Zoubin Ghahramani
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British intelligence researcher
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Machine Learning
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Computer Science
Zoubin Ghahramani's Degrees
- PhD Computer Science University of Pennsylvania
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Why Is Zoubin Ghahramani Influential?
(Suggest an Edit or Addition)According to Wikipedia, Zoubin Ghahramani FRS is a British-Iranian researcher and Professor of Information Engineering at the University of Cambridge. He holds joint appointments at University College London and the Alan Turing Institute. and has been a Fellow of St John's College, Cambridge since 2009. He was Associate Research Professor at Carnegie Mellon University School of Computer Science from 2003–2012. He was also the Chief Scientist of Uber from 2016 until 2020. He joined Google Brain in 2020 as senior research director. He is also Deputy Director of the Leverhulme Centre for the Future of Intelligence.
Zoubin Ghahramani's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (2015) (5980)
- An Introduction to Variational Methods for Graphical Models (1999) (4081)
- Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (2003) (3927)
- An internal model for sensorimotor integration. (1995) (3104)
- Active Learning with Statistical Models (1996) (2212)
- Computational principles of movement neuroscience (2000) (1905)
- Sparse Gaussian Processes using Pseudo-inputs (2005) (1651)
- Learning from labeled and unlabeled data with label propagation (2002) (1616)
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks (2015) (1474)
- Probabilistic machine learning and artificial intelligence (2015) (1329)
- Factorial Hidden Markov Models (1995) (1249)
- Deep Bayesian Active Learning with Image Data (2017) (1150)
- A Unifying Review of Linear Gaussian Models (1999) (1038)
- Kronecker Graphs: An Approach to Modeling Networks (2008) (987)
- Infinite latent feature models and the Indian buffet process (2005) (830)
- An Introduction to Hidden Markov Models and Bayesian Networks (2001) (798)
- Simultaneous Localization and Mapping with Sparse Extended Information Filters (2004) (772)
- The EM algorithm for mixtures of factor analyzers (1996) (744)
- Variational Methods (2014) (680)
- Perspectives and problems in motor learning (2001) (676)
- Supervised learning from incomplete data via an EM approach (1993) (673)
- The Infinite Hidden Markov Model (2001) (635)
- Parameter estimation for linear dynamical systems (1996) (605)
- Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference (2015) (600)
- Learning Dynamic Bayesian Networks (1997) (592)
- Bayesian Active Learning for Classification and Preference Learning (2011) (571)
- Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions (2003) (565)
- The variational Bayesian EM algorithm for incomplete data: with application to scoring graphical model structures (2003) (543)
- Infinite Mixtures of Gaussian Process Experts (2001) (527)
- Predictive Entropy Search for Efficient Global Optimization of Black-box Functions (2014) (526)
- GPflow: A Gaussian Process Library using TensorFlow (2016) (508)
- Linear dimensionality reduction: survey, insights, and generalizations (2014) (501)
- Scalable Variational Gaussian Process Classification (2014) (489)
- Variational Inference for Bayesian Mixtures of Factor Analysers (1999) (472)
- Gaussian Processes for Ordinal Regression (2005) (463)
- SMEM Algorithm for Mixture Models (1998) (455)
- Training generative neural networks via Maximum Mean Discrepancy optimization (2015) (451)
- Structure Discovery in Nonparametric Regression through Compositional Kernel Search (2013) (444)
- The Indian Buffet Process: An Introduction and Review (2011) (419)
- Gaussian Process Behaviour in Wide Deep Neural Networks (2018) (415)
- Variational Learning for Switching State-Space Models (2000) (403)
- MCMC for Doubly-intractable Distributions (2006) (381)
- A study of the effect of JPG compression on adversarial images (2016) (378)
- Bayesian hierarchical clustering (2005) (376)
- Propagation Algorithms for Variational Bayesian Learning (2000) (353)
- A Bayesian approach to reconstructing genetic regulatory networks with hidden factors (2005) (335)
- Warped Gaussian Processes (2003) (333)
- Preference learning with Gaussian processes (2005) (330)
- Learning with Multiple Labels (2002) (319)
- Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study (1995) (315)
- Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic (2016) (302)
- MetaGAN: An Adversarial Approach to Few-Shot Learning (2018) (293)
- Proceedings of the 24th international conference on Machine learning (2007) (289)
- Stick-breaking Construction for the Indian Buffet Process (2007) (287)
- Learning Nonlinear Dynamical Systems Using an EM Algorithm (1998) (281)
- Local and global sparse Gaussian process approximations (2007) (280)
- Bayesian Monte Carlo (2002) (277)
- Editors. Advances in Neural Information Processing Systems (2002) (265)
- Generative models for discovering sparse distributed representations. (1997) (265)
- Beam sampling for the infinite hidden Markov model (2008) (260)
- Modeling T-cell activation using gene expression profiling and state-space models (2004) (255)
- Modular decomposition in visuomotor learning (1997) (253)
- Computational motor control (2004) (247)
- Bayesian Classifier Combination (2012) (230)
- Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results (2004) (229)
- Automatic Construction and Natural-Language Description of Nonparametric Regression Models (2014) (213)
- Learning from Incomplete Data (1994) (213)
- Handling Incomplete Heterogeneous Data using VAEs (2018) (207)
- Generalization to Local Remappings of the Visuomotor Coordinate Transformation (1996) (204)
- Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning (2004) (199)
- Bayesian correlated clustering to integrate multiple datasets (2012) (188)
- Turing: A Language for Flexible Probabilistic Inference (2018) (180)
- Analysis of the synergies underlying complex hand manipulation (2004) (179)
- Modeling Dyadic Data with Binary Latent Factors (2006) (176)
- Optimization with EM and Expectation-Conjugate-Gradient (2003) (176)
- Randomized Nonlinear Component Analysis (2014) (168)
- Tree-Structured Stick Breaking for Hierarchical Data (2010) (167)
- Student-t Processes as Alternatives to Gaussian Processes (2014) (162)
- SIGMa: simple greedy matching for aligning large knowledge bases (2012) (157)
- On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes (2015) (157)
- A Probabilistic Model for Online Document Clustering with Application to Novelty Detection (2004) (156)
- Metropolis Algorithms for Representative Subgraph Sampling (2008) (156)
- Bayesian Sets (2005) (153)
- Bayesian model search for mixture models based on optimizing variational bounds (2002) (153)
- Computational models of sensorimotor integration (1997) (153)
- Gaussian Process Regression Networks (2011) (153)
- Semi-supervised learning : from Gaussian fields to Gaussian processes (2003) (150)
- Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks (2017) (146)
- Avoiding pathologies in very deep networks (2014) (144)
- Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning (2017) (142)
- Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions (2015) (140)
- Probabilistic Models for Incomplete Multi-dimensional Arrays (2009) (139)
- Infinite Sparse Factor Analysis and Infinite Independent Components Analysis (2007) (139)
- Probabilistic Matrix Factorization with Non-random Missing Data (2014) (138)
- Bayesian Gaussian Process Classification with the EM-EP Algorithm (2006) (138)
- Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling (2010) (137)
- Neural Adaptive Sequential Monte Carlo (2015) (136)
- Learning Multiple Related Tasks using Latent Independent Component Analysis (2005) (130)
- Discovering latent influence in online social activities via shared cascade poisson processes (2013) (130)
- MCMC for Variationally Sparse Gaussian Processes (2015) (129)
- Bayesian nonparametric latent feature models (2007) (129)
- Biomarker discovery in microarray gene expression data with Gaussian processes (2005) (128)
- Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (2015) (125)
- Predictive automatic relevance determination by expectation propagation (2004) (124)
- A General Framework for Constrained Bayesian Optimization using Information-based Search (2015) (124)
- Graphical Models and Variational Methods (2001) (123)
- Bayesian Cluster Analysis: Point Estimation and Credible Balls (with Discussion) (2015) (122)
- Random function priors for exchangeable arrays with applications to graphs and relational data (2012) (119)
- Active Learning of Model Evidence Using Bayesian Quadrature (2012) (116)
- Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms (2004) (116)
- Collaborative Gaussian Processes for Preference Learning (2012) (113)
- An Infinite Latent Attribute Model for Network Data (2012) (112)
- Flexible latent variable models for multi-task learning (2008) (111)
- The Infinite Factorial Hidden Markov Model (2008) (110)
- Hidden Markov Decision Trees (1996) (108)
- A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series (2009) (106)
- Factorial Learning and the EM Algorithm (1994) (106)
- Bayesian non-parametrics and the probabilistic approach to modelling (2013) (105)
- Relational Learning with Gaussian Processes (2006) (105)
- Bayesian Exponential Family PCA (2008) (105)
- Variational Bayesian learning of directed graphical models with hidden variables (2006) (104)
- The Mirage of Action-Dependent Baselines in Reinforcement Learning (2018) (104)
- Perceptual distortion contributes to the curvature of human reaching movements (1994) (104)
- Copula Processes (2010) (103)
- A Non-Parametric Bayesian Method for Inferring Hidden Causes (2006) (99)
- Unsupervised Learning (2003) (98)
- Switching State-Space Models (1996) (95)
- Appearance-based gender classification with Gaussian processes (2006) (89)
- Learning the Structure of Deep Sparse Graphical Models (2009) (86)
- Spectral Methods for Automatic Multiscale Data Clustering (2006) (85)
- Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering (2009) (84)
- Copula-based Kernel Dependency Measures (2012) (83)
- On the Convergence of Bound Optimization Algorithms (2002) (82)
- Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates (1998) (81)
- The delve manual (1996) (79)
- Time-Sensitive Dirichlet Process Mixture Models (2005) (77)
- Variable Noise and Dimensionality Reduction for Sparse Gaussian processes (2006) (77)
- Generalised Wishart Processes (2010) (76)
- Statistical Model Criticism using Kernel Two Sample Tests (2015) (72)
- Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning (2019) (72)
- One-Network Adversarial Fairness (2019) (71)
- Bayesian and L1 Approaches to Sparse Unsupervised Learning (2011) (70)
- Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks (2013) (69)
- Accelerated sampling for the Indian Buffet Process (2009) (68)
- A Nonparametric Bayesian Approach to Modeling Overlapping Clusters (2007) (67)
- Discovering transcriptional modules by Bayesian data integration (2010) (66)
- Bilinear dynamical systems (2005) (66)
- Discovering Interpretable Representations for Both Deep Generative and Discriminative Models (2018) (66)
- Learning Nonlinear Dynamical Systems Using the Expectation–Maximization Algorithm (2001) (65)
- The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models (2009) (65)
- Unsupervised learning of sensory-motor primitives (2003) (63)
- Approximate inference for the loss-calibrated Bayesian (2011) (62)
- Learning to Parse Images (1999) (61)
- Cold-start Active Learning with Robust Ordinal Matrix Factorization (2014) (61)
- Sandwiching the marginal likelihood using bidirectional Monte Carlo (2015) (60)
- Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits (2020) (60)
- Occam's Razor (2000) (59)
- A bayesian view of motor adaptation (2002) (59)
- Variational Bayesian dropout: pitfalls and fixes (2018) (58)
- R/BHC: fast Bayesian hierarchical clustering for microarray data (2009) (58)
- Denotational validation of higher-order Bayesian inference (2017) (57)
- The Automatic Statistician (2019) (57)
- Dropout as a Bayesian Approximation: Appendix (2015) (56)
- Practical probabilistic programming with monads (2015) (55)
- The Random Forest Kernel and other kernels for big data from random partitions (2014) (55)
- Statistical models for partial membership (2008) (55)
- A Simple Bayesian Framework for Content-Based Image Retrieval (2006) (54)
- The infinite HMM for unsupervised PoS tagging (2009) (54)
- Biomimetics: Nature-Based Innovation (2011) (52)
- Solving inverse problems using an EM approach to density estimation (1993) (52)
- Pareto Frontier Learning with Expensive Correlated Objectives (2016) (51)
- Distributed Flexible Nonlinear Tensor Factorization (2016) (50)
- Dependent Indian Buffet Processes (2010) (49)
- Graph Kernels by Spectral Transforms (2006) (49)
- Few-shot learning of neural networks from scratch by pseudo example optimization (2018) (48)
- A Hierarchical Community of Experts (1999) (48)
- Continuous Relaxations for Discrete Hamiltonian Monte Carlo (2012) (47)
- Towards semi-supervised classification with Markov random fields (2002) (46)
- A Very Simple Safe-Bayesian Random Forest (2015) (44)
- A graphical model for protein secondary structure prediction (2004) (44)
- Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures (2009) (44)
- Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process (2009) (43)
- A note on the evidence and Bayesian Occam's razor (2005) (42)
- A Bayesian network model for protein fold and remote homologue recognition (2002) (42)
- Hidden Common Cause Relations in Relational Learning (2007) (41)
- Variational Gaussian Dropout is not Bayesian (2017) (41)
- Gaussian Process Vine Copulas for Multivariate Dependence (2013) (41)
- A Probabilistic Model for Dirty Multi-task Feature Selection (2015) (39)
- Distributed Inference for Dirichlet Process Mixture Models (2015) (39)
- Human Activity Recognition by Combining a Small Number of Classifiers (2016) (39)
- Graphical Models for Inference with Missing Data (2014) (38)
- The variational Kalman smoother (2001) (38)
- Computation and psychophysics of sensorimotor integration (1996) (38)
- A nonparametric variable clustering model (2012) (37)
- Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction (2006) (37)
- Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data (2015) (37)
- Compact approximations to Bayesian predictive distributions (2005) (36)
- Gaussian Process Volatility Model (2014) (36)
- Dynamic Covariance Models for Multivariate Financial Time Series (2013) (35)
- Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes (2017) (34)
- Hierarchical Non-linear Factor Analysis and Topographic Maps (1997) (34)
- Latent-Space Variational Bayes (2008) (34)
- Correlated Non-Parametric Latent Feature Models (2009) (34)
- Magnetic Hamiltonian Monte Carlo (2016) (33)
- Scaling the Indian Buffet Process via Submodular Maximization (2013) (33)
- Graphical models: parameter learning (2002) (33)
- Identifying Protein Complexes in High-Throughput Protein Interaction Screens Using an Infinite Latent Feature Model (2005) (32)
- Warped Mixtures for Nonparametric Cluster Shapes (2012) (32)
- Bayesian Learning of Sum-Product Networks (2019) (31)
- The EM-EP algorithm for Gaussian process classification (2003) (31)
- Output-Space Predictive Entropy Search for Flexible Global Optimization (2016) (30)
- Automatic Bayesian Density Analysis (2018) (30)
- Nested sampling for Potts models (2005) (29)
- Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models (2003) (29)
- Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning (2011) (28)
- Automatic Discovery of the Statistical Types of Variables in a Dataset (2017) (28)
- Mixture models for learning from incomplete data (1997) (28)
- Variational Inference for Nonparametric Multiple Clustering (2010) (27)
- Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices (2014) (26)
- Probabilistic Deep Learning using Random Sum-Product Networks (2018) (26)
- Choosing a Variable to Clamp: Approximate Inference Using Conditioned Belief Propagation (2009) (26)
- Pitman-Yor Diffusion Trees (2011) (25)
- Probabilistic graphical models for semi-supervised traffic classification (2010) (25)
- Dirichlet Process Mixture Models for Verb Clustering (2008) (25)
- Archipelago: nonparametric Bayesian semi-supervised learning (2009) (25)
- The Block Diagonal Infinite Hidden Markov Model (2009) (25)
- Resource-Efficient Neural Networks for Embedded Systems (2020) (24)
- Turing: Composable inference for probabilistic programming (2018) (24)
- Lost Relatives of the Gumbel Trick (2017) (24)
- A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views (2012) (24)
- Analogical Reasoning with Relational Bayesian Sets (2007) (24)
- The Mondrian Kernel (2016) (23)
- A new approach to data driven clustering (2006) (23)
- Active Learning for Interactive Visualization (2013) (23)
- Outlier Robust Gaussian Process Classification (2008) (23)
- Probabilistic Meta-Representations Of Neural Networks (2018) (23)
- Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering (2015) (22)
- Gene function prediction from synthetic lethality networks via ranking on demand (2010) (22)
- Face Recognition Based on Separable Lattice HMMS (2006) (22)
- Advances in neural information processing systems : proceedings of the ... conference (1989) (22)
- Pitfalls in the use of Parallel Inference for the Dirichlet Process (2014) (22)
- Particle Gibbs for Infinite Hidden Markov Models (2015) (22)
- Bayesian two-sample tests (2009) (21)
- Robust Estimation of Local Genetic Ancestry in Admixed Populations Using a Nonparametric Bayesian Approach (2012) (21)
- Modeling genetic regulatory networks using gene expression profiling and state space models (2005) (21)
- Functional programming for modular Bayesian inference (2018) (19)
- Choosing a Variable to Clamp (2009) (19)
- Determinantal clustering process - a nonparametric Bayesian approach to kernel based semi-supervised clustering (2013) (19)
- Accelerating Bayesian Hierarchical Clustering of Time Series Data with a Randomised Algorithm (2013) (19)
- Extensions of Gaussian processes for ranking: semi-supervised and active learning (2005) (19)
- Gaussian Processes for time-marked time-series data (2012) (19)
- MFDTs: mean field dynamic trees (2000) (18)
- Tree-Structured Stick Breaking Processes for Hierarchical Data (2010) (18)
- Antithetic and Monte Carlo kernel estimators for partial rankings (2018) (17)
- Quantifying mismatch in Bayesian optimization (2016) (17)
- Randomized algorithms for fast Bayesian hierarchical clustering (2005) (17)
- Optimal model inference for Bayesian mixture of experts (2000) (16)
- Bayesian Inference for Gaussian Mixed Graph Models (2006) (16)
- Modelling biological responses using gene expression profiling and linear dynamical systems (2001) (16)
- Bayes rule in perception, action and cognition (2004) (16)
- Unifying linear dimensionality reduction (2014) (16)
- Testing a Bayesian Measure of Representativeness Using a Large Image Database (2011) (16)
- AdvancedHMC.jl: A robust, modular and e cient implementation of advanced HMC algorithms (2019) (16)
- Entropy and Mutual Information (15)
- Scalable Discrete Sampling as a Multi-Armed Bandit Problem (2015) (15)
- Conditional Graphical Models (2007) (15)
- General Latent Feature Models for Heterogeneous Datasets (2017) (15)
- A reversible infinite HMM using normalised random measures (2014) (15)
- On Modern Deep Learning and Variational Inference (2015) (14)
- Scaling the iHMM: Parallelization versus Hadoop (2010) (14)
- Unsupervised Many-to-Many Object Matching for Relational Data (2016) (13)
- Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data (2013) (13)
- Non-parametric Bayesian Methods (2006) (13)
- Bayesian nonparametric latent feature models (with discussion) (2006) (13)
- Model Reductions for Inference: Generality of Pairwise, Binary, and Planar Factor Graphs (2013) (13)
- State of the Journal (2011) (13)
- Learning about protein hydrogen bonding by minimizing contrastive divergence (2006) (13)
- Forward dynamic models in human motor control: Psychophysical evidence (1994) (12)
- The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models (2013) (12)
- Relational Learning and Network Modelling Using Infinite Latent Attribute Models (2015) (12)
- Temporal processing with connectionist networks (1991) (12)
- The Status of Structural Genomics Defined Through the Analysis of Current Targets and Structures (2003) (11)
- Probabilistic models for data combination in recommender systems (2008) (11)
- The Random Forest Kernel and creating other kernels for big data from random partitions (2014) (11)
- Computational neuroscience: Building blocks of movement (2000) (11)
- Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic (2001) (11)
- A kernel method for unsupervised structured network inference (2009) (11)
- Flexible Martingale Priors for Deep Hierarchies (2012) (11)
- One-Shot Learning in Discriminative Neural Networks (2017) (11)
- A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response (2013) (10)
- Fast online anomaly detection using scan statistics (2010) (10)
- An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process (2015) (10)
- Bayesian Structured Prediction Using Gaussian Processes (2013) (10)
- GPstruct: Bayesian Structured Prediction Using Gaussian Processes (2015) (10)
- A Tutorial on Gaussian Processes (or why I don't use SVMs) (2011) (10)
- Second-Order Latent-Space Variational Bayes for Approximate Bayesian Inference (2008) (9)
- Probabilistic Models for Unsupervised Learning (1999) (9)
- Active learning with mixture models (1997) (9)
- Stick-breaking construction for the Indian buffet (2007) (9)
- A Generative Model of Vector Space Semantics (2013) (9)
- Kernel conditional graphical models (2007) (9)
- Tree-Based Inference for Dirichlet Process Mixtures (2009) (9)
- Branch-recombinant Gaussian processes for analysis of perturbations in biological time series (2018) (9)
- Variational Infinite Hidden Conditional Random Fields (2015) (9)
- Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures (2013) (9)
- Proceedings of the 27th International Conference on Neural Information Processing Systems (2014) (9)
- Bayesian Learning in Undirected Graphical Models (2003) (9)
- Factorial Mixture of Gaussians and the Marginal Independence Model (2009) (8)
- Generalized Method-of-Moments for Rank Aggregation (2013) (8)
- Message passing algorithms for dirichlet diffusion trees (2011) (8)
- Ergodic Measure Preserving Flows (2018) (8)
- A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains (2011) (8)
- An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process (2015) (8)
- Bayesian Time Series Models: Nonparametric hidden Markov models (2011) (7)
- Gaussian process regression with mismatched models (2002) (7)
- Beta Diffusion Trees (2014) (7)
- Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering (2013) (7)
- A Bayesian approach to modelling uncertainty in gene expression clusters (2002) (7)
- General Table Completion using a Bayesian Nonparametric Model (2014) (7)
- Computational Structure of coordinate transformations: A generalization study (1994) (6)
- Imitation networks: Few-shot learning of neural networks from scratch (2018) (6)
- Bayesian Support Vector Machines for Feature Ranking and Selection (2006) (6)
- Variational Bayesian Learning (2000) (6)
- A Simple and General Exponential Family Framework for Partial Membership and Factor Analysis (2014) (6)
- Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications (2014) (6)
- Expectation Propagation for Infinite Mixtures (2017) (6)
- Ranking Relations using Analogies in Biological and Information Networks (2009) (6)
- Active Learning for Constrained Dirichlet Process Mixture Models (2010) (6)
- Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs (2014) (6)
- Affective robotics: Human motion and behavioural inspiration for safe cooperation between humans and humanoid assistive robots (2011) (5)
- Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series (2009) (5)
- Bayesian Generalised Ensemble Markov Chain Monte Carlo (2016) (5)
- 6 LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION – MAXIMIZATION ALGORITHM (2001) (5)
- Protein secondary structure prediction using sigmoid belief networks to parameterize segmental semi-Markov models (2004) (5)
- The Dynamic Beamformer (2011) (4)
- Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada] (2001) (4)
- General Latent Feature Modeling for Data Exploration Tasks (2017) (4)
- Improving PPM with Dynamic Parameter Updates (2015) (4)
- Sublinear Approximate Inference for Probabilistic Programs (2014) (4)
- Expectation-conjugate gradient: An alternative to EM (2002) (4)
- Bayesian inference on random simple graphs with power law degree distributions (2017) (4)
- Sparse Parametric Gaussian Processes (2005) (4)
- 27th Annual Conference on Neural Information Processing Systems 2013: December 5-10, Lake Tahoe, Nevada, USA (2014) (4)
- Nonparametric Bayesian inference of transcriptional branching and recombination identifies regulators of early human germ cell development (2017) (4)
- Computational principles of multisensory integration: studies of adaptation to novel visuo-auditory remappings (1995) (3)
- Bayesian Methods for Artificial Intelligence and Machine Learning (2008) (3)
- Bayesian inference of transcriptional branching identifies regulators of early germ cell development in humans (2018) (3)
- Accelerated Gibbs sampling for the Indian buffet process (2009) (3)
- Subsampling-Based Approximate Monte Carlo for Discrete Distributions (2015) (3)
- Reconstructing Transcriptional Networks Using Gene Expression Profiling and Bayesian State-Space Models (2007) (3)
- Adaptation to switching force fields (2001) (3)
- A dependent partition-valued process for multitask clustering and time evolving network modelling (2013) (3)
- Motor learning models (2006) (3)
- Variational Measure Preserving Flows (2018) (2)
- Automating machine learning (2016) (2)
- Propagating uncertainty in POMDP value iteration with Gaussian processes (2004) (2)
- Message Passing Algorithms for the Dirichlet Diffusion Tree (2011) (2)
- Simultaneous and Mapping With Sparse Extended Information (2014) (2)
- A Birth-Death Process for Feature Allocation (2017) (2)
- The Block Diagonal Infinite Hidden Markov Model (2009) (2)
- Nonparametric Probabilistic Modelling (2012) (2)
- How machines learned to think statistically (2015) (2)
- Slice Sampling for Probabilistic Programming (2015) (2)
- approach to modelling Bayesian non-parametrics and the probabilistic (2013) (2)
- Scaling in a hierarchical unsupervised network (1999) (2)
- Interpretable Continual Learning (2018) (2)
- Efficient Bayesian hierarchical clustering for gene expression data (2006) (1)
- Nonparametric Bayesian Clustering via Infinite Warped Mixture Models (2012) (1)
- One Hidden Layer Linear Networks and Canonical Correlations (2003) (1)
- Classification using log Gaussian Cox processes (2014) (1)
- Pathological Properties of Deep Bayesian Hierarchies (2011) (1)
- Bayesian Hidden Markov models and extensions (2010) (1)
- Interpretability Constraints and Trade-offs in Using Mixed Membership Models (2014) (1)
- DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models (2020) (1)
- Efficient and Robust Machine Learning for Real-World Systems (2018) (1)
- Degrees of freedom and hand synergies in manipulation tasks (2000) (1)
- Maps, Modules, and Internal Models in Human Motor Control (2000) (1)
- Real-time control of radiofrequency ablation by three-dimensional echo decorrelation imaging (2020) (1)
- Beta diffusion trees and hierarchical feature allocations (2014) (1)
- IEEE Transactions on Pattern Analysis and Machine Intelligence: Editor's note (2010) (1)
- Pattern classification using a mixture of factor analyzers (1999) (1)
- Modelling Input Varying Correlations between Multiple Responses (2012) (1)
- Gender Classification with Bayesian Kernel Methods (2006) (1)
- Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects (2020) (1)
- Representative Subgraph Sampling using Markov Chain Monte Carlo Methods (2008) (1)
- Partial Membership and Factor Analysis (2014) (1)
- Proceedings, Twenty-Fourth International Conference on Machine Learning (2007) (1)
- A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models (2015) (1)
- Neural Adaptive Sequential Monte Carlo Supplementary Material (2015) (1)
- A Bayesian network approach to protein fold recognition (1998) (1)
- Time for bayes: comments to Amari and Kohonen (1999) (0)
- The training algorithm based on variational approximation for separable 2D-HMM (2003) (0)
- Chapter 9 The Automatic Statistician (2018) (0)
- MCMC parameter learning in spatial statistics (2006) (0)
- U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models (2005) (0)
- Lecture 6: Hierarchical and Nonlinear Models (2005) (0)
- Endorsed By: Acl Siglex Acl Sigsem a Regression Model of Adjective-noun Compositionality in Distributional Semantics Semantic Composition with Quotient Algebras Expectation Vectors: a Semiotics Inspired Approach to Geometric Lexical-semantic Representation Sketch Techniques for Scaling Distributiona (0)
- Unsupervised learning of sensory-motor synergies (2003) (0)
- Markov Beta Processes for Time Evolving Dictionary Learning (2016) (0)
- Scaling in a Hierarchical Unsupervised Network 1 (2000) (0)
- Computational Models of SensorimotorIntegrationZoubin Ghahramani (1997) (0)
- Identifying protein complexes from high-throughput protein interaction screens (2007) (0)
- Contrastive Learning Using Spectral Methods (2013) (0)
- (Invited Talk) Bayesian Hidden Markov Models and Extensions (2010) (0)
- Lecture 6: Graphical Models: Learning (2010) (0)
- Appendix : Variational Bayesian dropout : pitfalls and fixes (2018) (0)
- Edinburgh Explorer Denotational validation of higher-order Bayesian inference (2017) (0)
- Scalingin a Hierar chical UnsupervisedNetwork (1999) (0)
- [4] N. Friedman, M. Linial, I. Nachman, and D. Pe’er. Using Bayesian networks (2009) (0)
- Bayesian Methods for Unsupervised Learning (2003) (0)
- Face recognition based on separate lattice HMMs (2006) (0)
- Editorial (2011) (0)
- Weakly supervised collective feature learning from curated media (2018) (0)
- Baysian Segmental Models for Protein Secondary Structure and Contact Map Prediction (2006) (0)
- Assignment 5 : Variational and Sampling Methods Unsupervised Learning (2003) (0)
- Analysis of three-dimensional echo decorrelation and integrated backscatter imaging during ex vivo radiofrequency ablation (2020) (0)
- Gender Detection using Machine Learning Techniques and Delaunay Triangulation (2016) (0)
- Editor's Note (2011) (0)
- Editor’s Note (2010) (0)
- Classical and Bayesian approaches to reconstructing genetic regulatory networks (2004) (0)
- A neural network for learning how to parse tree adjoining grammars (1990) (0)
- Çò Ëøöù Blockinøùööö Îöööøøóòòð Ôôöóüüññøøóò× (2002) (0)
- A Bayesian approach to information retrieval from sets of items (2006) (0)
- Expectation propagation for infinite mixtures ( Extended abstract ) (2003) (0)
- Adaptative feedback control for non-stationary dynamics (2001) (0)
- Bayesian hidden markov models and extensions: Invited talk (2010) (0)
- STAT 538 Handout 5 February 21 , 2008 Variational bounds for graphical models (0)
- Multiple linear controllers for nonlinear and nonstationary dynamics (2000) (0)
- Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007 (2007) (0)
- Expert Finding using discriminative infinite Hidden Markov Model (2015) (0)
- Outlier Robust Learning Algorithm for Gaussian Process Classification (2007) (0)
- A theory of optimal motor-sensory primitives (2001) (0)
- Bayesian and L$_\mathbf{1}$ Approaches to Sparse Unsupervised Learning (2011) (0)
- Interaction between JWH133-induced Antinociception and two extreme Doses of Celecoxib (2009) (0)
- Probabilistic Models for Preference Learning (2011) (0)
- Introduction of New Associate Editors (2008) (0)
- Bayesian time series classification (2002) (0)
- Latent Variable Time Series Models (2004) (0)
- A Bayesian Unsupervised Learning Algorithm that Scales (2007) (0)
- The coding of movements in primary motor cortex: a TMS study (2000) (0)
- Dirichlet Fragmentation Processes (2015) (0)
- Editor's Note (2010) (0)
- Expectation propagation for infinite mixtures (Technical Report) (2003) (0)
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