-4-18. (Unpublished)

W

Wang, H and Grgic-Hlaca, N and Lahoti, P and Gummadi, KP and Weller, A An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision. (Unpublished)

Weller, A and Domke, J Clamping Improves TRW and Mean Field Approximations. (Unpublished)

Weller, A and Jebara, T Approximating the Bethe partition function. (Unpublished)

Weller, A and Jebara, T Bethe Bounds and Approximating the Global Optimum. (Unpublished)

Westwater, ML and Mancini, F and Gorka, AX and Shapleske, J and Serfontein, J and Grillon, C and Ernst, M and Ziauddeen, H and Fletcher, PC Prefrontal responses during proactive and reactive inhibition are differentially impacted by stress in anorexia and bulimia nervosa. BiorXiv. (Unpublished)

Westwater, ML and Mancini, F and Shapleske, J and Serfontein, J and Ernst, M and Ziauddeen, H and Fletcher, P Dissociable hormonal responses to symptoms and stress in anorexia and bulimia nervosa. Psychological Medicine. ISSN 0033-2917 (Unpublished)

Williamson, S and Ghahramani, Z and MacEachern, SN and Xing, EP Restricting exchangeable nonparametric distributions. (Unpublished)

Wilson, AG and Ghahramani, Z Copula Processes. (Unpublished)

Wilson, AG and Knowles, DA and Ghahramani, Z Gaussian Process Regression Networks. (Unpublished)

Wu, Y and Hernández-Lobato, JM and Ghahramani, Z Dynamic Covariance Models for Multivariate Financial Time Series. (Unpublished)

Z

Zafar, MB and Valera, I and Rodriguez, MG and Gummadi, KP and Weller, A From Parity to Preference-based Notions of Fairness in Classification. (Unpublished)

Zhang, Y and Hernández-Lobato, JM Ergodic Inference: Accelerate Convergence by Optimisation. (Unpublished)

Ś

Ścibior, A and Kammar, O and Vákár, M and Staton, S and Yang, H and Cai, Y and Ostermann, K and Moss, SK and Heunen, C and Ghahramani, Z Denotational validation of higher-order Bayesian inference. Proc. ACM Program. Lang. 2, POPL, Article 60 (January 2018). (Unpublished)

This list was generated on Tue Oct 27 20:18:37 2020 GMT.