ICML

ICML 2017 accepted papers and ML@GT

The list of accepted papers at ICML2017 was released yesterday and Andrej Karpathy has published a very nice post breaking down the acceptance by institution. Out of 1701 submissions 433 papers were accepted (or roughly 25.46%) from 420 different institutions. I am excited to see a very strong representation of Machine Learning @ Georgia Tech (ML@GT) with 13 papers (Andrej reported 14 but I could only find 13 — going with the conservative estimate). This brings GT into the Top 10 among institutions and if you only consider academic institutions it is 6th on the list being testament to GT’s strong research in ML. This also nicely ties in with the newly established Ph.D. program in Machine Learning where we will be admitting for fall this year for the first time creating an even more vibrant Machine Learning community at Georgia Tech tightly integrating research and education.

List of accepted ICML 2017 with Georgia Tech affiliation — Congratulations everyone (and of course also to the competition)!

  1. Lazifying Conditional Gradient Algorithms
    Gábor Braun (Georgia Institute of Technology) · Sebastian Pokutta (Georgia Tech) · Daniel Zink (Georgia Institute of Technology)
  2. Conditional Accelerated Lazy Stochastic Gradient Descent
    Guanghui Lan (Georgia Institute of Technology) · Sebastian Pokutta (Georgia Tech) · Yi Zhou (Georgia Institute of Technology) · Daniel Zink (Georgia Institute of Technology)
  3. Learning Hawkes Processes from Short Doubly-Censored Event Sequences
    Hongteng Xu (Georgia Institute of Technology) · Dixin Luo (University of Toronto) · Hongyuan Zha (Georgia Institute of Technology)
  4. Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
    Wen Sun (Carnegie Mellon University) · Arun Venkatraman (Carnegie Mellon University) · Geoff Gordon (Carnegie Mellon University) · Byron Boots (Georgia Tech) · Drew Bagnell (Carnegie Mellon University)
  5. Survival HMM: An Interpretable, Event-time Prediction Model for mHealth
    Walter Dempsey (University of Michigan) · Alexander Moreno (Georgia Institute of Technology) · Jim Rehg (Georgia Tech) · Susan Murphy (University of Michigan)
  6. Stochastic Generative Hashing
    Bo Dai (Georgia Tech) · Ruiqi Guo (Google Research) · Sanjiv Kumar (Google Research, NY) · Niao He (UIUC) · Le Song (Georgia Institute of Technology)
  7. Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control
    Yunpeng Pan (Georgia Tech) · Xinyan Yan (Georgia Institute of Technology) · Evangelos Theodorou (Georgia Tech) · Byron Boots (Georgia Tech)
  8. Online Multiview Learning: Dropping Convexity for Better Efficiency
    Zhehui Chen (Georgia Institute of Technology) · Lin Yang (Johns Hopkins) · Chris Junchi Li (Princeton University) · Tuo Zhao (Georgia Institute of Technology)
  9. Variational Policy for Guiding Point Processes
    Yichen Wang (Gatech) · Grady Williams (Georgia Tech) · Evangelos Theodorou (Georgia Tech) · Le Song (Georgia Institute of Technology)
  10. Fake News Mitigation via Point Process Based Intervention
    Mehrdad Farajtabar (Georgia Tech) · Jiachen Yang (Georgia Institute of Technology) · Xiaojing Ye (Georgia State University) · Huan Xu (Georgia Tech) · Shuang Li () · Rakshit Trivedi (Georgia Institute of Technology) · Elias Khalil (Georgia Tech) · Le Song (Georgia Institute of Technology) · Hongyuan Zha (Georgia Institute of Technology)
  11. Iterative Machine Teaching
    Weiyang Liu (Georgia Tech) · Bo Dai (Georgia Tech) · Jim Regh (Georgia Tech) · Le Song (Georgia Institute of Technology)
  12. Emulating the Expert: Inverse Optimization through Online Learning
    Sebastian Pokutta (Georgia Tech) · Andreas Bärmann (FAU Erlangen-Nürnberg) · Oskar Schneider (FAU Erlangen-Nürnberg)
  13. Know-Evolve: Deep Learning for Temporal Reasoning in Dynamic Knowledge Graphs
    Rakshit Trivedi (Georgia Institute of Technology) · Hajun Dai (Georgia Tech) · Yichen Wang (Gatech) · Le Song (Georgia Institute of Technology)

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