MEDG Spring 2016 Reading Group

We meet on Mondays from 1:30 - 3 pm in the MEDG space on the second floor of CSAIL. If you want to sign up to get reminders, ask here.



  • Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis by David Blei and Noémie Elhadad
    March 16 @ 12:30 pm
    Presented by Tristan
  • Gaussian process clustering for the functional characterisation of vital-sign trajectories by Marco A. F. Pimentel, David A. Clifton, and Lionel Tarassenko
    March 23 @ 12:30 pm
    Presented by Marzyeh


  • A. Tank, N. J. Foti and E. B. Fox. Bayesian Structure Learning for Stationary Time Series. Uncertainty in Artificial Intelligence (UAI), 2015. [arXiv]
  • A. Tank, N. J. Foti and E. B. Fox. Streaming variational inference for Bayesian nonparametric mixture models. Artificial Intelligence and Statistics (AISTATS), 2015. [arXiv]
  • N. J. Foti, J. Xu, D. Laird and E. B. Fox. Stochastic variational inference for hidden Markov models. Advances in Neural Information Processing Systems (NIPS), 2014. [paper] [supplement] [code]
  • AAAI 2016: Extracting Topical Phrases from Clinical Documents
  • NIPS 2015: Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction by Been, Julie and Finale.
  • NIPS 2015: A Hierarchical Approach to Individualized Disease Trajectory Predictions in Heterogeneous Populations Probabilistic Variational Bounds for Graphical Models
  • NIPS 2015: Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
  • NIPS 2015: Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM)
  • NIPS 2015: Streaming, Distributed Variational Inference for Bayesian Nonparametrics
  • NIPS 2015: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
  • NIPS 2015: Deeply Learning the Messages in Message Passing Inference
  • NIPS 2015: MCMC for Variationally Sparse Gaussian Processes
  • NIPS 2015: Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
  • NIPS 2015: Learning Bayesian Networks with Thousands of Variables
  • NIPS 2015: Training Very Deep Networks
  • NIPS 2015: Statistical Model Criticism using Kernel Two Sample Tests
  • NIPS 2015: A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models
  • Katherine Niehaus, Joshua Knowles and Nigam Shah: FIND FH – A phenotype model to identify patients with familial hypercholesterolemia
  • Linda Zhang, Daniel Fabbri and Colin Walsh: A Data Driven System for Clinical Preventive Order Recommendations
  • Wuyang Dai, Theodora Brisimi, Tingting Xu, Taiyao Wang, Venkatesh Saligrama and Ioannis Paschalidis: A Joint Clustering and Classifcation Approach for Healthcare Predictive Analytics
  • Nirav Shah, Vivek Vegi, Ankit Dhingra, Rema Padman, Daniel Nagin and Ari Robicsek: What is a “normal” postoperative temperature? Group based trajectory modeling in postoperative knee arthroplasty patients in a large health system
  • Narges Razavian and David Sontag: Temporal Convolutional Models of Biomarkers for Disease Diagnosis
  • AISTATS 2015: Model Selection for Topic Models via Spectral Decomposition by Dehua Cheng pdf
  • AISTATS 2015: Modeling Skill Acquisition Over Time with Sequence and Topic Modeling by José González-Brenes pdf
  • AISTATS 2015: The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation by Fangjian Guo pdf
  • AISTATS 2015: Estimating the accuracies of multiple classifiers without labeled data by Ariel Jaffe pdf
  • AISTATS 2015: Latent feature regression for multivariate count data by Arto Klami pdf
  • AISTATS 2015: Dimensionality estimation without distances by Matthäus Kleindessner pdf
  • AISTATS 2015: Tensor Factorization via Matrix Factorization by Volodymyr Kuleshov pdf
  • AISTATS 2015: Low-Rank Spectral Learning with Weighted Loss Functions by Alex Kulesza pdf
  • AISTATS 2015: Similarity Learning for High-Dimensional Sparse Data by Kuan Liu pdf
  • AISTATS 2015: Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning by Mario Lucic pdf
  • AISTATS 2015: Active Pointillistic Pattern Search by Yifei Ma pdf
  • AISTATS 2015: The Security of Latent Dirichlet Allocation by Shike Mei
  • AISTATS 2015: A Spectral Algorithm for Inference in Hidden semi-Markov Models by Igor Melnyk pdf
  • AISTATS 2015: Learning Efficient Anomaly Detectors from K-NN Graphs by Jonathan Root pdf
  • AISTATS 2015: Falling Rule Lists by Fulton Wang pdf
  • AISTATS 2015: A la Carte – Learning Fast Kernels by Zichao Yang pdf
  • AISTATS 2015: Minimizing Nonconvex Non-Separable Functions by Yaoliang Yu pdf
  • AISTATS 2015: A Simple Homotopy Algorithm for Compressive Sensing by Lijun Zhang pdf
  • AISTATS 2015: Scalable Nonparametric Multiway Data Analysis by Shandian Zhe pdf
  • AISTATS 2015: Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction by Mingyuan Zhou pdf
  • KDD 2015: Probabilistic Graphical Models of Dyslexia by Yair Lakretz
  • CVPR 2015: A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions by Kuan-Chuan Peng
  • CVPR 2015: Best-Buddies Similarity for Robust Template Matching by Tali Dekel
  • KDD 2014: Modeling Professional Similarity by Mining Professional Career Trajectories by Ye Xu
  • KDD 2015:  Hierarchical Graph-Coupled HMMs on Heterogeneity and Personalized Health by Kai Fan
  • KDD 2015: Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction by Tingyang Xu