Planned Readings

Optimization for ML #5 - 03. May. 2018 The Supervised Learning No-Free-Lunch Theorems¶

http://www.no-free-lunch.org/Wolp01a.pdf¶

Optimization for ML #3 - 05. Apr. 2018 Sharp Minima Can Generalize For Deep Nets

https://arxiv.org/pdf/1703.04933.pdf

Optimization for ML #2 - 08. Mar. 2018 Support Vector Machines

http://cs229.stanford.edu/notes/cs229-notes3.pdf

Optimization for ML #1 - 22. Feb. 2018 Large-Scale Machine Learning with Stochastic Gradient Descent

https://link.springer.com/content/pdf/10.1007%2F978-3-7908-2604-3_16.pdf

-------------------------------------------------------------------------------- Probabilistic Deep Learning #6 - 08. Feb. 2018 Application Session

* Bayesian Recurrent Neural Networks
* Learning & policy search in stochastic dynamical systems with BNNs
* Deep Probabilistic Programming
* Neural Discrete Representation Learning
* Deep Bayesian Active Learning with Image Data
Probabilistic Deep Learning #5 - 25. Jan. 2018 Weight Uncertainty in Neural Networks

http://proceedings.mlr.press/v37/blundell15.pdf

Probabilistic Deep Learning #4 - 11. Jan. 2018 Priors for infinite networks

https://link.springer.com/content/pdf/10.1007%2F978-1-4612-0745-0_2.pdf

Probabilistic Deep Learning #3 - 14. Nov. 2017 Deep Kernel Learning

http://proceedings.mlr.press/v51/wilson16.pdf

Probabilistic Deep Learning #2 - 30. Nov. 2017 What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

https://arxiv.org/pdf/1703.04977.pdf

Probabilistic Deep Learning #1 - 16. Nov. 2017 Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://arxiv.org/abs/1506.02142

-------------------------------------------------------------------------------- State-of-the-art ML #5 - 02. Nov. 2017 Learning from Simulated and Unsupervised Images through Adversarial Training

https://arxiv.org/abs/1612.07828

State-of-the-art ML #4 - 19. Oct. 2017 "Why Should I Trust You?": Explaining the Predictions of Any Classifier

https://arxiv.org/abs/1602.04938.pdf

State-of-the-art ML #3 - 05. Oct. 2017 Balancing information exposure in social networks

https://arxiv.org/pdf/1709.01491.pdf

State-of-the-art ML #2 - 21. Sep. 2017 The numerics of GAN

https://arxiv.org/pdf/1705.10461.pdf

State-of-the-art ML #1 - 07. Sep. 2017 Deep Gaussian Processes

http://proceedings.mlr.press/v31/damianou13a.pdf​

-------------------------------------------------------------------------------- Inference in Bayesian Networks #6 - 29. June 2017
* Fast nonparametric clustering of structured time-series" by James Hensman, Magnus Rattray and Neil D. Lawrence - Olga
* Deep Gaussian Process Andreas, C. Damianou and Neil D. Lawrence - Judith
* Pitkow, X., & Angelaki, D. E. (2017). How the Brain Might Work: Statistics Flowing in Redundant Population Codes. - Ylva
Inference in Bayesian Networks #5 - 15. June 2017 An Introduction to MCMC for Machine Learning - C. Andrieu - ‎2003

http://mathfaculty.fullerton.edu/sbehseta/Math470-10.1.1.13.7133.pdf

Inference in Bayesian Networks #4 - 01. June 2017 Approximate Bayesian computational methods

http://link.springer.com/article/10.1007%2Fs11222-011-9288-2?LI=true

Inference in Bayesian Networks #3 - 18. May 2017 Hierarchical Beta Processes and the Indian Buffet Process (Monte Carlo inference scheme)

http://people.ee.duke.edu/~lcarin/thibaux-jordan-aistats07.pdf

Inference in Bayesian Networks #2 - 04. May 2017 Expectation Propagation for approximate Bayesian inference

https://arxiv.org/abs/1301.2294

Inference in Bayesian Networks #1 - 20. Apr. 2017 Understanding Belief Propagation and its Generalizations

http://www.merl.com/publications/docs/TR2001-22.pdf

-------------------------------------------------------------------------------- Transfer Learning #6 - 05. Apr. 2017 Application session Marcus and Judith"Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping"

Vladimir and Ondrej"Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning"

Sahar"Domain Randomization for transferring deep neural networks from simulations to real world"

Silvia and Erik"Effective Transfer Learning of Affordances for Household Robots"

Taras and Joonatan"Sim-to-Real Robot Learning from Pixels with Progressive Nets"

Olga"Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks"

Ylva and Ramon"How Transferable Are Features in Deep Neural Networks"

Transfer Learning #5 - 23. Mar. 2017 A theory of learning from different domains

http://www.alexkulesza.com/pubs/adapt_mlj10.pdf

Transfer Learning #4 - 9. Mar. 2017 Understanding deep learning requires rethinking generalization

https://arxiv.org/pdf/1611.03530

Transfer Learning #3 - 23. Feb. 2017 DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

http://jmlr.org/proceedings/papers/v32/donahue14.pdf

Transfer Learning #2 - 9. Feb. 2017 Learning to learn by gradient descent by gradient descent

https://arxiv.org/abs/1606.04474

Transfer Learning #1 - 25. Jan. 2017 A Survey on Transfer Learning

https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf

---------------------------------------------------------------------------------- Approximate inference #6 - 12. Jan. 2017 Black-Box Variational Inference

http://www.cs.columbia.edu/~blei/papers/RanganathGerrishBlei2014.pdf

Approximate inference #5 - 15. Dec. 2016 - Christmas edition Variational Tempering

http://jmlr.org/proceedings/papers/v51/mandt16.pdf

Approximate inference #4 - 24. Nov. 2016 Variational Message Passing

http://www.jmlr.org/papers/volume6/winn05a/winn05a.pdf

Approximate inference #3 - 10. Nov. 2016 Stochastic Variational Inference

http://jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf

Approximate inference #2 - 27. Oct. 2016 An Introduction to Variational Methodsfor Graphical Models

https://people.eecs.berkeley.edu/~jordan/papers/variational-intro.pdf

Approximate inference #1 - 13. Oct. 2016 Graphical Models

https://www.cs.cmu.edu/~aarti/Class/10701/readings/graphical_model_Jordan.pdf

---------------------------------------------------------------------------------- Deep Learning #10 - 29. Sep. 2016 Deep learning application session Deep Learning #9 - 15. Sep. 2016 Learning to Communicate withDeep Multi-Agent Reinforcement Learning

https://arxiv.org/pdf/1605.06676v2.pdf

Deep Learning #8 - 23 June 2016 / 01. Sep. 2016 End-to-End Training of Deep Visuomotor Policies

https://arxiv.org/pdf/1504.00702v5.pdf

Deep Learning #7 - 9 June 2016 Benchmarking Deep Reinforcement Learning for Continuous Control

http://arxiv.org/pdf/1604.06778v2.pdf

Continuous control with deep reinforcement learning

http://arxiv.org/pdf/1509.02971v5.pdf

Deep Learning #6 - 26 May 2016 Playing atari with deep reinforcement learning

http://arxiv.org/abs/1312.5602

Mastering the game of Go with deep neural networks and tree search

http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html

Deep Learning #5 - 12 May 2016 Variational Auto-encoded Deep Gaussian Processes http://arxiv.org/abs/1511.06455

Deep Learning #4 - 21 April 2016 Long Short-Term Memory Deep Learning #3 - 7 April 2016 ADVERSARIAL AUTOENCODERS- Goodfellow et.al (2016)

Deep Learning #2 - 24 March 2016 Represenation Learning: A Review and New Perspectives - Vincent et. al. (2014)

Deep Learning #1 - 10 March 2016 Deep Learning - Hinton et. al. (Nature, 2015)

---------------------------------------------------------------------------------- Applications of Probabilistic Numerics and Bayesian Optimization (25 February 2016) Jim Holmström, Erik Ward:"Designing Engaging Games Using Bayesian Optimization"Silvia Cruciani, Ali Ghadirzadeh:"A Bayesian Exploration-Exploitation Approach for Optimal Online Sensing and Planning with a Visually Guided Mobile Robot"Cheng Zhang, Judith Butepage:"Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction"

[IMAGE]Anastasiia Varav, João ... de Carvalho"Metrics for Probabilistic Geometries"

Yanxia Zhang, Puren Guler"Robots that can adapt like animals"