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Number of items: 37.
Article
Aitchison, L;
Corradi, N;
Latham, PE;
(2016)
Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables.
PLoS Comput Biol
, 12
(12)
, Article e1005110. 10.1371/journal.pcbi.1005110.
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Aitchison, L;
Lengyel, M;
(2016)
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics.
PLOS Computational Biology
, 12
(12)
, Article e1005186. 10.1371/journal.pcbi.1005186.
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Ferrè, ER;
Sahani, M;
Haggard, P;
(2016)
Subliminal stimulation and somatosensory signal detection.
Acta Psychologica
, 170
pp. 103-111.
10.1016/j.actpsy.2016.06.009.
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Hiratani, N;
Fukai, T;
(2016)
Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity.
Frontiers in Neural Circuits
, 10
p. 41.
10.3389/fncir.2016.00041.
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Lorenz, R;
Monti, RP;
Violante, IR;
Anagnostopoulos, C;
Faisal, AA;
Montana, G;
Leech, R;
(2016)
The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI.
NeuroImage
, 129
pp. 320-334.
10.1016/j.neuroimage.2016.01.032.
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Muandet, K;
Sriperumbudur, B;
Fukumizu, K;
Gretton, A;
Schölkopf, B;
(2016)
Kernel Mean Shrinkage Estimators.
Journal of Machine Learning Research
, 17
, Article 48.
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Navajas, J;
Bahrami, B;
Latham, PE;
(2016)
Post-decisional accounts of biases in confidence.
Current Opinion in Behavioral Sciences
, 11
pp. 55-60.
10.1016/j.cobeha.2016.05.005.
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Stringer, C;
Pachitariu, M;
Steinmetz, NA;
Okun, M;
Bartho, P;
Harris, KD;
Sahani, M;
(2016)
Inhibitory control of correlated intrinsic variability in cortical networks.
Elife
, 5
, Article e19695. 10.7554/eLife.19695.
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Wager, TD;
Atlas, LY;
Botvinick, MM;
Chang, LJ;
Coghill, RC;
Davis, KD;
Iannetti, GD;
... Yarkoni, T; + view all
(2016)
Pain in the ACC?
Proceedings of The National Academy of Sciences of The United States of America (PNAS)
, 113
(18)
E2474-E2475.
10.1073/pnas.1600282113.
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Weichwald, S;
Grosse-Wentrup, M;
Gretton, A;
(2016)
MERLiN: Mixture Effect Recovery in Linear Networks.
IEEE Journal of Selected Topics in Signal Processing
, 10
(7)
pp. 1254-1266.
10.1109/JSTSP.2016.2601144.
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Williamson, RS;
Ahrens, MB;
Linden, JF;
Sahani, M;
(2016)
Input-Specific Gain Modulation by Local Sensory Context Shapes Cortical and Thalamic Responses to Complex Sounds.
Neuron
, 91
(2)
pp. 467-481.
10.1016/j.neuron.2016.05.041.
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Proceedings paper
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Adam, V;
Hensman, J;
Sahani, M;
(2016)
Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference.
In:
Proceedings of MLSP2016.
IEEE
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Bohner, G;
Sahani, M;
(2016)
Convolutional higher order matching pursuit.
In:
Proceedings of MLSP2016.
IEEE
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Chung, AW;
Pesce, E;
Monti, RP;
Montana, G;
(2016)
Classifying HCP task-fMRI networks using heat kernels.
In:
Proceedings of the 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).
IEEE: Trento, Italy.
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Chwialkowski, K;
Strathmann, H;
Gretton, A;
(2016)
A Kernel Test of Goodness of Fit.
In:
ICML ’16: Proceedings of the 32nd International Conference on Machine Learning.
(pp. pp. 2606-2615).
JMLR: Workshop and Conference Proceedings
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Fernandez Aguilar, T;
Rivera, N;
Teh, YW;
(2016)
Gaussian processes for survival analysis.
In:
(Proceedings) Advances in Neural Information Processing Systems 29 (NIPS 2016).
NIPS Proceedings
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Distinguishing distributions with interpretable features.
In:
ICML 2016 Workshop on Data-Efficient Machine Learning.
: New York, USA.
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Lorenz, R;
Monti, RP;
Hampshire, A;
Koush, Y;
Anagnostopoulos, C;
Faisal, AA;
Sharp, D;
... Violante, IR; + view all
(2016)
Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization.
In:
Proceedings of the 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).
IEEE: Trento, Italy.
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Monti, R;
Lorenz, R;
Leech, R;
Anagnostopoulos, C;
Montana, G;
(2016)
Text-mining the neurosynth corpus using deep boltzmann machines.
In:
Proceedings of the 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI) 2016.
IEEE: Trento, Italy.
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Rubenstein, PK;
Chwialkowski, KP;
Gretton, A;
(2016)
A Kernel Test for Three-Variable Interactions with Random Processes.
In:
UAI ’16: Proceedings of the 32nd International Conference on Uncertainty in Artificial Intelligence.
(pp. pp. 637-646).
AUAI Press
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Sahani, M;
Bohner, G;
Meyer, A;
(2016)
Score-matching estimators for continuous-time point-process regression models.
In:
Proceedings of MLSP2016.
IEEE
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Weichwald, S;
Gretton, A;
Schölkopf, B;
Grosse-Wentrup, M;
(2016)
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data.
In:
PRNI 2016: 6th International Workshop on Pattern Recognition in Neuroimaging.
Institute of Electrical and Electronic Engineers (IEEE)
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Report
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2016)
Learning Theory for Distribution Regression.
Journal of Machine Learning Research: London, UK.
(In press).
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Working / discussion paper
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Interpretable Distribution Features with Maximum Testing Power.
ArXiv
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Conference item
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Distinguishing distributions with interpretable features.
Presented at: International Conference on Machine Learning (ICML): Data-Efficient Machine Learning workshop, New York, USA.
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Sriperumbudur, B;
Szabo, Z;
(2016)
Optimal Uniform and Lp Rates for Random Fourier Features.
Presented at: Theory of Big Data Workshop, London, United Kingdom.
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Strathmann, H;
Sejdinovic, D;
Livingston, S;
Schuster, I;
Lomeli Garcia, M;
Szabo, Z;
Andrieu, C;
(2016)
Kernel techniques for adaptive Monte Carlo methods.
Presented at: Greek Stochastics Workshop on Big Data and Big Models, Tinos, Greek.
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Szabo, Z;
(2016)
Hypothesis Testing with Kernels.
Presented at: International Workshop on Pattern Recognition in Neuroimaging (PRNI), Trento, Italy.
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Szabo, Z;
(2016)
Kernel-based learning on probability distributions.
Presented at: UNSPECIFIED, San Diego, California, USA.
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Szabo, Z;
(2016)
Learning from Features of Sets and Probabilities.
Presented at: Talk at Imperial College London, Department of Computing, London, United Kingdom.
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Szabo, Z;
(2016)
Optimal Rates for the Random Fourier Feature Technique.
Presented at: invited talk at École Polytechnique, Palaiseau, France.
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Szabo, Z;
(2016)
Performance guarantees for kernel-based learning on probability distributions.
Presented at: Talk at Special Symposium on Intelligent Systems, MPI Tübingen, Germany.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2016)
Distribution Regression with Minimax-Optimal Guarantee.
Presented at: MASCOT-NUM 2016, Toulouse, France.
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Poster
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Jitkrittum, W;
Szabo, Z;
Chwialkowski, K;
Gretton, A;
(2016)
Distinguishing distributions with interpretable features.
Presented at: International Conference on Machine Learning (ICML): Data-Efficient Machine Learning workshop, New York, USA.
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Szabo, Z;
Sriperumbudur, B;
Poczos, B;
Gretton, A;
(2016)
Optimal Regression on Sets.
Presented at: eResearch Domain launch event, London, United Kingdom.
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Thesis
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Elliott, LT;
(2016)
Bayesian nonparametric models of genetic variation.
Doctoral thesis , UCL (University College London).
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Lakshminarayanan, B;
(2016)
Decision trees and forests: a probabilistic perspective.
Doctoral thesis , UCL (University College London).
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