Josh Gardner

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 41

Top Topics

Student Success

Factor Analyzers

Predictive Model

Top Venues

Publications

Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar
DataComp-LM: In search of the next generation of training sets for language models. CoRR (2024)
Josh Gardner, Juan C. Perdomo, Ludwig Schmidt
Large Scale Transfer Learning for Tabular Data via Language Modeling. CoRR (2024)
Josh Gardner, Renzhe Yu, Quan Nguyen, Christopher Brooks, René F. Kizilcec
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity. CoRR (2023)
Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Yitzhak Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, Ludwig Schmidt
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models. CoRR (2023)
Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, Ludwig Schmidt
VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models. NeurIPS (2023)
Josh Gardner, Zoran Popovic, Ludwig Schmidt
Benchmarking Distribution Shift in Tabular Data with TableShift. NeurIPS (2023)
Josh Gardner, Simon Durand, Daniel Stoller, Rachel M. Bittner
LLark: A Multimodal Foundation Model for Music. CoRR (2023)
Josh Gardner, Zoran Popovic, Ludwig Schmidt
Benchmarking Distribution Shift in Tabular Data with TableShift. CoRR (2023)
Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, Ludwig Schmidt
VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use. CoRR (2023)
Ian Simon, Josh Gardner, Curtis Hawthorne, Ethan Manilow, Jesse H. Engel
Scaling Polyphonic Transcription with Mixtures of Monophonic Transcriptions. ISMIR (2022)
Yusong Wu, Josh Gardner, Ethan Manilow, Ian Simon, Curtis Hawthorne, Jesse H. Engel
The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling. CoRR (2022)
Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, Jesse H. Engel
Multi-instrument Music Synthesis with Spectrogram Diffusion. ISMIR (2022)
Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse H. Engel
MT3: Multi-Task Multitrack Music Transcription. ICLR (2022)
Josh Gardner, Zoran Popovic, Ludwig Schmidt
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. NeurIPS (2022)
Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, Jesse H. Engel
Multi-instrument Music Synthesis with Spectrogram Diffusion. CoRR (2022)
Josh Gardner, Zoran Popovic, Ludwig Schmidt
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. CoRR (2022)
Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse H. Engel
MT3: Multi-Task Multitrack Music Transcription. CoRR (2021)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra
Driving with Data in the Motor City: Understanding and Predicting Fleet Maintenance Patterns. DSAA (2020)
Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra
Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data. CoRR (2020)
Josh Gardner, Yuming Yang, Ryan S. Baker, Christopher Brooks
Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective. EDM (2019)
Josh Gardner, Christopher Brooks, Ryan Baker
Evaluating the Fairness of Predictive Student Models Through Slicing Analysis. LAK (2019)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. CoRR (2019)
Timothy NeCamp, Josh Gardner, Christopher Brooks
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments. LAK (2019)
Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan S. Baker
MORF: A Framework for MOOC Predictive Modeling and Replication At Scale. CoRR (2018)
Timothy NeCamp, Josh Gardner, Christopher Brooks
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments. CoRR (2018)
Josh Gardner, Christopher Brooks
Coenrollment networks and their relationship to grades in undergraduate education. LAK (2018)
Josh Gardner, Christopher Brooks
Student success prediction in MOOCs. User Model. User Adapt. Interact. 28 (2) (2018)
Christopher Brooks, Josh Gardner, Kaifeng Chen
How Gender Cues in Educational Video Impact Participation and Retention. ICLS (2018)
Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan S. Baker
MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data. IEEE BigData (2018)
Josh Gardner, Christopher Brooks
Dropout Model Evaluation in MOOCs. AAAI (2018)
Josh Gardner, Yuming Yang, Ryan S. Baker, Christopher Brooks
Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining. CoRR (2018)
Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan Baker
Replicating MOOC predictive models at scale. L@S (2018)
Josh Gardner, Christopher Brooks
Dropout Model Evaluation in MOOCs. CoRR (2018)
Josh Gardner, Christopher Brooks
Evaluating Predictive Models of Student Success: Closing the Methodological Gap. CoRR (2018)
Josh Gardner, Christopher Brooks
Toward Replicable Predictive Model Evaluation in MOOCs. EDM (2017)
Josh Gardner, Danai Koutra, Jawad Mroueh, Victor Pang, Arya Farahi, Sam Krassenstein, Jared Webb
Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit. CoRR (2017)
Josh Gardner, Christopher Brooks
A Statistical Framework for Predictive Model Evaluation in MOOCs. L@S (2017)
Josh Gardner, Ogechi Onuoha, Christopher Brooks
Integrating syllabus data into student success models. LAK (2017)
Josh Gardner, Christopher Brooks
Student Success Prediction in MOOCs. CoRR (2017)
Josh Gardner, Christopher Brooks
Statistical Approaches to the Model Comparison Task in Learning Analytics. MLA/BLAC@LAK (2017)