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namedatetopiclinknotes
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2017 Fall, meeting at Tuesday 1:30-2:45pm in Halligan 127
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all09/19/2017set up the schedule
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Rishit/Fakhteh09/26/2017Ch2 of W&Jhttp://www.nowpublishers.com/article/Details/MAL-001
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Linfeng/Xinmeng10/03/20172.5 and 2.6 of Ch2, 31.-3.3 of Ch 3
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Dylan/Ramtin10/10/2017Ch3.4
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Brian / Kevin10/17/2017
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10/24/2017--
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Dan / Alex10/31/2017Ch4.1.1 - Ch4.1.3
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11/07/2017
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Dan / Alex11/14/2017Finish Ch4.1
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Dylan11/21/2017Ch4.3
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Rishit11/28/2017Deep Learning and the Information Bottleneck Principle,https://arxiv.org/pdf/1503.02406.pdf
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12/05/2017NO MEETING DURING NIPS
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Linfeng12/12/2017Opening Black Box Deep NNshttps://arxiv.org/pdf/1703.00810.pdf
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2018 Spring, meeting at Wednesday 1:30-2:45pm in Halligan 127
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Kevin1/24/2018Safe and Nested Subgame Solving for Imperfect-Information Games
http://papers.nips.cc/paper/6671-safe-and-nested-subgame-solving-for-imperfect-information-games.pdf
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Dan Banco1/31/2018A Linear Time Kernel Goodness of Fit Testhttp://papers.nips.cc/paper/6630-a-linear-time-kernel-goodness-of-fit-test.pdf
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Daniel Pechi2/7/2018Learned in Translation: Contextualized Word Vectorshttps://papers.nips.cc/paper/7209-learned-in-translation-contextualized-word-vectors.pdf
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Dylan2/14/2018Understanding Black-box Predictions via Influence Functionshttps://arxiv.org/abs/1703.04730
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Linfeng2/21/2018
A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions
https://people.ece.cornell.edu/acharya/papers/pml-opt.pdf
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Dylan2/28/2018
Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations
http://www.kdd.org/kdd2016/papers/files/rpp0548-kangA.pdf
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Nathan3/7/2018Wavenethttps://arxiv.org/abs/1609.03499
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Daniel Kasenberg3/14/2018The Mechanics of n-Player Differentiable Gameshttps://arxiv.org/abs/1802.05642
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Kevin3/26/2018Gaussian Markov Random Fields: Theory and ApplicationsAvailable online via Tufts library
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Dan Banco4/4/2018Gaussian Markov Random Fields: Theory and Applications
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Linfeng4/11/2018Gaussian Markov Random Fields: Theory and Applications
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Dylan4/18/2018Gaussian Markov Random Fields: Theory and Applications
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----------4/25/2018----------
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Dan Banco5/2/2018Gaussian Markov Random Fields: Theory and ApplicationsChapters 2.6.2, 2.6.3 and 2.7
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Ramtin5/9/2018Sever: A Robust Meta-Algorithm for Stochastic Optimizationhttps://arxiv.org/abs/1803.02815
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----------5/16/2018----------
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Kevin5/23/2018Efficient representation of low dimensional manifolds using deep neural networkshttps://arxiv.org/abs/1602.04723
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----------5/30/2018----------
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Linfeng6/6/2018Graph Attention Networkshttps://arxiv.org/abs/1710.10903
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Liping6/13/2018REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
http://papers.nips.cc/paper/6856-rebar-low-variance-unbiased-gradient-estimates-for-discrete-latent-variable-models.pdf
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Kevin6/20/2018
Unsupervised and Semi-Supervised Learning with Categorical Generative Adversarial Networks
https://arxiv.org/pdf/1511.06390.pdf
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Linfeng6/27/2018Spherical CNNshttps://openreview.net/pdf?id=Hkbd5xZRb
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----------7/4/2018----------
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Dan Banco7/11/2018Wasserstein GANhttps://arxiv.org/abs/1701.07875
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Kevin7/18/2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
https://arxiv.org/pdf/1802.00420.pdf
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Dan Banco7/25/2018Wasserstein Auto-encoders
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Linfeng8/1/2018Glow: Generative Flow with Invertible 1x1 Convolutions
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Brian Montambault
8/8/2018Learning Deep Mean Field Games for Modeling Large Population Behavior
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Luke8/15/2018Hamiltonian Dynamics methods applications in Bayesian learning
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Luke8/22/2018Hamiltonian Dynamics methods applications in Bayesian learning
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Siyuan8/28/2018Adversarial Deep Learning for Robust Detection of Binary Encoded Malwarehttps://arxiv.org/pdf/1801.02950.pdf
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2018 Fall, meeting at W 3:00-4:15, H209
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Kevin9/5/2018Delayed Impact of Fair Machine Learning
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Ugo9/12/2018Bayesian Data Analysis Ch1-2
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Brian9/19/2018Bayesian Data Analysis Ch3
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Linfeng9/26/2018Bayesian Data Analysis Ch5
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Dylan10/3/2018Bayesian Data Analysis Ch6
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Kevin10/10/2018Bayesian Data Analysis Ch7
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Dan Banco10/17/2018Bayesian Data Analysis Ch9
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-10/24/2018Vote Party
Dylan out (IEEE VIS)
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Rui10/31/2018RNN with Missing Data
https://papers.nips.cc/paper/1126-recurrent-neural-networks-for-missing-or-asynchronous-data.pdf
Dylan out (IEEE VIS)
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-11/7/2018Justin Domke's talk
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Gabriel11/14/2018RNN with Missing DataRecurrent Neural Networks for Multivariate Time Series with Missing Values
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-11/21/2018Turkeys
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Dan Banco11/28/2018RNN with Missing Datahttp://zacklipton.com/media/papers/rnns-missing-data_9.pdf
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Dylan12/5/2018
Learning on Graphs (Semi-Supervised Classification with Graph Convolutional Networks )
https://openreview.net/pdf?id=SJU4ayYgl
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Kevin12/12/2018Learning on Graphshttps://arxiv.org/pdf/1611.08402.pdf
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TBD
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Linfeng12/18/2018 1:00 PM
Learning on Graphs (Bayesian Semi-supervised Learning with Graph Gaussian Processes)
https://arxiv.org/pdf/1809.04379.pdf
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12/26/2018Break
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1/2/2019Break
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Mike1/9/2019NIPS recap
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Daniel1/15/2019Bayes Backprop (Weight Uncertainty in Neural Networks)https://arxiv.org/pdf/1505.05424.pdf
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Kevin1/22/2019BAYESIAN RECURRENT NEURAL NETWORKShttps://arxiv.org/pdf/1704.02798.pdf
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Linfeng1/29/2019Bayesian Hypernetworkshttp://bayesiandeeplearning.org/2017/papers/34.pdf
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Daniel 2/5/2019Gan for Compressed Sensinghttps://arxiv.org/abs/1703.03208
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Daniel2/12/2019Gan for Compressed Sensinghttp://home.engineering.iastate.edu/~chinmay/files/papers/ganICASSP18.pdf
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Daniel2/12/2019Gan for Compressed Sensinghttps://arxiv.org/abs/1810.03587
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Executive decision
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Gabe2/19/2019Numeric Optimization, Nocedal and Wright - Chapters 1-2https://link.springer.com/book/10.1007%2F978-0-387-40065-5
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Kevin2/26/2019Numeric Optimization, Nocedal and Wright - Chapters 3https://link.springer.com/book/10.1007%2F978-0-387-40065-5
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Rui3/5/2019Numeric Optimization, Nocedal and Wright - Chapters 4-
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Linfeng3/12/2019Numeric Optimization, Nocedal and Wright - Chapters 4.2+-
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-3/19/2019-
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Daniel3/26/2019Numeric Optimization, Nocedal and Wright - Chapters ~5/6.1Pick a topic with 2-3 papers for next time
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Gabe4/2/2019Troubling Trends in Machine Learning Scholarship
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Kevin4/9/2019
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Rui4/16/2019Human-level control through deep reinforcement learninghttps://www.nature.com/articles/nature14236.pdf
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Linfeng4/23/2019Mastering the game of Go with deep neural networks and tree searchhttps://www.nature.com/articles/nature16961.pdf
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Daniel4/30/2019
Truncated Horizon Policy Search: Combining Reinforcement Learning and Imitation Learning
https://arxiv.org/abs/1805.11240
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Kevin5/7/2019A theory of learning from different domainshttps://link.springer.com/content/pdf/10.1007/s10994-009-5152-4.pdf
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Gabe5/14/2019Return of frustratingly easy domain adaptationhttps://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12443
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Linfeng5/21/2019Deep Visual Domain Adaptation: A Surveyhttps://arxiv.org/pdf/1802.03601.pdf
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Daniel6/11/2019QMC 1http://statweb.stanford.edu/~owen/courses/362-1011/readings/siggraph03.pdf
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Gabe6/18/2019QMC 2 https://arxiv.org/pdf/1807.01604.pdf
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Kevin6/25/2019QMC 3https://papers.nips.cc/paper/7304-geometrically-coupled-monte-carlo-sampling.pdf
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Linfeng7/2/2019Amortized Variational Inference for Gaussian Processes
Variational inference:
[1] Variational Inference: A Review for Statisticians. https://arxiv.org/pdf/1601.00670.pdf
Gaussian processes:
[2] Gaussian processes in machine learning. http://www.gaussianprocess.org/gpml/chapters/ (you can skim just chapter 2 to save some time)
[3] Gaussian Processes for Regression: A Quick Introduction. http://www.robots.ox.ac.uk/~mebden/reports/GPtutorial_old.pdf
Our recent paper on AISTATS 2019:
[4] Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes. http://proceedings.mlr.press/v89/liu19c.html
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Hao Cui7/9/2019
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Da Tang7/16/2019
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Ruiyuan7/23/2019Current research topics
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Daniel7/30/2019Current research topics
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Gabe8/6/2019Current research topics
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Kevin8/13/2019Current research topics
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Dylan8/20/2019Current research topics