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Machine Learning Reading Group @ CUED
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Reading Group in Division F of the Cambridge University Engineering Department. Run by Zoubin Ghahramani and Carl Rasmussen. This reading group is also called 5CF6: Machine Learning Research and Communication Club. If you have a question about this list, please contact: Zoubin Ghahramani; Carl Edward Rasmussen; Konstantina Palla; David Duvenaud; Colorado Reed. If you have a question about a specific talk, click on that talk to find its organiser. 3 upcoming talks and 163 talks in the archive. Bayesian Nonparametrics in Real-World Applications: Statistical Machine Translation and Language Modelling on Big Datasets
Sparsity: Beyond L1
Reproducing Kernel Hilbert Spaces in Non-parametric Statistics
An Introduction to Sum Product Networks
Bayesian Reinforcement Learning
Probabilistic Programming
Machine Learning in Speech Recognition
Fragmentation Coagulation
Conditional Density Estimation
RCC Planning
Discrete Optimization
Advanced Sampling
NIPS Recap
NIPS Recap
Bayesian and Gradient Reinforcement Learning
Spectral Clustering
Modern Neural Networks: the Hinton Camp
Distributional compositional models of semantics
Completely Random Measures in Bayesian Nonparametrics
Model selection in a large compositional spacenote: the first 45 mins will be planning and the talk will start at around 3:15pm
Dependent normalized random measures
GP-BUCB for Spinal Cord Injury Therapy: Batch Active Learning with Applications
On Data (In-)Dependent Hashing
Poisson Processes: Applications in Machine Learning
A rough guide to the Aldous-Hoover representation theorem for exchangeable arrays
Title to be confirmed
Dirichlet Process Mixture Models and Bayesian Nonparametric Density Estimation
"Structured sparsity and convex optimization"
Financial Problems tractable to Machine Learning Methods
A Predictive Study of Bayesian Nonparametric Regression Models
Title to be confirmed
Weighted Finite-state Automata
Active Learning
Topic Modelling
Structural Learning of Dynamic Bayesian Networks
Extended ensemble Monte Carlo
"Symmetry and sufficiency"
Information bottleneckPlease note the change in time. This RCC will take place at 15:00-16.30pm.
Ensemble Methods in Machine Learning
Approximate Inference in Gaussian Process Models
Proper local scoring rules
Hyper and structural Markov laws for graphical models
Connections between Gaussian Process Regression, Kalman filtering and RTS Smoothing
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
Expectation Propagation for POMDP Spoken Dialogue Models
Exchangeability
Deep Belief Networks for Phone Recongition
The effect of normalization -- a case study in speech synthesis
Post-NIPS Highlight session
Optimal weighted nearest neighbour classifiers
Kernel Methods
Machine Learning techniques in computer vision applications
Infinite multiple relational models for complex networks
Completely Random Measures
Submodularity for Machine Learning
NIPS Highlight Session
Poisson Processes
Convex Optimisation
An Introduction to Bayesian Statistics
Cancelled: No RCCCancelled
Herding or a '3rd way to learn'
Advanced Scientific Programming in Python
Unsupervised Grammar Induction
Structured Learning and Structural SVMs
Conditional Random Fields : Theory and Application
Redirected to Rob Nowak, LR12Redirected
Topics in Statistical Machine Translation
Title to be confirmed
Information RetrievalRoom changed
Numerical Linear AlgebraRoom changed
Bundle methods and its application in machine learning
Sparsification for Gaussian Processes for Regression
Title to be confirmed
CancelledCancelled
CancelledCanceled
Title to be confirmed
Semi supervised learning
Variational inference in graphical models: The view from the marginal polytope
The Fractional Belief Propagation menace
(Canceled) A Causal Calculus for Statistical ResearchCanceled
RCC Planning Meeting
Unbounded-depth hierarchical Pitman-Yor processes
Learning rates in Bayesian nonparametrics
Bayesian Agglomerative Clustering with Coalescents
Machine Learning RCC - Bayesian Agglomerative Clustering with Coalescents, Teh, Daumé and Roy, NIPS 2007
Filtering of Noisy Time-Series Data
Speed Reviewing
High-dimensional variable selection via sure independence screening
Ensemble Methods in Machine Learning
Structured prediction using energy-based models
Technical Writing II
Hessian-based Markov-Chain Monte Carlo Algorithms
Message PassingNote unusual time
Slice Sampling
Technical Writing
Divergence measures and message passing
The IBM approach to speech separation
Parsing Images the UCLA Way
Hilbert space embedding of probability distributions
Bregman Divergences and Machine Learning
The Convex Concave Procedure
Hybrids of Generative and Discriminative Models (Cont)
Large Margin Training of Hidden Markov Models
Hybrid of Generative and Discriminative Models
Divergence measures and message passing - CANCELLEDCanceled
Distributed Computing for Machine Learning
A Hierarchical Bayesian Language Model based on Pitman-Yor Processes
Probabalistic Inference for solving (PO)MDPs
NIPS Offline Conference
An Introduction to Relational Learning
Bayesian Reinforcement Learning in Continuous POMDPs
Cluster Analysis of Heterogeneous Rank Data
Gaussian Process Latent Variable Models
Point Process Intensity Estimation with GP's
Knows What It Knows: A Framework For Self-Aware Learning
Approximating the Kullback-Leibler Divergence Between GMMs
To Naive Bayes or to Logistically Regress: That is the Question
CANCELLEDCANCELLED
Condition Monitoring
Levy Processes
The War on Loops
Variational Bayesian Mixtures of Gaussians
Stochastic integration and Ito's lemma
Continuous Time Bayesian Networks
'All of Nonparametric Statistics'
The Information Bottleneck / Dynamic Kernals for speaker verification
Probabilistic Matrix Factorization/Deep Belief Nets
Title to be confirmed
Spectral methods
Active Learning
Bayesian Adaptive Inference and Adaptive Training
Hierarchical Dirichlet Processes
Bayesian Reinforcement Learning
Transductive and Semi-Supervised Learning
Variational inference and exponential families
Sparse Bayesian Linear Models
Autonomous Agents under Operational Closure
Kingman's coalescent, non-parametric Bayesian agglomerative clustering, and ICML 2007
Sensible priors from finite linear modelsNote unusual time
Statistical Models for Partial Membership
Infinite ICA and Information Retrieval
Overlapping Clusters and 4th-year Projects
What can Gaussian Processes do for Reinforcement Learning?
System Conditioning vs Explicit Bayes Inference, and Collaborative LDA
Affinity Propagation and Hierarchical Beta ProcessesRoom changed, Note unusual time
Optimal Learning
Gaussian Approximations for Binary Gaussian Process Classification, and Hidden Topic Markov Models
Function Approximation in MDPs
Change Point Problems in Linear Dynamical SystemsPostponed (originally April 5)
GP-LVMs
Topics in Convex Optimisation
An Introduction to Generalized Ensemble MCMC for Machine Learning
Some NIPS papers
Universal Artificial Intelligence, and Probability Monads
Neural Networks
Latent Dirichlet Allocation and Dirichlet Diffusion Trees
Non-parametric mixture models
Active Learning and Experimental DesignNote unusual time
Collaborative Filtering
On Choosing Priors
Partially Observable Markov Decision Processes (POMDPs)
Nonlinear Dimensionality Reduction
Machine Learning Reading Group in Engineering Department
Please see above for contact details for this list. |
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Yarin Gal (University of Cambridge).
Thursday 16 May 2013, 15:00-16:30