| SUPPLE: automatically generating user interfaces K Gajos, DS Weld Proceedings of the 9th international conference on Intelligent user …, 2004 | 589 | 2004 |
| Ability-based design: Concept, principles and examples JO Wobbrock, SK Kane, KZ Gajos, S Harada, J Froehlich ACM Transactions on Accessible Computing (TACCESS) 3 (3), 1-27, 2011 | 583 | 2011 |
| Understanding in-video dropouts and interaction peaks inonline lecture videos J Kim, PJ Guo, DT Seaton, P Mitros, KZ Gajos, RC Miller Proceedings of the first ACM conference on Learning@ scale conference, 31-40, 2014 | 512 | 2014 |
| Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness K Reinecke, T Yeh, L Miratrix, R Mardiko, Y Zhao, J Liu, KZ Gajos Proceedings of the SIGCHI conference on human factors in computing systems …, 2013 | 403 | 2013 |
| To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making Z Buçinca, MB Malaya, KZ Gajos Proceedings of the ACM on Human-Computer Interaction 5 (CSCW1), 1-21, 2021 | 392 | 2021 |
| Automatically generating personalized user interfaces with Supple KZ Gajos, DS Weld, JO Wobbrock Artificial intelligence 174 (12-13), 910-950, 2010 | 377 | 2010 |
| The efficacy of Web-based cognitive behavioral interventions for chronic pain: a systematic review and meta-analysis DD Macea, K Gajos, YAD Calil, F Fregni The Journal of Pain 11 (10), 917-929, 2010 | 358 | 2010 |
| Platemate: crowdsourcing nutritional analysis from food photographs J Noronha, E Hysen, H Zhang, KZ Gajos Proceedings of the 24th annual ACM symposium on User interface software and …, 2011 | 332 | 2011 |
| Opportunity knocks: A system to provide cognitive assistance with transportation services DJ Patterson, L Liao, K Gajos, M Collier, N Livic, K Olson, S Wang, D Fox, ... UbiComp 2004: Ubiquitous Computing: 6th International Conference, Nottingham …, 2004 | 282 | 2004 |
| Exploring the design space for adaptive graphical user interfaces KZ Gajos, M Czerwinski, DS Tan, DS Weld Proceedings of the working conference on Advanced visual interfaces, 201-208, 2006 | 277 | 2006 |
| Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems Z Buçinca, P Lin, KZ Gajos, EL Glassman Proceedings of the 25th international conference on intelligent user …, 2020 | 256 | 2020 |
| Improving the performance of motor-impaired users with automatically-generated, ability-based interfaces KZ Gajos, JO Wobbrock, DS Weld Proceedings of the SIGCHI conference on Human Factors in Computing Systems …, 2008 | 255 | 2008 |
| Automatically generating user interfaces adapted to users' motor and vision capabilities KZ Gajos, JO Wobbrock, DS Weld Proceedings of the 20th annual ACM symposium on User interface software and …, 2007 | 226 | 2007 |
| Crowdsourcing performance evaluations of user interfaces S Komarov, K Reinecke, KZ Gajos Proceedings of the SIGCHI conference on human factors in computing systems …, 2013 | 222 | 2013 |
| Evaluation of artery visualizations for heart disease diagnosis M Borkin, K Gajos, A Peters, D Mitsouras, S Melchionna, F Rybicki, ... IEEE transactions on visualization and computer graphics 17 (12), 2479-2488, 2011 | 215 | 2011 |
| Human computation tasks with global constraints H Zhang, E Law, R Miller, K Gajos, D Parkes, E Horvitz Proceedings of the SIGCHI Conference on Human Factors in Computing Systems …, 2012 | 213 | 2012 |
| Axis: Generating explanations at scale with learnersourcing and machine learning JJ Williams, J Kim, A Rafferty, S Maldonado, KZ Gajos, WS Lasecki, ... Proceedings of the third (2016) ACM conference on learning@ scale, 379-388, 2016 | 204 | 2016 |
| Quantifying visual preferences around the world K Reinecke, KZ Gajos Proceedings of the SIGCHI conference on human factors in computing systems …, 2014 | 196 | 2014 |
| Predictability and accuracy in adaptive user interfaces KZ Gajos, K Everitt, DS Tan, M Czerwinski, DS Weld Proceedings of the SIGCHI Conference on Human Factors in Computing Systems …, 2008 | 192 | 2008 |
| How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection M Jacobs, MF Pradier, TH McCoy Jr, RH Perlis, F Doshi-Velez, KZ Gajos Translational psychiatry 11 (1), 108, 2021 | 191 | 2021 |
Daniel WeldAllen Institute for AI & University of Washington在 cs.washington.edu 的电子邮件经过验证
