Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Daniel E. Ho

Daniel E. Ho

Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits


Oct 25, 2021
Ben Chugg, Daniel E. Ho

Figure 1 for Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
Figure 2 for Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
Figure 3 for Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
* Published in Machine Learning in Public Health Workshop at NeurIPS 2021 
  Access Paper or Ask Questions

On the Opportunities and Risks of Foundation Models


Aug 18, 2021
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

Figure 1 for On the Opportunities and Risks of Foundation Models
Figure 2 for On the Opportunities and Risks of Foundation Models
Figure 3 for On the Opportunities and Risks of Foundation Models
Figure 4 for On the Opportunities and Risks of Foundation Models
* Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) 
  Access Paper or Ask Questions

Context-Aware Legal Citation Recommendation using Deep Learning


Jun 20, 2021
Zihan Huang, Charles Low, Mengqiu Teng, Hongyi Zhang, Daniel E. Ho, Mark S. Krass, Matthias Grabmair

Figure 1 for Context-Aware Legal Citation Recommendation using Deep Learning
Figure 2 for Context-Aware Legal Citation Recommendation using Deep Learning
Figure 3 for Context-Aware Legal Citation Recommendation using Deep Learning
Figure 4 for Context-Aware Legal Citation Recommendation using Deep Learning
* 10 pages published in Proceedings of ICAIL 2021; link to data here: https://reglab.stanford.edu/data/bva-case-citation-dataset ; code available here: https://github.com/TUMLegalTech/bva-citation-prediction 
  Access Paper or Ask Questions

Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms


May 29, 2021
Ben Chugg, Brandon Anderson, Seiji Eicher, Sandy Lee, Daniel E. Ho

Figure 1 for Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
Figure 2 for Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
Figure 3 for Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
Figure 4 for Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms

  Access Paper or Ask Questions

When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset


May 17, 2021
Lucia Zheng, Neel Guha, Brandon R. Anderson, Peter Henderson, Daniel E. Ho

Figure 1 for When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
Figure 2 for When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
Figure 3 for When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
Figure 4 for When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
* ICAIL 2021. Code & data available at https://github.com/reglab/casehold 
  Access Paper or Ask Questions

Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery


Mar 17, 2021
Caleb Robinson, Anthony Ortiz, Juan M. Lavista Ferres, Brandon Anderson, Daniel E. Ho

Figure 1 for Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
Figure 2 for Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
Figure 3 for Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
Figure 4 for Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery

  Access Paper or Ask Questions

Affirmative Algorithms: The Legal Grounds for Fairness as Awareness


Dec 18, 2020
Daniel E. Ho, Alice Xiang

Figure 1 for Affirmative Algorithms: The Legal Grounds for Fairness as Awareness
Figure 2 for Affirmative Algorithms: The Legal Grounds for Fairness as Awareness
Figure 3 for Affirmative Algorithms: The Legal Grounds for Fairness as Awareness
* 10/30/20 U. Chi. L. Rev. Online 143, https://lawreviewblog.uchicago.edu/2020/10/30/aa-ho-xiang/ 
 * 12 pages, 3 figures 
  Access Paper or Ask Questions