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AdriΓ Garriga Alonso (University of Cambridge).
Wednesday 13 February 2019, 13:45-15:15
Engineering Department, CBL Room 438
Contact: No managers listed
0 upcoming talks View 342 archived talks
Machine Learning Reading Group @ CUED
Logical Uncertainty
Machine Learning Reading Group @ CUED
Stein Discrepancy
Machine Learning Reading Group @ CUED
Neural Ordinary Differential Equations
Machine Learning Reading Group @ CUED
Natural gradient in deep neural networks
Machine Learning Reading Group @ CUED
Graph Neural Networks
Machine Learning Reading Group @ CUED
Defending Against Adversarial Attacks
Machine Learning Reading Group @ CUED
Reinforcement Learning and Control as Probabilistic Inference
Machine Learning Reading Group @ CUED
Neural Networks and Natural Language Processing
Machine Learning Reading Group @ CUED
Information Theory, Codes, and Compression
Machine Learning Reading Group @ CUED
Kernel Mean Embeddings
Computational and Biological Learning Seminar Series
Control, inference and learning
Machine Learning Reading Group @ CUED
Differential Privacy
Machine Learning Reading Group @ CUED
Probabilistic Numerics
Machine Learning Reading Group @ CUED
NIPS 2016 papers in 5mins
Machine Learning Reading Group @ CUED
Symmetry in Statistical Models
Machine Learning Reading Group @ CUED
Bayesian Optimization
Machine Learning Reading Group @ CUED
deep generative models
Machine Learning Reading Group @ CUED
Sketching methods
Machine Learning Reading Group @ CUED
A crash-course on Bayesian Reinforcement Learning
Computational and Biological Learning Seminar Series
Internal models and the neural control of prey interception
Machine Learning Reading Group @ CUED
Partially Observable Markov Decision Processes (POMDPs)
Machine Learning Reading Group @ CUED
Learning polynomials with Neural Networks
Computational and Biological Learning Seminar Series
A role for circadian rhythms in maintaining neuronal properties
Machine Learning Reading Group @ CUED
An introduction to Bayesian nonparametrics: some inference schemes for infinite mixture models
Computational and Biological Learning Seminar Series
From Sensation to Conception: Theoretical Perspectives on Multisensory Perception and Cross-Modal Transfer
Machine Learning Reading Group @ CUED
Topics in Expectation Propagation
Machine Learning Reading Group @ CUED
Deep Q-Learning and AlphaGo
Machine Learning Reading Group @ CUED
inference failures in probabilistic programming
Computational and Biological Learning Seminar Series
Perspectives on Designing Optimal User Interfaces
Machine Learning Reading Group @ CUED
Variational Methods and Compressed Sensing
Machine Learning Reading Group @ CUED
Fast Fusion of Multi-band Images: A Powerful Tool for Super-resolution
Computational and Biological Learning Seminar Series
Approximate Message Passing Algorithms
Machine Learning Reading Group @ CUED
Probabilistic programming
Machine Learning Reading Group @ CUED
Learning by learning rich generative models
Machine Learning Reading Group @ CUED
Natural gradient descent and variational inference
Machine Learning Reading Group @ CUED
Differentiable Data Structures and (if we have time) POMDPs
β οΈ NOTE Topic Change
Machine Learning Reading Group @ CUED
Random projection ensemble classification
Machine Learning Reading Group @ CUED
Deep learning for time series
Machine Learning Reading Group @ CUED
Stochastic discrete integration
Machine Learning Reading Group @ CUED
Stochastic optimization and adaptive learning rates
Machine Learning Reading Group @ CUED
Logic, Theorem Proving, and Probabilistic Programming
Machine Learning Reading Group @ CUED
Connections between kernels, GPs, and NNs
Machine Learning Reading Group @ CUED
Detecting paraphrases using recursive autoencoders
Machine Learning Reading Group @ CUED
Bayesian optimization and its applications
Machine Learning Reading Group @ CUED
LP relaxations for MAP inference
Machine Learning Reading Group @ CUED
Convolutional Neural Networks
Machine Learning Reading Group @ CUED
Hessian-free Optimisation for Neural Networks
Machine Learning Reading Group @ CUED
Scalable MCMC
Machine Learning Reading Group @ CUED
Causal Inference
Machine Learning Reading Group @ CUED
Gaussian Process Models for Time Series
Machine Learning Reading Group @ CUED
Informational Geometry
Machine Learning Reading Group @ CUED
Deep Gaussian Processes
Machine Learning Reading Group @ CUED
Combinatorial Stochastic Processes in Bayesian Nonparametrics
Machine Learning Reading Group @ CUED
Variance Reduction Techniques and Stochastic Optimisation for Monte Carlo
Machine Learning Reading Group @ CUED
Transfer Learning
Machine Learning Reading Group @ CUED
Advanced HMC
Machine Learning Reading Group @ CUED
Active Learning
Machine Learning Reading Group @ CUED
Deep Probabilistic Models (Wake/Sleep)
Machine Learning Reading Group @ CUED
PAC Bayes
Machine Learning Reading Group @ CUED
LSTM and Recurrent Neural Networks
Machine Learning Reading Group @ CUED
Automatic Differentiation with Theano
Machine Learning Reading Group @ CUED
State Space Abstraction for Reinforcement Learning
Machine Learning Reading Group @ CUED
Kernel Embedding for Distributions
Machine Learning Reading Group @ CUED
Unsupervised Representation Learning
Machine Learning Reading Group @ CUED
ββTime-varying dynamic Bayesian network reconstruction with information sharingβ
Machine Learning Reading Group @ CUED
Advanced Gaussian Process approximation methods
Machine Learning Reading Group @ CUED
Statistical model criticism
Machine Learning Reading Group @ CUED
Functional Programming
Machine Learning Reading Group @ CUED
The Bernoulli Factory problem and some connections with Computable Analysis
Machine Learning Reading Group @ CUED
Neural Network Language Modelling
Machine Learning Reading Group @ CUED
Loopy belief propagation
Machine Learning Reading Group @ CUED
An introduction to Sequential Monte Carlo
Machine Learning Reading Group @ CUED
Probabilistic Data Structures and Algorithms
Machine Learning Reading Group @ CUED
Information Theory and Method of Types: Channels, Quantizers, and Divergences
Machine Learning Reading Group @ CUED
Integrated Nested Laplace Approximation (INLA)
Machine Learning Reading Group @ CUED
Spectral Learning
Machine Learning Reading Group @ CUED
Random Projections
Machine Learning Reading Group @ CUED
Herding: Driving Deterministic Dynamics To Learn And Sample Probabilistic Models
Computational and Biological Learning Seminar Series
Assembly and function of motor circuits for automatic and goal oriented movements
Computational and Biological Learning Seminar Series
A talk of two distinct parts: 1) a parametric empirical Bayesian approach to integration of fMRI, EEG and MEG data, 2) prediction error in episodic memory encoding
Machine Learning Reading Group @ CUED
Bayesian and Bandit Optimization
Machine Learning Reading Group @ CUED
Random Forests: One tool for all your problems.
Machine Learning Reading Group @ CUED
Modelling Nonlinear Dynamical Systems
Machine Learning Reading Group @ CUED
Adaptive Hamiltonian-based MCMC samplers
Machine Learning Reading Group @ CUED
Bayesian Nonparametrics in Real-World Applications: Statistical Machine Translation and Language Modelling on Big Datasets
Machine Learning Reading Group @ CUED
Sparsity: Beyond L1
Machine Learning Reading Group @ CUED
Reproducing Kernel Hilbert Spaces in Non-parametric Statistics
Machine Learning Reading Group @ CUED
An Introduction to Sum Product Networks
Machine Learning Reading Group @ CUED
Bayesian Reinforcement Learning
Machine Learning Reading Group @ CUED
Probabilistic Programming
Machine Learning Reading Group @ CUED
Machine Learning in Speech Recognition
Machine Learning Reading Group @ CUED
Fragmentation Coagulation
Machine Learning Reading Group @ CUED
Conditional Density Estimation
Machine Learning Reading Group @ CUED
RCC Planning
Machine Learning Reading Group @ CUED
Discrete Optimization
Machine Learning Reading Group @ CUED
Advanced Sampling
Machine Learning Reading Group @ CUED
NIPS Recap
Machine Learning Reading Group @ CUED
NIPS Recap
Machine Learning Reading Group @ CUED
Bayesian and Gradient Reinforcement Learning
Machine Learning Reading Group @ CUED
Spectral Clustering
Machine Learning Reading Group @ CUED
Modern Neural Networks: the Hinton Camp
Machine Learning Reading Group @ CUED
Distributional compositional models of semantics
Machine Learning Reading Group @ CUED
Completely Random Measures in Bayesian Nonparametrics
Machine Learning Reading Group @ CUED
Model selection in a large compositional space
β οΈ note: the first 45 mins will be planning and the talk will start at around 3:15pm
Machine Learning Reading Group @ CUED
Dependent normalized random measures
Computational and Biological Learning Seminar Series
Optimal integration of top-down and bottom-up uncertainty in humans, monkeys, and neural networks
Machine Learning Reading Group @ CUED
GP-BUCB for Spinal Cord Injury Therapy: Batch Active Learning with Applications
Machine Learning Reading Group @ CUED
On Data (In-)Dependent Hashing
Machine Learning Reading Group @ CUED
Poisson Processes: Applications in Machine Learning
Machine Learning Reading Group @ CUED
A rough guide to the Aldous-Hoover representation theorem for exchangeable arrays
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Dirichlet Process Mixture Models and Bayesian Nonparametric Density Estimation
Machine Learning Reading Group @ CUED
"Structured sparsity and convex optimization"
Computational and Biological Learning Seminar Series
Optimal encoding and decoding in sensory populations
Machine Learning Reading Group @ CUED
Financial Problems tractable to Machine Learning Methods
Machine Learning Reading Group @ CUED
A Predictive Study of Bayesian Nonparametric Regression Models
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Weighted Finite-state Automata
Machine Learning Reading Group @ CUED
Active Learning
Machine Learning Reading Group @ CUED
Topic Modelling
Machine Learning Reading Group @ CUED
Structural Learning of Dynamic Bayesian Networks
Machine Learning Reading Group @ CUED
Extended ensemble Monte Carlo
Machine Learning Reading Group @ CUED
"Symmetry and sufficiency"
Machine Learning Reading Group @ CUED
Information bottleneck
β οΈ Please note the change in time. This RCC will take place at 15:00-16.30pm.
Machine Learning Reading Group @ CUED
Ensemble Methods in Machine Learning
Machine Learning @ CUED
Bayesian Quadrature for Prediction and Optimisation
Machine Learning @ CUED
Efficient MCMC for Continuous Time Discrete State Systems
Machine Learning @ CUED
A Maximum Entropy Perspective on Spectral Dimensionality Reduction
Machine Learning Reading Group @ CUED
Approximate Inference in Gaussian Process Models
Machine Learning Reading Group @ CUED
Proper local scoring rules
Machine Learning @ CUED
Variational Inference for Non-Conjugate Models
Machine Learning Reading Group @ CUED
Hyper and structural Markov laws for graphical models
Machine Learning @ CUED
Not so naive Bayesian classification
Machine Learning @ CUED
Bayesian Nonparametrics: Latent Feature and Prediction Models, and Efficient Inference
Machine Learning @ CUED
Machine Learning Markets
Machine Learning @ CUED
Factored Shapes and Appearances for Parts-based Object Understanding AND Transformation Equivariant Boltzmann Machines
Machine Learning @ CUED
Optimal Reinforcement Learning for Gaussian Systems
Machine Learning @ CUED
Exclusive PΓ³lya Urns and their applications
Machine Learning @ CUED
Approximate Bayesian Inference for Large Scale Inverse Problems: A Computational Viewpoint
Machine Learning @ CUED
Some Practical Reflections on Graphical Models
Machine Learning @ CUED
Beyond Keyword Search: Discovering Relevant Scientific Literature
Machine Learning @ CUED
Graphical Models for Bandit Problems
Machine Learning @ CUED
Bayesian regression and classification with multivariate sparsifying priors
Machine Learning @ CUED
An FX trading system using adaptive reinforcement learning
β οΈ (rescheduled from May 24th)
Machine Learning @ CUED
Censored Exploration in Dark Pools
Machine Learning Reading Group @ CUED
Connections between Gaussian Process Regression, Kalman filtering and RTS Smoothing
Machine Learning @ CUED
An FX trading system using adaptive reinforcement learning
β οΈ RESCHEDULED FOR JUNE 7th
Machine Learning Reading Group @ CUED
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
Machine Learning Reading Group @ CUED
Expectation Propagation for POMDP Spoken Dialogue Models
Machine Learning @ CUED
Characterization of the Ewens-Pitman family of random partitions by a deletion property and a de Finetti-type theorem for exchangeable hierarchies
Machine Learning @ CUED
Nonlinear Dynamics of Learning
Machine Learning Reading Group @ CUED
Exchangeability
Machine Learning @ CUED
Exponential Conditional Volatility Models
Machine Learning Reading Group @ CUED
Deep Belief Networks for Phone Recongition
Machine Learning @ CUED
Challenges in implementing the Bayesian paradigm
Machine Learning @ CUED
Expectation Propagation in Sparse Linear Models with Spike and Slab Priors
Machine Learning @ CUED
Probabilistic matrix factorization for reconstruction of missing data
Machine Learning Reading Group @ CUED
The effect of normalization -- a case study in speech synthesis
Machine Learning @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Post-NIPS Highlight session
Machine Learning Reading Group @ CUED
Optimal weighted nearest neighbour classifiers
Machine Learning Reading Group @ CUED
Kernel Methods
Machine Learning Reading Group @ CUED
Machine Learning techniques in computer vision applications
Machine Learning @ CUED
Differential Geometric MCMC Methods
Machine Learning Reading Group @ CUED
Infinite multiple relational models for complex networks
Machine Learning @ CUED
Mining viral datasets
Machine Learning Reading Group @ CUED
Completely Random Measures
Machine Learning @ CUED
Universal Bayesian Agents: Theory and Applications
Machine Learning @ CUED
Machine learning in cancer research (a.k.a CRI meets CUED)
β οΈ (note new date)
Machine Learning @ CUED
Novi Quadrianto
Machine Learning Reading Group @ CUED
Submodularity for Machine Learning
Machine Learning @ CUED
Learning item trees for collaborative filtering with implicit feedback
Machine Learning @ CUED
Non-Smooth-Norm Image Reconstruction from Noisy Data
Machine Learning Reading Group @ CUED
NIPS Highlight Session
Machine Learning Reading Group @ CUED
Poisson Processes
Machine Learning @ CUED
Crowdsourcing data modelling
Machine Learning Reading Group @ CUED
Convex Optimisation
Machine Learning @ CUED
Short talks: Mixed Cumulative Distribution Networks; Nonparametric Bayesian community discovery in social networks; Expectation Propagation for Dirichlet Process Mixture Models
Machine Learning Reading Group @ CUED
An Introduction to Bayesian Statistics
Machine Learning Reading Group @ CUED
Herding or a '3rd way to learn'
Machine Learning Reading Group @ CUED
Advanced Scientific Programming in Python
Machine Learning Reading Group @ CUED
Unsupervised Grammar Induction
Machine Learning @ CUED
Searching for Knowledge Instead of Web Pages
Machine Learning @ CUED
Bayesian Inference with Kernels
Computational and Biological Learning Seminar Series
Continuous control of brain computer interfaces based on a covert spatial attention paradigm
Machine Learning @ CUED
On the Equivalence of Graph Cuts and Max-product Belief Propagation
Machine Learning @ CUED
Creating structured and ο¬exible models: some open problems
Machine Learning @ CUED
Efficient Bayesian analysis of multiple changepoint models
Machine Learning @ CUED
Learning Common Grammar from Multilingual Corpus / Online Multiscale Dynamic Topic Models
Machine Learning @ CUED
Scalable Parallel Computing with CUDA
Machine Learning @ CUED
Using transformed domains to sparsify Gaussian Processes
Machine Learning @ CUED
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Machine Learning @ CUED
Natural Conjugate Gradient Learning for Fixed-Form Variational Bayes
Machine Learning @ CUED
Sparse Factor Analysis Applied to Three Biological Problems
Machine Learning @ CUED
Using topic models to help cure cancer
Machine Learning Reading Group @ CUED
Structured Learning and Structural SVMs
Machine Learning @ CUED
Structured Prediction Cascades
Machine Learning @ CUED
Parametric Bandits, Query Learning, and the Haystack Dimension
Machine Learning Reading Group @ CUED
Conditional Random Fields : Theory and Application
Machine Learning Reading Group @ CUED
Topics in Statistical Machine Translation
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Bundle methods and its application in machine learning
Machine Learning Reading Group @ CUED
Sparsification for Gaussian Processes for Regression
Machine Learning @ CUED
Slice sampling with latent Gaussian models
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning @ CUED
Dynamic Network Tomography: Model, Algorithm, Theory, and Application
Machine Learning @ CUED
Subspace Codes for Adversarial Error-Correction in Network Coding
β οΈ short talk
Machine Learning @ CUED
Bayesian Inference in Networks of Queues
Machine Learning @ CUED
Stochastic Outlier Selection
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning Reading Group @ CUED
Semi supervised learning
Machine Learning Reading Group @ CUED
Variational inference in graphical models: The view from the marginal polytope
Machine Learning @ CUED
Mind Reading by Machine Learning: Optimal Experimental Design
β οΈ Short talk
Machine Learning Reading Group @ CUED
The Fractional Belief Propagation menace
Machine Learning Reading Group @ CUED
(Canceled) A Causal Calculus for Statistical Research
β οΈ Canceled
Machine Learning Reading Group @ CUED
RCC Planning Meeting
Machine Learning Reading Group @ CUED
Unbounded-depth hierarchical Pitman-Yor processes
Machine Learning Reading Group @ CUED
Learning rates in Bayesian nonparametrics
Machine Learning @ CUED
Indian Buffet Processes with Power-law Behaviour
Machine Learning @ CUED
Gaussian Processes for Active Data Selection, Faults, Changepoints and Sensor Selection
Machine Learning Reading Group @ CUED
Bayesian Agglomerative Clustering with Coalescents
Machine Learning @ CUED
KL control theory and decision making under uncertainty
Machine Learning Reading Group @ CUED
Machine Learning RCC - Bayesian Agglomerative Clustering with Coalescents, Teh, DaumΓ© and Roy, NIPS 2007
Machine Learning Reading Group @ CUED
Filtering of Noisy Time-Series Data
Machine Learning Reading Group @ CUED
Speed Reviewing
Machine Learning @ CUED
Information theoretic model selection in clustering
Machine Learning Reading Group @ CUED
High-dimensional variable selection via sure independence screening
Machine Learning Reading Group @ CUED
Ensemble Methods in Machine Learning
Machine Learning @ CUED
Motor Skills Learning for Robotics
Machine Learning Reading Group @ CUED
Structured prediction using energy-based models
Machine Learning Reading Group @ CUED
Technical Writing II
Machine Learning @ CUED
Coconut: Optimizing computations for machine learning
Machine Learning @ CUED
Convex Variational Bayesian Inference for Large Scale Generalized Linear Models
Machine Learning @ CUED
Computable Probability Theory
Machine Learning Reading Group @ CUED
Hessian-based Markov-Chain Monte Carlo Algorithms
Machine Learning Reading Group @ CUED
Slice Sampling
Machine Learning @ CUED
Using gradient descent for optimization and learning
Machine Learning Reading Group @ CUED
Technical Writing
Machine Learning Reading Group @ CUED
Divergence measures and message passing
Machine Learning Reading Group @ CUED
The IBM approach to speech separation
Machine Learning Reading Group @ CUED
Parsing Images the UCLA Way
Machine Learning Reading Group @ CUED
Hilbert space embedding of probability distributions
Computational and Biological Learning Seminar Series
Algorithms for Understanding Motor Cortical Processing and Neural Prosthetic Systems
Machine Learning @ CUED
Learning from Measurements in Exponential Families
Machine Learning Reading Group @ CUED
Bregman Divergences and Machine Learning
Machine Learning @ CUED
Shrinkage regression for multivariate inference with missing data, with an application to portfolio balancing
Machine Learning @ CUED
Quasi-linear Sensor Management
Machine Learning Reading Group @ CUED
The Convex Concave Procedure
Machine Learning Reading Group @ CUED
Hybrids of Generative and Discriminative Models (Cont)
Computational and Biological Learning Seminar Series
Mind reading by machine learning: an ideal observer based analysis of cognitive scientific experiments
Machine Learning @ CUED
Generalization in Learning
Machine Learning Reading Group @ CUED
Large Margin Training of Hidden Markov Models
Machine Learning Reading Group @ CUED
Hybrid of Generative and Discriminative Models
Machine Learning Reading Group @ CUED
Divergence measures and message passing - CANCELLED
β οΈ Canceled
Machine Learning @ CUED
Extending the Affinity Propagation Model
Machine Learning Reading Group @ CUED
Distributed Computing for Machine Learning
Machine Learning Reading Group @ CUED
A Hierarchical Bayesian Language Model based on Pitman-Yor Processes
Machine Learning @ CUED
The Block Diagonal Infinite Hidden Markov Model
Machine Learning @ CUED
Probabilistic Graph Models for Debugging Software
Machine Learning Reading Group @ CUED
Probabalistic Inference for solving (PO)MDPs
Machine Learning @ CUED
HMMs for Protein Sequencing from Mass Spectrometry Data
Machine Learning Reading Group @ CUED
NIPS Offline Conference
Machine Learning Reading Group @ CUED
An Introduction to Relational Learning
Machine Learning Reading Group @ CUED
Bayesian Reinforcement Learning in Continuous POMDPs
Machine Learning @ CUED
Hardness Ratios
Machine Learning Reading Group @ CUED
Cluster Analysis of Heterogeneous Rank Data
Machine Learning Reading Group @ CUED
Gaussian Process Latent Variable Models
Machine Learning @ CUED
Consensus finding, exponential models and infinite rankings
Machine Learning Reading Group @ CUED
Point Process Intensity Estimation with GP's
Machine Learning Reading Group @ CUED
Knows What It Knows: A Framework For Self-Aware Learning
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part III)
Machine Learning Reading Group @ CUED
Approximating the Kullback-Leibler Divergence Between GMMs
Machine Learning @ CUED
Efficient Sequential Monte Carlo Inference for Kingman's Coalescent
β οΈ Note unusual time
Machine Learning @ CUED
Context in human robot interaction
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part II)
Machine Learning Reading Group @ CUED
To Naive Bayes or to Logistically Regress: That is the Question
Machine Learning @ CUED
Foundations of Nonparametric Bayesian Methods (Part I)
Machine Learning @ CUED
Shared Segmentation of Natural Scenes using Dependent Pitman-Yor Processes
Machine Learning Reading Group @ CUED
Condition Monitoring
Machine Learning @ CUED
Spoken Dialogue Management
Machine Learning @ CUED
A Bayesian approach to language learning
Machine Learning @ CUED
Learning Bigrams from Unigrams
Machine Learning Reading Group @ CUED
Levy Processes
Machine Learning @ CUED
Nonparametric Bayesian Natural Language Model Domain Adaptation: A Hierarchical, Hierarchical Pitman-Yor Process Language Model
Machine Learning @ CUED
Bayesian approaches to autonomous Bayesian real-time learning
Machine Learning Reading Group @ CUED
The War on Loops
Machine Learning @ CUED
Non-negative matrix factorization with Gaussian process priors
Machine Learning @ CUED
Matrix Factorization and Relational Learning
Machine Learning @ CUED
Double Feature: Optimal Precoding for MIMO and Divergence Estimation for Continuous Distributions
Machine Learning @ CUED
Nonparametric Bayesian Learning of Switching Dynamical Systems
Machine Learning @ CUED
Message-passing inference on graphical models
Machine Learning Reading Group @ CUED
Variational Bayesian Mixtures of Gaussians
Machine Learning @ CUED
Bayesian analysis of complex biological systems
Machine Learning @ CUED
Variational inference for partially observed diffusion processes
Computational and Biological Learning Seminar Series
Seeing Patterns in Randomness: Irrational Superstition or Adaptive Behavior?
Machine Learning @ CUED
Assessing high-dimensional latent variable models
Machine Learning @ CUED
Identification of dynamical subpopulations, optimal experiment design and more
Machine Learning Reading Group @ CUED
Stochastic integration and Ito's lemma
Machine Learning @ CUED
H-Infinity Clustering
Machine Learning Reading Group @ CUED
Continuous Time Bayesian Networks
Machine Learning @ CUED
Modeling Behaviour in Economic Games using Game-Theoretic POMDPs
Machine Learning @ CUED
Talking with Robots: A Case Study in Architectures for Cognitive Robotics
Machine Learning @ CUED
Beam Sampling for Infinite Hidden Markov Models
Machine Learning @ CUED
Learning quantum physics
Machine Learning @ CUED
Convergence analysis of the EM algorithm and joint minimization of free energy
Machine Learning @ CUED
Discriminative Methods with Structure
Machine Learning Reading Group @ CUED
'All of Nonparametric Statistics'
Machine Learning @ CUED
A Bayesian approach to network modularity: inferring the structure and scale of modular networks
Machine Learning Reading Group @ CUED
The Information Bottleneck / Dynamic Kernals for speaker verification
Machine Learning @ CUED
Model selection and model order adaptation for clustering
Machine Learning @ CUED
Expectation Propagation, Experimental Design for the Sparse Linear Model
Machine Learning @ CUED
Biomedical Image Search
Machine Learning Reading Group @ CUED
Probabilistic Matrix Factorization/Deep Belief Nets
Machine Learning Reading Group @ CUED
Title to be confirmed
Machine Learning @ CUED
Stable distribution and data sketching
Machine Learning Reading Group @ CUED
Spectral methods
Machine Learning Reading Group @ CUED
Active Learning
Machine Learning @ CUED
Sparse Gaussian Process in Disease Mapping
Machine Learning Reading Group @ CUED
Bayesian Adaptive Inference and Adaptive Training
Machine Learning @ CUED
Gene Regulatory Network Inference: A Kernel-Based Learning Approach
Machine Learning Reading Group @ CUED
Hierarchical Dirichlet Processes
Machine Learning @ CUED
Clinical data based optimal STI strategies for HIV: a reinforcement learning approach
Machine Learning Reading Group @ CUED
Bayesian Reinforcement Learning
Machine Learning Reading Group @ CUED
Transductive and Semi-Supervised Learning
Machine Learning @ CUED
An overview of covariance operators in Hilbert space, and their applications
Machine Learning @ CUED
Optimal Control and Reinforcement Learning with Gaussian Process Models
Machine Learning Reading Group @ CUED
Variational inference and exponential families
Please see above for contact details for this list.