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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; Dr R.E. Turner; Isaac Reid; Andy Lin; Yichao Liang; ; jh2383; Xianda Sun. If you have a question about a specific talk, click on that talk to find its organiser. 0 upcoming talks and 399 talks in the archive. Scalable Sampling Using Annealed Algorithms
Are we making progress in unlearning?
Natural Experiments in NLP and Where to Find ThemZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Linear Attention for Efficient Transformers
Task AlignmentZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
A Light Introduction to Topological Data AnalysisZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Mean Field Theory of NNsZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk
Discussing the Stanford AI ReportZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Discussing the Stanford AI ReportZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
AI ControlZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Neural likelihood-free inferenceZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
An Introduction to the Conjugate Gradient MethodZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
An Introduction to Transformer Neural ProcessesZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Learning Symmetries in Neural NetworksZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
GenCast: Diffusion-based ensemble forecasting for medium-range weather (or: How to ruin a numerical weather forecaster’s Christmas)Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Bayesian coresetsZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Flow matching, stochastic interpolants and everything in betweenZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Challenges of Regulating Increasingly Complicated Human-AI Collaborative SystemsZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
A Poisson Process Model for Monte CarloZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Learning Directed Acyclic Graphs (DAGs) With Continuous OptimizationZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Deep Learning for Medium-Range Global Weather PredictionZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Learning linear models in-context with transformersZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Quasi-Monte Carlo: structure in the randomness for better sampling
Deciphering Batch Effects in Single-cell Transcriptomics with Concept BottlenecksZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Schrödinger bridges, diffusion and SDEsZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
The LLM Tidal WaveZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
The LLM Tidal WaveZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Game theory, distributional reinforcement learning, control and verificationZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
On choosing the mass matrix for Hamiltonian Monte CarloZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Reward ModellingZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Navigating the Future: Upcoming EU AI Regulation and its Potential Impact on the FieldZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Learning-based multiscale modeling: computing, data science, and uncertainty quantificationZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Neural Tangent KernelZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
No-regret Dynamics for Multi-agent LearningZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Scalable Approaches to Self-Supervised Learning using Spectral AnalysisZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Physics-informed machine learningZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Causal Machine LearningZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
User Manipulation in Recommender SystemsZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
Random Features for Kernel ApproximationZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
An Overview of Differential Privacy, Membership Inference Attacks, and Federated LearningZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Bayesian Neural NetworksZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Offline Reinforcement LearningZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
{PF}^2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization Under Unknown ConstraintsZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Scalable simulation and inference in non-Gaussian stochastic PDEs
Information Geometry — Natural Gradient DescentZoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Predicting generalization of ML models.Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.
Benchmarking and evaluation in contemporary machine learning
Advanced artificial agents intervene in the provision of reward
Theory and Practice of Infinitely Wide Neural Networks - Guest Talk
The unreasonable effectiveness of mathematics in large scale deep learning
The role of meta-learning for few-shot classification
Benefits and Shortcomings of Assistance
Discussion: Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Discussion: Learning PAC-Bayes Priors for Probabilistic Neural Networks
Rethinking evaluation for machine learning models
Discussion: Bayesian Model Selection, the Marginal Likelihood, and Generalization
How close are these distributions? A brief introduction to statistical distances and divergences.
Bests of ICLR
Machine Learning with Quantum Computers
Autoregressive Diffusion Models
An Overview of AI Alignment
The Bayesian Learning Rule for Adaptive AI
Introduction to differential privacy
Retrieval Augmented NLP
Circuits and Interpretability
Optimal Transport Metrics
Learned Compression
Diffusion and Score-based Generative Models
AI Safety
Energy-Based Models
Deep Kernels
Mean Field Theory of NN (postponed to March 2022)
Representation Learning: A Causal Perspective
Ethics, Integrity and Good Practice in ML
Implicit Regularization in Deep Learning
Planning Meeting + PI Presentations
Towards Neuro-Causality: Relating Graph Neural Networks to Structural Causal Models
CBL Alumni Talk - Task-specific routing of information in neural circuits via structured noise by Cristina Savin
CBL Alumni Talk: Nonlinear filtering as a unifying principle in neuroscience by Jean-Pascal Pfister
CBL Alumni Talk: Finale Doshi-Velez
Pseudo-Points and State-Space Gaussian Processes
CBL Alumni Talk: Latent Stochastic Differential Equations: An Unexplored Model Class.
The Statistical Finite Element Method
CBL Alumni Talk - Our Power as Technical Designers
Best of ICLR
CBL Alumni Talk: Accurate Gaussian Processes and how they can help Deep Learning
An Introduction to PAC-Bayes
Automated Augmented Conjugate Inference for Gaussian Processes
CBL Alumni Talk: Examining Critiques in Bayesian Deep Learning
Online Learning and Online Convex Optimisation
Monte Carlo Gradient Estimation in Machine Learning
Secondmind's research activities to make Gaussian Processes industry proof
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks
Gaussian Processes I have Known
Causal Representation Learning
Long-Range Transformers
Inference in Stochastic Processes
Fairness in AI
Variational Bayes as Surrogate Regression
Bayesian optimization / Gaussian Process Bandits
Meta-reinforcement learning
Best of NeurIPS
Neural Processes
Large-scale sequential experimentation
Symmetries in Reinforcement Learning
Gradient-based Hyperparameter Optimisation
Best Papers from ICML 2020
Active Learning
Recent advances in the theory and applications of VAEs
Best of ICLR 2020
Differentiable Planning
Recent Developments in Bayesian Deep Learning
Strategic Classification
Machine Learning on Sets
Boltzmann Generators and Stochastic Normalizing Flows
World Models
Federated Learning
Self-Supervised Representation Learning
Kernel Mean Embeddings
Neural Tangent Kernel
Scalable Gaussian Processes
Probabilistic Programming
Neural Attention
Equivariance and Symmetries in CNNs
Hyperparameter Optimisation
Meta-Learning or "Learning To Learn"
Hamiltonian Monte Carlo for Hierarchical Models
Particle MCMC
Stochastic Differential Equations
Generalisation in neural networks
An Overview of Normalizing Flows
Sampling as Optimization
Implicit Variational Inference
Continual Learning: Definitions, Benchmarks, and Approaches
Causal Inference and Causal Reinforcement Learning
Generative models for few-shot prediction tasks
Logical Uncertainty
Stein Discrepancy
Neural Ordinary Differential Equations
Natural gradient in deep neural networks
Graph Neural Networks
Defending Against Adversarial Attacks
Reinforcement Learning and Control as Probabilistic Inference
Neural Networks and Natural Language Processing
Information Theory, Codes, and Compression
Statistical Learning Theory
Deep Generative Models
Minimum Description Length
Machine Learning for Sounds
NIPS 2017 Highlights
Hardware Efficient Machine Learning
Learning to Learn
WGAN and Optimal Transport
Interpretability in Machine Learning
Deep Structured Prediction for Handwriting Recognition
Cooperative Inverse RL
Infer.NET
Deep Learning Book - Meeting 9 - Representation Learning
Deep Learning Book - Meeting 7 - Neural Turing Machines & Conditional Random Fields as RNNs
Kernel Mean Embeddings
Deep Learning Book - Meeting 6 - Recurrent Neural Networks
Differential Privacy
Deep Learning Book - Meeting 5 - ResNets and DenseNets
Deep Learning Book - Meeting 4 - Convolutional Networks
Probabilistic Numerics
Deep Learning Book - Meeting 3 - Optimization
Deep Learning Book - Meeting 2 - Regularization
Deep Learning Book - Meeting 1 - Deep Feedforward NNs
NIPS 2016 papers in 5mins
Symmetry in Statistical Models
Bayesian Optimization
deep generative models
Sketching methods
A crash-course on Bayesian Reinforcement Learning
Partially Observable Markov Decision Processes (POMDPs)
Learning polynomials with Neural Networks
An introduction to Bayesian nonparametrics: some inference schemes for infinite mixture models
Topics in Expectation Propagation
Deep Q-Learning and AlphaGo
inference failures in probabilistic programming
Variational Methods and Compressed Sensing
Fast Fusion of Multi-band Images: A Powerful Tool for Super-resolution
It's the Network Dummy: Exhuming the reticular theory while shoveling a little dirt on the neuron doctrine
Probabilistic programming
Learning by learning rich generative models
Natural gradient descent and variational inference
Differentiable Data Structures and (if we have time) POMDPsNOTE Topic Change
Random projection ensemble classification
Deep learning for time series
Stochastic discrete integration
Stochastic optimization and adaptive learning rates
Logic, Theorem Proving, and Probabilistic Programming
Connections between kernels, GPs, and NNs
Detecting paraphrases using recursive autoencoders
Bayesian optimization and its applications
LP relaxations for MAP inference
Convolutional Neural Networks
Hessian-free Optimisation for Neural Networks
Scalable MCMC
Causal Inference
Gaussian Process Models for Time Series
Informational Geometry
Deep Gaussian Processes
Combinatorial Stochastic Processes in Bayesian Nonparametrics
Variance Reduction Techniques and Stochastic Optimisation for Monte Carlo
Transfer Learning
Advanced HMC
Active Learning
Deep Probabilistic Models (Wake/Sleep)
PAC Bayes
LSTM and Recurrent Neural Networks
Automatic Differentiation with Theano
State Space Abstraction for Reinforcement Learning
Kernel Embedding for Distributions
Unsupervised Representation Learning
Time-varying dynamic Bayesian network reconstruction with information sharing
Advanced Gaussian Process approximation methods
Statistical model criticism
Functional Programming
The Bernoulli Factory problem and some connections with Computable Analysis
Neural Network Language Modelling
Loopy belief propagation
An introduction to Sequential Monte Carlo
Probabilistic Data Structures and Algorithms
Information Theory and Method of Types: Channels, Quantizers, and Divergences
Integrated Nested Laplace Approximation (INLA)
Spectral Learning
Random Projections
Herding: Driving Deterministic Dynamics To Learn And Sample Probabilistic Models
Bayesian and Bandit Optimization
Random Forests: One tool for all your problems.
Modelling Nonlinear Dynamical Systems
Adaptive Hamiltonian-based MCMC samplers
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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Saifuddin Syed (University of Oxford).
Wednesday 20 November 2024, 11:00-12:30