Jake M. Hofman

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 23

Top Topics

Binary Space Partitioning

Interactive Visualizations

Observational Data

Top Venues

Publications

Sofia Eleni Spatharioti, Daniel G. Goldstein, Jake M. Hofman
Using Open Data to Automatically Generate Localized Analogies. CHI (2024)
Hancheng Cao, Sofia Eleni Spatharioti, Daniel G. Goldstein, Jake M. Hofman
Comparing scalable strategies for generating numerical perspectives. CoRR (2023)
Sofia Eleni Spatharioti, David M. Rothschild, Daniel G. Goldstein, Jake M. Hofman
Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment. CoRR (2023)
Sayash Kapoor, Emily Cantrell, Kenny Peng, Thanh Hien Pham, Christopher A. Bail, Odd Erik Gundersen, Jake M. Hofman, Jessica Hullman, Michael A. Lones, Momin M. Malik, Priyanka Nanayakkara, Russell A. Poldrack, Inioluwa Deborah Raji, Michael Roberts, Matthew J. Salganik, Marta Serra-Garcia, Brandon M. Stewart, Gilles Vandewiele, Arvind Narayanan
REFORMS: Reporting Standards for Machine Learning Based Science. CoRR (2023)
Jake M. Hofman, Angelos Chatzimparmpas, Amit Sharma, Duncan J. Watts, Jessica Hullman
Pre-registration for Predictive Modeling. CoRR (2023)
Jamelle Watson-Daniels, Solon Barocas, Jake M. Hofman, Alexandra Chouldechova
Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints. CoRR (2023)
Jamelle Watson-Daniels, Solon Barocas, Jake M. Hofman, Alexandra Chouldechova
Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints. FAccT (2023)
Cindy Xiong, Ali Sarvghad, Daniel G. Goldstein, Jake M. Hofman, Çagatay Demiralp
Investigating Perceptual Biases in Icon Arrays. CHI (2022)
Huy Anh Nguyen, Jake M. Hofman, Daniel G. Goldstein
Round Numbers Can Sharpen Cognition. CHI (2022)
Yea-Seul Kim, Jake M. Hofman, Daniel G. Goldstein
Putting scientific results in perspective: Improving the communication of standardized effect sizes. CHI (2022)
Xiaoying Pu, Sean Kross, Jake M. Hofman, Daniel G. Goldstein
Datamations: Animated Explanations of Data Analysis Pipelines. CHI (2021)
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach
Manipulating and Measuring Model Interpretability. CHI (2021)
Gabriel Kaptchuk, Daniel G. Goldstein, Eszter Hargittai, Jake M. Hofman, Elissa M. Redmiles
How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt. CoRR (2020)
Jake M. Hofman, Daniel G. Goldstein, Siddhartha Sen, Forough Poursabzi-Sangdeh
Expanding the Scope of Reproducibility Research Through Data Analysis Replications. WWW (Companion Volume) (2020)
Jake M. Hofman, Daniel G. Goldstein, Jessica Hullman
How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results. CHI (2020)
Christopher Riederer, Jake M. Hofman, Daniel G. Goldstein
To Put That in Perspective: Generating Analogies that Make Numbers Easier to Understand. CHI (2018)
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach
Manipulating and Measuring Model Interpretability. CoRR (2018)
Omar Alonso, Vasileios Kandylas, Serge-Eric Tremblay, Jake M. Hofman, Siddhartha Sen
What's Happening and What Happened: Searching the Social Web. WebSci (2017)
Sharad Goel, Ashton Anderson, Jake M. Hofman, Duncan J. Watts
The Structural Virality of Online Diffusion. Manag. Sci. 62 (1) (2016)
Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts
Exploring Limits to Prediction in Complex Social Systems. WWW (2016)
Amit Sharma, Jake M. Hofman, Duncan J. Watts
Split-door criterion for causal identification: Automatic search for natural experiments. CoRR (2016)
Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts
Exploring limits to prediction in complex social systems. CoRR (2016)
Pablo Javier Barrio, Daniel G. Goldstein, Jake M. Hofman
Improving Comprehension of Numbers in the News. CHI (2016)
Amit Sharma, Jake M. Hofman, Duncan J. Watts
Estimating the Causal Impact of Recommendation Systems from Observational Data. CoRR (2015)
Amit Sharma, Jake M. Hofman, Duncan J. Watts
Estimating the Causal Impact of Recommendation Systems from Observational Data. EC (2015)
Prem Gopalan, Jake M. Hofman, David M. Blei
Scalable Recommendation with Hierarchical Poisson Factorization. UAI (2015)
Allison June-Barlow Chaney, Mike Gartrell, Jake M. Hofman, John Guiver, Noam Koenigstein, Pushmeet Kohli, Ulrich Paquet
A large-scale exploration of group viewing patterns. TVX (2014)
Quang Duong, Sharad Goel, Jake M. Hofman, Sergei Vassilvitskii
Sharding social networks. WSDM (2013)
Prem Gopalan, Jake M. Hofman, David M. Blei
Scalable Recommendation with Poisson Factorization. CoRR (2013)
Sharad Goel, Jake M. Hofman, M. Irmak Sirer
Who Does What on the Web: A Large-Scale Study of Browsing Behavior. ICWSM (2012)
Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts
Everyone's an influencer: quantifying influence on twitter. WSDM (2011)
Shaomei Wu, Jake M. Hofman, Winter A. Mason, Duncan J. Watts
Who says what to whom on twitter. WWW (2011)
Munmun De Choudhury, Winter A. Mason, Jake M. Hofman, Duncan J. Watts
Inferring relevant social networks from interpersonal communication. WWW (2010)
Jonathan E. Bronson, Jake M. Hofman, Jingyi Fei, Ruben L. Gonzalez, Chris H. Wiggins
Graphical models for inferring single molecule dynamics. BMC Bioinform. 11 (S-8) (2010)
Sharad Goel, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, Duncan J. Watts
Predicting consumer behavior with Web search. Proc. Natl. Acad. Sci. USA 107 (41) (2010)
R. Dean Malmgren, Jake M. Hofman, Luís A. Nunes Amaral, Duncan J. Watts
Characterizing Individual Communication Patterns CoRR (2009)
R. Dean Malmgren, Jake M. Hofman, Luís A. Nunes Amaral, Duncan J. Watts
Characterizing individual communication patterns. KDD (2009)