notes on ML/CS/etc articles
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Academic papers Irene has read

My goal is to read an academic paper every day. Here I keep myself accountable.


Jun 11, 2018: Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information. S. Rao, H. Daume. 2018. [pdf]

Jun 10, 2018: An Alternative View: When Does SGD Escape Local Minima? R. Kleinberg, Y. Li, Y. Yuan. 2018. [pdf]

Jun 05, 2018: Do CIFAR-10 Classifiers Generalize to CIFAR-10?. B. Recht, R. Roelofs, L. Schmit, V. Shankar. 2018. [pdf]

Jun 03, 2018: Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health. T. Althoff, K. Clark, J. Leskovec. 2016. [pdf]

May 31, 2018: Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. M. Kearns, S. Neel, A. Roth, Z. Steven Wu. 2018. [pdf]

May 30, 2018: The Seven Pillars of Causal Reasoning with Reflections on Machine Learning. J. Pearl. 2018. [pdf]

Sep 05, 2017: Classification of common human diseases derived from shared genetic and environmental determinants. K. Wang, Hallie Gaitsch, Hoifung Poon, N. J Cox, A. Rzhetsky. 2017. [pdf]

Aug 16, 2017: Understanding Black-Box Predictions via Influence Functions. P. Wei Koh, P. Liang. 2017. [pdf]

May 10, 2017: Large-Scale Physiological Waveform Retrieval via Locality-Sensitive Hashing. Y. Bryce Kim, U. O'Reilly. 2015. [pdf]

May 10, 2017: Practical Bayesian Optimization of Machine Learning Algorithms. J. Snoek, H. Larochelle, R. P. Adams. 2012. [pdf]

May 08, 2017: Learning Graphical Models Using Multiplicative Weights. A. R. Klivans, R. Meka. 2017. [pdf]

May 07, 2017: Mining Frequent Graph Patterns with Differential Privacy. E. Shen, T. Yu. 2013. [pdf]

May 06, 2017: Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels. C. G. Northcutt, T. Wu, I. L. Chuang. 2017. [pdf]

May 05, 2017: Random Features for Large-Scale Kernel Machines. A. Rahimi, B. Recht. 2007. [pdf]

May 04, 2017: Differential Privacy and Machine Learning: a Survey and Review. Z. Ji, Z. C. Lipton, C. Elkan. 2014. [pdf]

Mar 27, 2017: The Dependence of Machine Learning on Electronic Medical Record Quality. L. Ho, D. Ledbetter, M. Aczon. 2017. [pdf]

Jan 13, 2017: Bringing Impressionism to Life with Neural Style Transfer in Come Swim B. Joshi, K. Stewart, D. Shapiro. 2017. [pdf]

Jan 12, 2017: Probabilistic Topic Models. D. Blei. Communications of the ACM 2012. [pdf]

Jan 11, 2017: Correlated factors in anchored factor analysis. Y. Halpern. Ch 6 of PhD thesis, 2016. [pdf]

Jan 10, 2017: How the machine 'thinks': Understanding opacity in machine learning algorithms. J. Burrell. Big Data and Society 2016. [pdf]

Jan 9, 2017: Combatting Police Discrimination in the Age of Big Data. New Criminal Law Review 2016. [pdf]

Jan 8, 2017: Structured Inference Networks for Nonlinear State Space Models. R. Krishnan, U. Shalit, D. Sontag. AAAI 2017. [pdf]

Jan 7, 2017: Let's Get Together: The Formation and Success of Online Creative Collaborations. B. Settles, S. Dow. CHI 2013. [pdf]

Jan 6, 2017: Learning a health knowledge graph from electronic medical records. Y. Halpern. Ch 5 of PhD thesis, 2016. [pdf]

Jan 5, 2017: Equality of Opportunity in Supervised Learning. M. Hardt, E. Price, N. Srebro. NIPS 2016. [pdf]

Jan 4, 2017: Generative Adversarial Nets. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio. NIPS 2014. [pdf]

Jan 3, 2017: Train and Test Tightness of LP Relaxations in Structured Prediction. O. Meshi, M. Mahdavi, A. Weller, D. Sontag. ICML 2016. [pdf]

Jan 2, 2017: Comorbidity Clusters in Autism Spectrum Disorders. F. Doshi-Velez, Y. Ge, I. Kohane. Pediatrics 2014. [pdf]

Jan 1, 2017: Unsupervised Learning of Disease Progression Models. X. Wang, D. Sontag, F. Wang. KDD 2014. [pdf]

Dec 31, 2016: How to Read a Paper. S. Keshav, 2016. [pdf]

To be read


A New Approach to the Minimum Cut Problem. D. Karger, C. Stein. JACM 1996. [pdf]

A Practical Algorithm for Topic Modeling with Provable Guarantees. S. Arora, R. Ge, Y. Halpern, D. Mimno, A. Moitra, D. Sontag, Y. Wu, M. Zhu. ICML 2013. [pdf]

The Pseudo-Dimension of Near-Optimal Auctions. J. Margenstern, T. Roughgarden. NIPS 2015. [pdf]

Topic Models. D. Blei, J. Lafferty. [pdf]

Latent Dirichlet Allocation. D. Blei, A. Ng, M. Jordan. [pdf]

Variational Inference: A Review for Statisticians. D. Blei, A. Kucukelbir, J. McAuliffe. 2016. [pdf]

Learning Bayesian Networks with Incomplete Data by Augmentation. T. Adel, C. de Campos. 2016. [pdf]

Early death after discharge from emergency departments: analysis of national US insurance claims data. Z. Obermeyer, B. Cohn, M. Wilson, A. Jena, D. Cutler. 2016. [[pdf]](

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. T. Bolukbasi, K. Chang, J. Zou, V. Saligrama, A. Kalai. [pdf]

Edge-exchangeable graphs and sparsity. D. Cai, T. Campbell, T. Broderick. [pdf]

Counterfactual Prediction with Deep Instrumental Variable Networks. J. Hartford, G. Lewis, K. Leyton-Brown, M. Taddy. 2016. [pdf]

Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study. T. Dawes, A. Marvao, W. Shi, T. Fletcher, G. Watson, C. Rhodes, L. Howard, J. Gibbs, D. Rueckert, S. Cook, M. Wilkins, D. O'Reagan. Caridac Imaging 2017. [pdf]

RETAIN: Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism. E. Choi, M. Bahadori, A. Schuetz, W. Stewart, J. Sun. NIPS 2016. [pdf]

Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes. A. Garg, N. Adhikari, H. McDonald, M. Rosas-Arellano, P. Devereaux, J. Beyene, J. Sam, R. Haynes. JAMA 2005. [pdf]

Cache-Oblivious Algorithms. M. Frigo, C. Leiserson, H. Prokop, S. Ramachandran. FOCS 1999. [pdf]

Rethinking LDA: Moment Matching for Discrete ICA. A. Podosinnikova, F. Bach, S. Lacoste-Julien. NIPS 2015. [pdf]

Variational Dropout and the Local Reparameterization Trick D. Kingma, T. Salimans, M. Welling. NIPS 2015. [pdf]