ICLR 2018 accepted papers and ML@GT

The list of accepted papers at ICLR 2018 was released last week and Machine Learning at Georgia Tech (ML@GT) had a strong presence. Out of 935 submissions, 23 oral and 314 conference papers were accepted (roughly 36%). We are pleased to announce that Georgia Tech had 10 conference papers this year, with 1 of them being oral (2% acceptance) and 2 others within the top 100, as well as 1 additional workshop paper.

This brings GT into the Top 15 among institutions and if you only consider academic institutions it is 9th on the list (note that this is a very conservative estimate as this analysis seems to miss many GT papers). This is a testament to Georgia Tech’s strong research in ML, and we see this presence increasing significantly given the ML Ph.D. program and the number of new faculty hires in this area every year.

The list of accepted ICLR 2018 papers with Georgia Tech affiliation  is below.

1. Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations (oral). Jiachen Yang (Georgia Tech), Xiaojing Ye (Georgia State), Rakshit Trivedi (Georgia Tech), Huan Xu (Georgia Tech), Hongyuan Zha (Georgia Tech)
2. Boosting the Actor with Dual Critic. Bo Dai (Georgia Tech), Albert Shaw (Georgia Tech), Niao He (UIUC), Lihong Li (Google), Le Song (Georgia Tech)
3. Cascade Adversarial Machine Learning Regularized with a Unified Embedding. Taesik Na (Georgia Tech), Jong Hwan Ko (Georgia Tech), Saibal Mukhopadhyay (Georgia Tech)
4. Learning to Cluster in order to Transfer Across Domains and Tasks (top 100). Yen-Chang Hsu (Georgia Tech), Zhaoyang Lv (Georgia Tech), Zsolt Kira (Georgia Tech Research Institute)
5. Multi-Agent Compositional Communication Learning from Raw Visual Input. Edward Choi (Georgia Tech), Angeliki Lazaridou (Deep Mind), Nando de Freitas (University of Oxford)
6. Generative Models of Visually Grounded Imagination (top 100). Ramakrishna Vedantam (Georgia Tech), Ian Fischer (Google), Jonathan Huang (Google), Kevin Murphy (Google)
7. Initialization matters: Orthogonal Predictive State Recurrent Neural Networks. Krzysztof Choromanski (Google), Carlton Downey (CMU), Byron Boots (Georgia Tech)
8. Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. Ashwin Kalyan (Georgia Tech), Abhishek Mohta (MSR), Oleksandr Polozov (Univ. of Washington), Dhruv Batra (Georgia Tech), Prateek Jain (UT Austin), Sumit Gulwani (MSR)
9. Syntax-Directed Variational Autoencoder for Structured Data. Hanjun Dai (Georgia Tech), Yingtao Tian (State University of NY), Bo Dai (Georgia Tech), Steven Skiena (State University of NY), Le Song (Georgia Tech)
10. Truncated Horizon Policy Search: Deep Combination of Reinforcement and Imitation. Wen Sun (CMU), J. Andrew Bagnell (CMU), Byron Boots (Georgia Tech)
11. Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms (workshop). Tom Zahavy (Technion), Bingyi Kang (National University of Singapore), Alex Sivak (Technion), Jiashi Feng ((National University of Singapore), Huan Xu (Georgia Tech), Shie Mannor (Technion)