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ORCIDJake M. Hofman
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Journal Articles
- 2016
[j3]Sharad Goel, Ashton Anderson, Jake M. Hofman, Duncan J. Watts:
The Structural Virality of Online Diffusion. Manag. Sci. 62(1): 180-196 (2016)- 2010
- [j2]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): S2 (2010) - [j1]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): 17486-17490 (2010)
Conference and Workshop Papers
- 2024
- [c22]Sofia Eleni Spatharioti
, Daniel G. Goldstein, Jake M. Hofman:
Using Open Data to Automatically Generate Localized Analogies. CHI 2024: 1036:1-1036:13 - 2023
- [c21]Jamelle Watson-Daniels, Solon Barocas, Jake M. Hofman, Alexandra Chouldechova:
Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints. FAccT 2023: 297-311 - 2022
- [c20]Cindy Xiong, Ali Sarvghad, Daniel G. Goldstein, Jake M. Hofman, Çagatay Demiralp:
Investigating Perceptual Biases in Icon Arrays. CHI 2022: 137:1-137:12 - [c19]Huy Anh Nguyen, Jake M. Hofman, Daniel G. Goldstein:
Round Numbers Can Sharpen Cognition. CHI 2022: 375:1-375:15 - [c18]Yea-Seul Kim, Jake M. Hofman, Daniel G. Goldstein:
Putting scientific results in perspective: Improving the communication of standardized effect sizes. CHI 2022: 625:1-625:14 - 2021
- [c17]Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach:
Manipulating and Measuring Model Interpretability. CHI 2021: 237:1-237:52 - [c16]Xiaoying Pu, Sean Kross, Jake M. Hofman, Daniel G. Goldstein:
Datamations: Animated Explanations of Data Analysis Pipelines. CHI 2021: 467:1-467:14 - 2020
- [c15]Jake M. Hofman, Daniel G. Goldstein, Jessica Hullman:
How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results. CHI 2020: 1-12 - [c14]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: 567-571 - 2018
- [c13]Christopher Riederer, Jake M. Hofman, Daniel G. Goldstein:
To Put That in Perspective: Generating Analogies that Make Numbers Easier to Understand. CHI 2018: 548 - 2017
- [c12]Omar Alonso, Vasileios Kandylas, Serge-Eric Tremblay, Jake M. Hofman, Siddhartha Sen:
What's Happening and What Happened: Searching the Social Web. WebSci 2017: 191-200 - 2016
- [c11]Pablo Javier Barrio, Daniel G. Goldstein, Jake M. Hofman:
Improving Comprehension of Numbers in the News. CHI 2016: 2729-2739 - [c10]Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts:
Exploring Limits to Prediction in Complex Social Systems. WWW 2016: 683-694 - 2015
- [c9]Amit Sharma, Jake M. Hofman, Duncan J. Watts:
Estimating the Causal Impact of Recommendation Systems from Observational Data. EC 2015: 453-470 - [c8]Prem Gopalan, Jake M. Hofman, David M. Blei:
Scalable Recommendation with Hierarchical Poisson Factorization. UAI 2015: 326-335 - 2014
- [c7]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: 31-38 - 2013
- [c6]Quang Duong, Sharad Goel, Jake M. Hofman, Sergei Vassilvitskii:
Sharding social networks. WSDM 2013: 223-232 - 2012
- [c5]Sharad Goel, Jake M. Hofman, M. Irmak Sirer:
Who Does What on the Web: A Large-Scale Study of Browsing Behavior. ICWSM 2012 - 2011
- [c4]Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts:
Everyone's an influencer: quantifying influence on twitter. WSDM 2011: 65-74 - [c3]Shaomei Wu, Jake M. Hofman, Winter A. Mason, Duncan J. Watts:
Who says what to whom on twitter. WWW 2011: 705-714 - 2010
- [c2]Munmun De Choudhury, Winter A. Mason, Jake M. Hofman, Duncan J. Watts:
Inferring relevant social networks from interpersonal communication. WWW 2010: 301-310 - 2009
- [c1]R. Dean Malmgren, Jake M. Hofman, Luís A. Nunes Amaral, Duncan J. Watts:
Characterizing individual communication patterns. KDD 2009: 607-616
Informal and Other Publications
- 2023
- [i12]Jamelle Watson-Daniels, Solon Barocas, Jake M. Hofman, Alexandra Chouldechova:
Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints. CoRR abs/2306.13738 (2023) - [i11]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 abs/2307.03744 (2023) - [i10]Hancheng Cao, Sofia Eleni Spatharioti, Daniel G. Goldstein, Jake M. Hofman:
Comparing scalable strategies for generating numerical perspectives. CoRR abs/2308.01535 (2023) - [i9]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 abs/2308.07832 (2023) - [i8]Jake M. Hofman, Angelos Chatzimparmpas, Amit Sharma, Duncan J. Watts, Jessica Hullman:
Pre-registration for Predictive Modeling. CoRR abs/2311.18807 (2023) - 2020
- [i7]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 abs/2005.04343 (2020) - 2018
- [i6]Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach:
Manipulating and Measuring Model Interpretability. CoRR abs/1802.07810 (2018) - 2016
- [i5]Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts:
Exploring limits to prediction in complex social systems. CoRR abs/1602.01013 (2016) - [i4]Amit Sharma, Jake M. Hofman, Duncan J. Watts:
Split-door criterion for causal identification: Automatic search for natural experiments. CoRR abs/1611.09414 (2016) - 2015
- [i3]Amit Sharma, Jake M. Hofman, Duncan J. Watts:
Estimating the Causal Impact of Recommendation Systems from Observational Data. CoRR abs/1510.05569 (2015) - 2013
- [i2]Prem Gopalan, Jake M. Hofman, David M. Blei:
Scalable Recommendation with Poisson Factorization. CoRR abs/1311.1704 (2013) - 2009
- [i1]R. Dean Malmgren, Jake M. Hofman, Luís A. Nunes Amaral, Duncan J. Watts:
Characterizing Individual Communication Patterns. CoRR abs/0905.0106 (2009)
Coauthor Index
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