Papers By Topic

Aniket Kittur, Lixiu Yu, Tom Hope, Joel Chan, Hila Lifshitz-Assaf, Karni Gilon, Felicia Ng, Robert E. Kraut, and Dafna Shahaf, Scaling Up Analogy-based Innovation with Crowds and AI
PNAS (to appear)


Joel Chan, Joseph Chee Chang, Tom Hope, Dafna Shahaf, and Aniket Kittur, SOLVENT: A Mixed Initiative System for Finding Analogies between Research Papers
CSCW 2018


Karni Gilon, Felicia Ng, Joel Chan, Dafna Shahaf and Aniket Kittur, Analogy Mining for Specific Design Needs
CHI 2018


Tom Hope, Joel Chan, Aniket Kittur and Dafna Shahaf, Accelerating Innovation Through Analogy Mining
KDD 2017


 Best Research Paper and Best Student Research Paper, KDD'17

Joel Chan, Tom Hope, Dafna Shahaf and Aniket Kittur, Scaling up Analogy with Crowdsourcing and Machine Learning
ICCBR-16 Computational Analogy Workshop


David Tsurel, Dan Pelleg, Ido Guy and Dafna Shahaf, Fun Facts: Automatic Trivia Fact Extraction from Wikipedia
WSDM 2017


Dafna Shahaf, A Hard Look at Soft Concepts
IJCAI 2016


 Early Career Award

Dafna Shahaf, Eric Horvitz and Robert Mankoff, Inside Jokes: Identifying Humorous Cartoon Captions
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2015. Also presented at the 2015 International Humor Conference.

We study the influence of the language of cartoon captions on the perceived humorousness of the cartoons. Our studies are based on a large corpus of crowdsourced cartoon captions that were submitted to a contest hosted by the New Yorker.
 Also became a book chapter: The Languages of Humor: Verbal, Visual, and Physical Humor, Bloomsbury Academic, 2018

Tom Hope and Dafna Shahaf, Ballpark Learning: Estimating Labels from Rough Group Comparison
ECML-PKDD 2016


 MIT Tech Review: This week's most thought-provoking papers from the Physics arXiv. (Link)
 https://github.com/ttthhh/ballpark.git

Dafna Shahaf, Carlos Guestrin, Eric Horvitz, Jure Leskovec, Information Cartography
Communications of the ACM (CACM), 2016



Dafna Shahaf, Jaewon Yang, Caroline Suen, Jeff Jacobs, Heidi Wang and Jure Leskovec, Information Cartography: Creating Zoomable, Large-Scale Maps of Information
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013

We propose a methodology for creating structured summaries of information, which we call zoomable metro maps. Just as cartographic maps have been relied upon for centuries to help us understand our surroundings, metro maps can help us understand the information landscape. [...] As different users might be interested in different levels of granularity, the maps are zoomable, with each level of zoom showing finer details and interactions.

Dafna Shahaf, Carlos Guestrin and Eric Horvitz, Metro Maps of Information
ACM SIGWEB Newsletter, 2013


Dafna Shahaf, Carlos Guestrin and Eric Horvitz, Metro Maps of Science
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2012



Dafna Shahaf, Carlos Guestrin and Eric Horvitz, Trains of Thought: Generating Information Maps
International World Wide Web Conference (WWW), 2012

Complex stories spaghetti into branches, side stories, and intertwining narratives. In order to explore these stories, one needs a map to navigate unfamiliar territory. We propose a methodology for creating structured summaries of information, which we call metro maps. [...] Most importantly, metro maps explicitly show the relations among retrieved pieces in a way that captures story development.

Dafna Shahaf and Carlos Guestrin, Connecting Two (or Less) Dots: Discovering Structure in News Articles
ACM Transactions on Knowledge Discovery from Data, 2011.


Dafna Shahaf and Carlos Guestrin, Connecting the Dots Between News Articles.
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2010. Also presented at IJCAI'11


 Best Research Paper, KDD'10

Khalid El-Arini, Gaurav Veda, Dafna Shahaf, and Carlos Guestrin, Turning Down the Noise in the Blogosphere
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2009

In recent years, the blogosphere has experienced a substantial increase in the number of posts published daily, forcing users to cope with information overload. [...] we present a principled approach for picking a set of posts that best covers the important stories in the blogosphere. [...] In addition, since people have varied interests, our coverage algorithm incorporates user preferences in order to tailor the selected posts to individual tastes.

Dafna Shahaf and Eric Horvitz, Generalized task markets for human and machine computation
The Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI) 2010

We discuss challenges and opportunities for developing generalized task markets where human and machine intelligence are enlisted to solve problems, based on a consideration of the competencies, availabilities, and pricing of different problemsolving resources. The approach couples human computation with machine learning and planning, and is aimed at optimizing the flow of subtasks to people and to computational problem solvers. We illustrate key ideas in the context of Lingua Mechanica, a project focused on harnessing human and machine translation skills to perform translation among languages.
 3rd most-cited paper of AAAI'10 (@2014)

Dafna Shahaf and Eyal Amir, Towards a Theory of AI-Completeness
CommonSense 2007

(A thought experiment: complexity models for computational problems that include a human in the process. Take with a grain of salt)

Yossi Azar, Eric Horvitz, Eyal Lubetzky, Yuval Peres, and Dafna Shahaf, Tractable near-optimal policies for crawling
PNAS, 2018


Asaf Valadarsky, Michael Schapira, Dafna Shahaf, and Aviv Tamar, Learning to Route
HotNets, 2017