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2019 Meetings
Date  Presenter  Topic  Reading 

Nov. 22  Michael  Measuring Fairness in Machine Learning Slides 
CorbettDavies and Goel, The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning (arXiv:1808.00023 2018) Also discussed:

Nov. 15  Michael Guerzhoy  Reconciling modern machinelearning practice and the classical bias–variance tradeoff Slides 
Belkin et al., Reconciling modern machinelearning practice and the classical bias–variance tradeoff (PNAS ,2019)
Lilian Weng, Are Deep Neural Networks Dramatically Overfitted? (2019) 
Nov. 8  Ryan Lee  Neural Machine Translation by Jointly Learning to Align and Translate  Bahdanau et al, Neural Machine Translation by Jointly Learning to Align and Translate (ICLR 2015) 
Oct. 25  Michael Guerzhoy

Wasserstein GAN Slides 
Arjovsky et al, Wasserstein GAN (ICML 2017) Depth First Learning: Wasserstein GAN (2019) 
Oct. 18  Ryan Lee  Generative Adversarial Networks  Goodfellow, Generative Adversarial Networks tutorial (2016) 
Oct 11  Michael Guerzhoy

Nondelusional QLearning (winner, Best Paper Award at NeurIPS 2018) Slides 
Lu et al., Nondelusional Qlearning and valueiteration (NeurIPS 2018) 
Oct. 7  Michael Guerzhoy  Introductory meeting, take 2 (repeat of Sept. 27)  
Sept. 27  Michael Guerzhoy 
Organizational matters A refresher on Qlearning Slides 
Ch. 6 of Sutton and Barto (2017) Mnih et al, Humanlevel control through deep reinforcement learning (Nature, 2015)

Past Meetings
Date  Presenter  Topic  Reading  

Random matrices 

05/03/18  Adam Charles  Matrix concentration inequalities; application: shortterm memory of linear recurrent neural networks 


04/26/18  Mikio Aoi  Random projections for least squares 


04/19/18  Farhan Damani  Lowrank matrix approximation 


04/12/18  Greg Darnell  Markov, Chebyshov, and Chernoff's inequalities; Johnson–Lindenstrauss lemma 


04/05/18  Bianca Dumitrascu  Motivation for random matrices; intro on concentration inequalities 


Information geometry 

03/15/18  Jordan Ash, Alex Beatson  The FisherRao metric and generalization of neural networks 


03/01/18  Sidu Jena  Information entropy and max entropy methods 


02/08/18  Diana Cai, Bianca Dumitrascu  Natural gradients, mirror descent and stochastic variational inference 


02/08/18  Alex Beatson, Greg Gundersen  Intro: information geometry, fdivergences, the Fisher metric, and the exponential family 


01/25/18  Sidu Jena, Archit Verma  Differential geometry overview 


Reinforcement learning & control theory 

11/16/17  Archit Verma  Robust Control 


11/09/17  Ari Seff  Optimal Control 


11/02/17  Sidu Jena, Max Wilson  Control Theory Basics 


10/26/17 
Alex Beatson  ActorCritic 


10/19/17 
Niranjani Prasad, Gregory Gundersen  QLearning 


10/12/17  Ryan Adams  Policy Gradient Methods 


Misc. previous topics 

6/22 
David Zoltowski, Mikio Aoi  Stochastic Gradient Descent as Approximate Bayesian Inference Mandt, Hoffman, Blei (2017)  
6/1 
Stephen Keeley  Understanding deep convolutional networks Mallat (2016)  
5/25 
Davit Zoltowski  Variational Inference with Normalizing Flows Rezende, Mohamed (2016)  
5/11 
Jordan Ash  Generative Adversarial Nets Ian J. Goodfellow∗ , Jean PougetAbadie† , Mehdi Mirza, Bing Xu, David WardeFarley, Sherjil Ozair‡ , Aaron Courville, Yoshua Bengio§  
5/4 
Greg Darnell  Convolutional Neural Networks Analyzed via Convolutional Sparse Coding Vardan Papyan, Yaniv Romano, Michael Elad (2016)  
4/27 
Bianca Dumitrascu 
Why does deep learning work so well? Henry W. Lin, Max Tegmark (2017) 

4/20  Yuki Shiraito  Dropout as Bayesian approximation: Representing Model Uncertainty in Deep Learning Gal & Ghahramani (2016)  
4/13  Mikio Aoi  A Probabilistic Theory of Deep Learning Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk (2015)  
3/30  Brian DePasquale  Semisupervised Learning with Deep Generative Models Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling  
3/16  Adam Charles  On the expressive power of deep learning: A tensor analysis Cohen, Sharir, Shashua (2016)  
3/9  Mikio Aoi  AutoEncoding Variational Bayes Kingma, Welling (2014)  
3/2  Bianca Dumitrascu  Stochastic Backpropagation and Approximate Inference in Deep Generative Models Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra (2014)  
2/16  Nick Roy  Understanding deep learning requires rethinking generalization Zhang, Bengio, Hardt, Recht, Vinyals (2017) 