Josh Gardner - RankMe

46 papers A* 7A 4B 5Journal 25Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining. CoRR Jeffrey Li, Josh Gardner, Doug Kang, Fangping Shi, Karanjeet Singh, Chun-Liang Li, Herumb Shandilya, David Hall, Oncel Tuzel, Percy Liang, Ludwig Schmidt, Hadipour Ansari, Fartash Faghri
2025	J	`jnl`	Data-Centric Lessons To Improve Speech-Language Pretraining. CoRR Vishaal Udandarao, Zhiyun Lu, Xuankai Chang, Yongqiang Wang, Violet Z. Yao, Albin Madapally Jose, Fartash Faghri, Josh Gardner, Chung-Cheng Chiu
2025	J	`jnl`	Language Models Improve When Pretraining Data Matches Target Tasks. CoRR David Mizrahi, Anders Boesen Lindbo Larsen, Jesse Allardice, Suzie Petryk, Yuri Gorokhov, Jeffrey Li, Alex Fang, Josh Gardner, Tom Gunter, Afshin Dehghan
2024	A*	`conf`	DataComp-LM: In search of the next generation of training sets for language models. NeurIPS Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar Guha, Sedrick Scott Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee F. 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, Alex Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar
2024	J	`jnl`	DataComp-LM: In search of the next generation of training sets for language models. CoRR Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee F. 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
2024	A*	`conf`	Large Scale Transfer Learning for Tabular Data via Language Modeling. NeurIPS Josh Gardner, Juan C. Perdomo, Ludwig Schmidt
2024	J	`jnl`	Large Scale Transfer Learning for Tabular Data via Language Modeling. CoRR Josh Gardner, Juan C. Perdomo, Ludwig Schmidt
2023	A*	`conf`	Benchmarking Distribution Shift in Tabular Data with TableShift. NeurIPS Josh Gardner, Zoran Popovic, Ludwig Schmidt
2023	J	`jnl`	Benchmarking Distribution Shift in Tabular Data with TableShift. CoRR Josh Gardner, Zoran Popovic, Ludwig Schmidt
2023	J	`jnl`	Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity. CoRR Josh Gardner, Renzhe Yu, Quan Nguyen, Christopher Brooks, René F. Kizilcec
2023	J	`jnl`	LLark: A Multimodal Foundation Model for Music. CoRR Josh Gardner, Simon Durand, Daniel Stoller, Rachel M. Bittner
2023	J	`jnl`	OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models. CoRR 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
2023	J	`jnl`	VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use. CoRR Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, Ludwig Schmidt
2023	A*	`conf`	VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models. NeurIPS Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, Ludwig Schmidt
2022	A*	`conf`	MT3: Multi-Task Multitrack Music Transcription. ICLR Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse H. Engel
2022	—	`conf`	Multi-instrument Music Synthesis with Spectrogram Diffusion. ISMIR Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, Jesse H. Engel
2022	J	`jnl`	Multi-instrument Music Synthesis with Spectrogram Diffusion. CoRR Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, Jesse H. Engel
2022	—	`conf`	Scaling Polyphonic Transcription with Mixtures of Monophonic Transcriptions. ISMIR Ian Simon, Josh Gardner, Curtis Hawthorne, Ethan Manilow, Jesse H. Engel
2022	A*	`conf`	Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. NeurIPS Josh Gardner, Zoran Popovic, Ludwig Schmidt
2022	J	`jnl`	Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. CoRR Josh Gardner, Zoran Popovic, Ludwig Schmidt
2022	J	`jnl`	The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling. CoRR Yusong Wu, Josh Gardner, Ethan Manilow, Ian Simon, Curtis Hawthorne, Jesse H. Engel
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	J	`jnl`	MT3: Multi-Task Multitrack Music Transcription. CoRR Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse H. Engel
2020	J	`jnl`	Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data. CoRR Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra
2020	B	`conf`	Driving with Data in the Motor City: Understanding and Predicting Fleet Maintenance Patterns. DSAA Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	A	`conf`	Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments. LAK Timothy NeCamp, Josh Gardner, Christopher Brooks
2019	A	`conf`	Evaluating the Fairness of Predictive Student Models Through Slicing Analysis. LAK Josh Gardner, Christopher Brooks, Ryan Baker
2019	B	`conf`	Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective. EDM Josh Gardner, Yuming Yang, Ryan S. Baker, Christopher Brooks
2018	J	`jnl`	Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments. CoRR Timothy NeCamp, Josh Gardner, Christopher Brooks
2018	A	`conf`	Coenrollment networks and their relationship to grades in undergraduate education. LAK Josh Gardner, Christopher Brooks
2018	A*	`conf`	Dropout Model Evaluation in MOOCs. AAAI Josh Gardner, Christopher Brooks
2018	J	`jnl`	Dropout Model Evaluation in MOOCs. CoRR Josh Gardner, Christopher Brooks
2018	J	`jnl`	Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining. CoRR Josh Gardner, Yuming Yang, Ryan S. Baker, Christopher Brooks
2018	J	`jnl`	Evaluating Predictive Models of Student Success: Closing the Methodological Gap. CoRR Josh Gardner, Christopher Brooks
2018	—	`conf`	How Gender Cues in Educational Video Impact Participation and Retention. ICLS Christopher Brooks, Josh Gardner, Kaifeng Chen
2018	J	`jnl`	MORF: A Framework for MOOC Predictive Modeling and Replication At Scale. CoRR Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan S. Baker
2018	—	`conf`	MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data. IEEE BigData Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan S. Baker
2018	B	`conf`	Replicating MOOC predictive models at scale. L@S Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan Baker
2018	J	`jnl`	Student success prediction in MOOCs. User Model. User Adapt. Interact. Josh Gardner, Christopher Brooks
2017	B	`conf`	A Statistical Framework for Predictive Model Evaluation in MOOCs. L@S Josh Gardner, Christopher Brooks
2017	J	`jnl`	Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit. CoRR Josh Gardner, Danai Koutra, Jawad Mroueh, Victor Pang, Arya Farahi, Sam Krassenstein, Jared Webb
2017	A	`conf`	Integrating syllabus data into student success models. LAK Josh Gardner, Ogechi Onuoha, Christopher Brooks
2017	—	`conf`	Statistical Approaches to the Model Comparison Task in Learning Analytics. MLA/BLAC@LAK Josh Gardner, Christopher Brooks
2017	J	`jnl`	Student Success Prediction in MOOCs. CoRR Josh Gardner, Christopher Brooks
2017	B	`conf`	Toward Replicable Predictive Model Evaluation in MOOCs. EDM Josh Gardner, Christopher Brooks