AAAI

CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

Jingyuan Wang, Xu He, Ze Wang, Junjie Wu, Nicholas Jing Yuan, Xing Xie, Zhang Xiong

Comments 8 pages, 5 figures, conference

Journal ref Jingyuan Wang, Xu He, Ze Wang, Junjie Wu, Nicholas Jing Yuan, Xing Xie, Zhang Xiong, CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition, in the Proceedings of AAAI-18, 2018

Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation

Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou

Comments Camera-ready version for AAAI/ACM AIES 2018. Data and pseudocode at https://github.com/shftan/auditblackbox. Previously titled "Detecting Bias in Black-Box Models Using Transparent Model Distillation". A short version was presented at NIPS 2017 Symposium on Interpretable Machine Learning

Apprenticeship Bootstrapping Via Deep Learning with a Safety Net for UAV-UGV Interaction

Hung Nguyen, Vu Tran, Tung Nguyen, Matthew Garratt, Kathryn Kasmarik, Michael Barlow, Sreenatha Anavatti, Hussein Abbass

Comments Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606)

Interactive Surveillance Technologies for Dense Crowds

Aniket Bera, Dinesh Manocha

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos

Yang Wang, Zhenheng Yang, Peng Wang, Yi Yang, Chenxu Luo, Wei Xu

Comments Submitted to AAAI 2019

Balancing Efficiency and Coverage in Human-Robot Dialogue Collection

Matthew Marge, Claire Bonial, Stephanie Lukin, Cory Hayes, Ashley Foots, Ron Artstein, Cassidy Henry, Kimberly Pollard, Carla Gordon, Felix Gervits, Anton Leuski, Susan Hill, Clare Voss, David Traum

Comments Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606)

Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence

Justin Hart, Harel Yedidsion, Yuqian Jiang, Nick Walker, Rishi Shah, Jesse Thomason, Aishwarya Padmakumar, Rolando Fernandez, Jivko Sinapov, Raymond Mooney, Peter Stone

Comments Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606)

FLIC: Fast Linear Iterative Clustering with Active Search

Jiaxing Zhao, Ren Bo, Qibin Hou, Ming-Ming Cheng, Paul L. Rosin

Comments AAAI 2018

Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph

Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra, Karthik Sankaranarayanan, Sarath Chandar

Comments Accepted in AAAI'18

Co-Arg: Cogent Argumentation with Crowd Elicitation

Mihai Boicu, Dorin Marcu, Gheorghe Tecuci, Lou Kaiser, Chirag Uttamsingh, Navya Kalale

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Towards Online Learning from Corrective Demonstrations

Reymundo A. Gutierrez, Elaine Schaertl Short, Scott Niekum, Andrea L. Thomaz

Comments Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606)

AI for Trustworthiness! Credible User Identification on Social Web for Disaster Response Agencies

Rahul Pandey, Hemant Purohit, Jennifer Chan, Aditya Johri

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Multimodal Interactive Learning of Primitive Actions

Tuan Do, Nikhil Krishnaswamy, Kyeongmin Rim, James Pustejovsky

Comments Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606)

Stakeholders in Explainable AI

Alun Preece, Dan Harborne, Dave Braines, Richard Tomsett, Supriyo Chakraborty

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Deep HMResNet Model for Human Activity-Aware Robotic Systems

Hazem Abdelkawy, Naouel Ayari, Abdelghani Chibani, Yacine Amirat, Ferhat Attal

Comments Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606)

A Systems Approach to Achieving the Benefits of Artificial Intelligence in UK Defence

Gavin Pearson, Phil Jolley, Geraint Evans

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Incorporating GAN for Negative Sampling in Knowledge Representation Learning

Peifeng Wang, Shuangyin Li, Rong pan

Comments Accepted to AAAI 2018

Growing and Retaining AI Talent for the United States Government

Edward Raff

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model

Ryo Nakahashi, Chris L. Baker, Joshua B. Tenenbaum

Comments Accepted at AAAI 16

Journal ref Proceedings of 30th conference on artificial intelligence (AAAI 2016) pp. 3754--3760

Federated AI for building AI Solutions across Multiple Agencies

Dinesh Verma, Simon Julier, Greg Cirincione

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Modeling Epistemological Principles for Bias Mitigation in AI Systems: An Illustration in Hiring Decisions

Marisa Vasconcelos, Carlos Cardonha, Bernardo Gonçalves

Journal ref 2018 AAAI/ACM Conference on AI, Ethics, and Society

Defining the Collective Intelligence Supply Chain

Iain Barclay, Alun Preece, Ian Taylor

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

BubbleTouch: A Quasi-Static Tactile Skin Simulator

Brayden Hollis, Stacy Patterson, Jinda Cui, Jeff Trinkle

Comments Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606)

Complexity of Shift Bribery in Committee Elections

Robert Bredereck, Piotr Faliszewski, Rolf Niedermeier, Nimrod Talmon

Comments 30 Pages. An extended abstract of this paper appears in the Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI 16)

Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams

Mahardhika Pratama, Andri Ashfahani, Yew Soon Ong, Savitha Ramasamy, Edwin Lughofer

Comments have been submitted to AAAI 2019 conference

DT-LET: Deep Transfer Learning by Exploring where to Transfer

Jianzhe Lin, Qi Wang, Rabab Ward, Z. Jane Wang

Comments Conference paper submitted to AAAI 2019

Uncertainty Aware AI ML: Why and How

Lance Kaplan, Federico Cerutti, Murat Sensoy, Alun Preece, Paul Sullivan

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Bias Amplification in Artificial Intelligence Systems

Kirsten Lloyd

Comments Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure

Besmira Nushi, Ece Kamar, Eric Horvitz

Journal ref AAAI Conference on Human Computation and Crowdsourcing 2018

Transparency and Explanation in Deep Reinforcement Learning Neural Networks

Rahul Iyer, Yuezhang Li, Huao Li, Michael Lewis, Ramitha Sundar, Katia Sycara

Comments 8 pages, 5 figures, Accepted at AAAI/ACM Conference on AI, Ethics, and Society 2018