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Machine learning has proved useful in many software disciplines, including computer vision, speech and audio processing, natural language processing, robotics and some other fields.
Improving black-box adversarial attacks with a transfer-based prior
Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 1906
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Sign-opt: A query-efficient hard-label adversarial attack
Minhao Cheng, Simranjit Singh, Patrick Chen, Pin-Yu Chen, Sijia Liu, and Cho-Jui Hsieh · 1909
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Steamer: An interactive inspectable simulation-based training system
James D Hollan, Edwin L Hutchins, and Louis Weitzman · 1984
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Queries and concept learning
Dana Angluin · 1988
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Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
Nick Littlestone · 1988
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Computational learning theory: survey and selected bibliography
Dana Angluin · 1992
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A survey of applications of markov decision processes
Douglas J White · 1993
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An integrative model of organizational trust
Roger C Mayer, James H Davis, and F David Schoorman · 1995
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Target-text mediated interactive machine translation
George Foster, Pierre Isabelle, and Pierre Plamondon · 1997
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Application of interactive genetic algorithm to fashion design
Hee-Su Kim and Sung-Bae Cho · 2000
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Support vector machine active learning for image retrieval
Simon Tong and Edward Chang · 2001
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Data mining: practical machine learning tools and techniques with java implementations
Ian H Witten and Eibe Frank · 2002
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A human-oriented image retrieval system using interactive genetic algorithm
Sung-Bae Cho and Joo-Young Lee · 2002
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Optimizing search engines using clickthrough data
Thorsten Joachims · 2002
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Interactive machine learning
Jerry Alan Fails and Dan R Olsen Jr · 2003
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An improved interactive genetic algorithm incorporating relevant feedback
Shang-Fei Wang, Xu-Fa Wang, and Jia Xue · 2005
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Query chains: learning to rank from implicit feedback
Filip Radlinski and Thorsten Joachims · 2005
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Cueflik: interactive concept learning in image search
James Fogarty, Desney Tan, Ashish Kapoor, and Simon Winder · 2008
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A new approach for interactive image retrieval based on fuzzy feedback and support vector machine
Malihe Javidi, Baharak Shakeri Aski, Hale Homaei, and Hamid Reza Pourreza · 2008
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Clustering with interactive feedback
Maria-Florina Balcan and Avrim Blum · 2008
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Ensemblematrix: interactive visualization to support machine learning with multiple classifiers
Justin Talbot, Bongshin Lee, Ashish Kapoor, and Desney S Tan · 2009
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Interactive emotion recognition using support vector machine for human-robot interaction
Ching-Chih Tsai, You-Zhu Chen, and Ching-Wen Liao · 2009
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The wekinator: a system for real-time, interactive machine learning in music
Rebecca Fiebrink and Perry R Cook · 2010
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Online learning for interactive statistical machine translation
Daniel Ortiz-Martínez, Ismael García-Varea, and Francisco Casacuberta · 2010
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Adversarial machine learning
Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and J Doug Tygar · 2011
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Baobabview: Interactive construction and analysis of decision trees
Stef Van Den Elzen and Jarke J Van Wijk · 2011
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Active learning and crowdsourcing for machine translation in low resource scenarios
Vamshi Ambati · 2011
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Effective end-user interaction with machine learning
Saleema Amershi, James Fogarty, Ashish Kapoor, and Desney Tan · 2011
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Wekinating 000000swan: Using machine learning to create and control complex artistic systems
Margaret Schedel, Phoenix Perry, and Rebecca Fiebrink · 2011
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Using gaussian processes to monitor diabetes development
P Qian, Y Zhou, and C Rudin · 2011
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A user-oriented image retrieval system based on interactive genetic algorithm
Chih-Chin Lai and Ying-Chuan Chen · 2011
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Regroup: Interactive machine learning for on-demand group creation in social networks
Saleema Amershi, James Fogarty, and Daniel Weld · 2012
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Deploying an interactive machine learning system in an evidence-based practice center: abstrackr
Byron C Wallace, Kevin Small, Carla E Brodley, Joseph Lau, and Thomas A Trikalinos · 2012
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Active learning for interactive machine translation
Jesús González-Rubio, Daniel Ortiz-Martínez, and Francisco Casacuberta · 2012
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Interactive machine learning in data exploitation
Reid Porter, James Theiler, and Don Hush · 2013
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Power to the people: The role of humans in interactive machine learning
Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza · 2014
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Opening the black box: Strategies for increased user involvement in existing algorithm implementations
T. Mühlbacher, H. Piringer, S. Gratzl, M. Sedlmair, and M. Streit · 2014
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Interactive learning of pattern rankings
Vladimir Dzyuba, Matthijs van Leeuwen, Siegfried Nijssen, and Luc De Raedt · 2014
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The gesture recognition toolkit
Nicholas Gillian and Joseph A Paradiso · 2014
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Infuse: interactive feature selection for predictive modeling of high dimensional data
Josua Krause, Adam Perer, and Enrico Bertini · 2014
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Local algorithms for interactive clustering
Pranjal Awasthi, Maria Balcan, and Konstantin Voevodski · 2014
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An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance
Ilker Kose, Mehmet Gokturk, and Kemal Kilic · 2015
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Principles of explanatory debugging to personalize interactive machine learning
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf · 2015
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Interactive control of diverse complex characters with neural networks
Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, and Emanuel V Todorov · 2015
Cited alongside, same era.
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Andreas Holzinger · 2016
Cited alongside, same era.
Interacting with predictions: Visual inspection of black-box machine learning models
Josua Krause, Adam Perer, and Kenney Ng · 2016
Cited alongside, same era.
"why should I trust you?": Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Simple black-box adversarial attacks
Chuan Guo, Jacob Gardner, Yurong You, Andrew Gordon Wilson, and Kilian Weinberger · 2019
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Explaining vulnerabilities to adversarial machine learning through visual analytics
Yuxin Ma, Tiankai Xie, Jundong Li, and Ross Maciejewski · 2019
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Explanatory interactive machine learning
Stefano Teso and Kristian Kersting · 2019
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Safe exploration for interactive machine learning
Matteo Turchetta, Felix Berkenkamp, and Andreas Krause · 2019
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Improving deep reinforcement learning in minecraft with action advice
Spencer Frazier and Mark Riedl · 2019
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Interactive machine learning for more expressive game interactions
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Felix Berkenkamp, Andreas Krause, and Angela P Schoellig · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
Stealing machine learning models via prediction { \{ APIs } \}
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
Cited alongside, same era.
Chissl: A human-machine collaboration space for unsupervised learning
Dustin Arendt, Caner Komurlu, and Leslie M Blaha · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
Cited alongside, same era.
Carlos Gonzalez Diaz, Phoenix Perry, and Rebecca Fiebrink · 2019
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Understanding the role of interactive machine learning in movement interaction design
Marco Gillies · 2019
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Robot learning of industrial assembly task via human demonstrations
Maria Kyrarini, Muhammad Abdul Haseeb, Danijela Ristić-Durrant, and Axel Gräser · 2019
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Interactive machine learning: experimental evidence for the human in the algorithmic loop
Andreas Holzinger, Markus Plass, Michael Kickmeier-Rust, Katharina Holzinger, Gloria Cerasela Crişan, Camelia-M Pintea, and Vasile Palade · 2019
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Towards an integrative theoretical framework of interactive machine learning systems
Miguel Angel Meza Martínez, Mario Nadj, and Alexander Maedche · 2019
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Evaluating explanation without ground truth in interpretable machine learning
Fan Yang, Mengnan Du, and Xia Hu · 2019
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Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
Chun-Chen Tu, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, and Shin-Ming Cheng · 2019
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Poisoning attack in federated learning using generative adversarial nets
Jiale Zhang, Junjun Chen, Di Wu, Bing Chen, and Shui Yu · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Mlsploit: A framework for interactive experimentation with adversarial machine learning research
Nilaksh Das, Siwei Li, Chanil Jeon, Jinho Jung, Shang-Tse Chen, Carter Yagemann, Evan Downing, Haekyu Park, Evan Yang, Li Chen, et al · 2019
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Understanding the effect of accuracy on trust in machine learning models
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach · 2019
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Ilastik: interactive machine learning for (bio) image analysis
Stuart Berg, Dominik Kutra, Thorben Kroeger, Christoph N Straehle, Bernhard X Kausler, Carsten Haubold, Martin Schiegg, Janez Ales, Thorsten Beier, Markus Rudy, et al · 2019
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An overview of interactive machine learning, Oct 2020
Jules Françoise · 2020
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Interactive machine learning for data exfiltration detection: Active learning with human expertise
Mu-Huan Chung, Mark Chignell, Lu Wang, Alexandra Jovicic, and Abhay Raman · 2020
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Massif: Interactive interpretation of adversarial attacks on deep learning
Nilaksh Das, Haekyu Park, Zijie J Wang, Fred Hohman, Robert Firstman, Emily Rogers, and Duen Horng Chau · 2020
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 2020
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Resource usage and performance trade-offs for machine learning models in smart environments
Davy Preuveneers, Ilias Tsingenopoulos, and Wouter Joosen · 2020
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Activity recognition through interactive machine learning in a dynamic sensor setting
Agnes Tegen, Paul Davidsson, and Jan A Persson · 2020
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Algorithmic improvements for deep reinforcement learning applied to interactive fiction
Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, and Marc G Bellemare · 2020
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Interactive machine learning for soybean seed and seedling quality classification
André Dantas de Medeiros, Nayara Pereira Capobiango, José Maria da Silva, Laércio Junio da Silva, Clíssia Barboza da Silva, and Denise Cunha Fernandes dos Santos Dias · 2020
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Interactive machine learning and explainability in mobile classification of forest-aesthetics
Simon Flutura, Andreas Seiderer, Tobias Huber, Katharina Weitz, Ilhan Aslan, Ruben Schlagowski, Elisabeth André, and Joachim Rathmann · 2020
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Aispace2: an interactive visualization tool for learning and teaching artificial intelligence
Chenliang Zhou, Dominic Kuang, Jingru Liu, Hanbo Yang, Zijia Zhang, Alan Mackworth, and David Poole · 2020
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Interactive genetic algorithm to collect user perceptions. application to the design of stemmed glasses
E Poirson, J-F Petiot, and David Blumenthal · 2020
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Interactive steering of hierarchical clustering
Weikai Yang, Xiting Wang, Jie Lu, Wenwen Dou, and Shixia Liu · 2020
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Simple interactive image segmentation using label propagation through knn graphs
Fabricio Aparecido Breve · 2020
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A survey of surveys on the use of visualization for interpreting machine learning models
Angelos Chatzimparmpas, Rafael M Martins, Ilir Jusufi, and Andreas Kerren · 2020
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Challenges in interactive machine learning, 2020
Stefano Teso and Oliver Hinz · 2020
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Textattack: A framework for adversarial attacks in natural language processing
John X Morris, Eli Lifland, Jin Yong Yoo, and Yanjun Qi · 2020
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The relationship between trust in ai and trustworthy machine learning technologies
Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, and Aad Van Moorsel · 2020
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Soliciting human-in-the-loop user feedback for interactive machine learning reduces user trust and impressions of model accuracy
Donald Honeycutt, Mahsan Nourani, and Eric Ragan · 2020
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Explainable active learning (xal) toward ai explanations as interfaces for machine teachers
Bhavya Ghai, Q Vera Liao, Yunfeng Zhang, Rachel Bellamy, and Klaus Mueller · 2021
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A review on human–ai interaction in machine learning and insights for medical applications
Mansoureh Maadi, Hadi Akbarzadeh Khorshidi, and Uwe Aickelin · 2021
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Heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game
Jingwen Meng, Xiaoming You, and Sheng Liu · 2021
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Quality assurance for ai-based systems: Overview and challenges (introduction to interactive session)
Michael Felderer and Rudolf Ramler · 2021
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What doesn’t kill you makes you robust (er): Adversarial training against poisons and backdoors
Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, and Tom Goldstein · 2021
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Adversarial examples in physical world
Jiakai Wang · 2021
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Transmart: A practical interactive machine translation system
Guoping Huang, Lemao Liu, Xing Wang, Longyue Wang, Huayang Li, Zhaopeng Tu, Chengyan Huang, and Shuming Shi · 2021
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