NIPS Conference 2017 showcases work from DAC Ph.D. students

 

 

 

 

 

 

 

 

 

 

 

 

A group of Ph.D. students from the Discovery Analytics Center headed with their faculty advisors to Long Beach, California, last week to present papers and posters at the 2017 Conference on Neural Information Processing Systems (NIPS). One of the workshop papers was distinguished with a Best Paper Award and two of the students received NIPS Travel Awards.

2017 marks the 31^st year for the international multi-track machine learning and computational neuroscience conference includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers, and workshops.

The Women in Machine Learning Workshop was held in conjunction with this year’s NIPS conference and DAC students presented during that event as well.

At the main conference, Sirui Yao presented “Beyond Parity: Fairness Objectives for Collaborative Filtering” (Yao and Bert Huang, assistant professor of computer science). An overview video for the paper can be viewed on YouTube.

DAC faculty Jia-Bin Huang, assistant professor of electrical and computer engineering collaborated on two papers which were also presented at the main NIPS conference: “Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks” (with the University of California MERCED); and “MaskRNN: Instance Level Video Object Segmentation” (with the University of Illinois in Urbana-Champaign).

Yuliang Zou presented “Label-Efficient Learning of Transferable Representations across Domains and Tasks” (Zou collaborating with Stanford University and the University of California, Berkeley).

Both Yao and Zou received NIPS Travel Awards.

A Best Paper award went to “Co-trained Ensemble Models for Weakly Supervised Cyberbullying Detection” (Elaheh Raisi and Bert Huang), presented by Raisi during the conference workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond.

“Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight” (Jia-Bin Huang and Yen-Chen Lin, visiting student, collaborating with Nvidia Research and the National Tsing Hua University in Taiwan) was presented by Lin at the conference workshop on Machine Deception.

Raisi and Yao presented posters at the Women in Machine Learning Workshop. Raisi presented “A Weakly Supervised Deep Model for Cyberbullying Detection” (Elaheh Raisi, Bert Huang); and Yao presented “Fairness and Accuracy in Recommendation with Imbalanced Data Sparsity” (Sirui Yao, Bert Huang).