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Josh Gardner 0001
Person information
- affiliation: University of Washington, USA
- affiliation: Google Research, Brain Team, USA
- affiliation (former): University of Michigan, Ann Arbor, MI, USA
Other persons with the same name
- Joshua Gardner 0002 (aka: Josh Gardner 0002) — State University of New York at Buffalo, NY, USA
- Joshua Gardner 0003 — Bangor University, UK
- Joshua Gardner 0004 — Princeton University, NJ, USA
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2020 – today
- 2024
- [c21]Joshua Patrick Gardner, Simon Durand, Daniel Stoller, Rachel M. Bittner:
LLark: A Multimodal Instruction-Following Language Model for Music. ICML 2024 - [i20]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 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 abs/2406.11794 (2024) - [i19]Josh Gardner, Juan C. Perdomo, Ludwig Schmidt:
Large Scale Transfer Learning for Tabular Data via Language Modeling. CoRR abs/2406.12031 (2024) - 2023
- [c20]Joshua Gardner
, Renzhe Yu
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity. FAccT 2023: 1664-1684 - [c19]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 - [c18]Josh Gardner, Zoran Popovic, Ludwig Schmidt:
Benchmarking Distribution Shift in Tabular Data with TableShift. NeurIPS 2023 - [i18]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 abs/2305.00927 (2023) - [i17]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 abs/2308.01390 (2023) - [i16]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 abs/2308.06595 (2023) - [i15]Josh Gardner, Simon Durand, Daniel Stoller, Rachel M. Bittner:
LLark: A Multimodal Foundation Model for Music. CoRR abs/2310.07160 (2023) - [i14]Josh Gardner, Zoran Popovic, Ludwig Schmidt:
Benchmarking Distribution Shift in Tabular Data with TableShift. CoRR abs/2312.07577 (2023) - 2022
- [c17]Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse H. Engel:
MT3: Multi-Task Multitrack Music Transcription. ICLR 2022 - [c16]Ian Simon, Josh Gardner, Curtis Hawthorne, Ethan Manilow, Jesse H. Engel:
Scaling Polyphonic Transcription with Mixtures of Monophonic Transcriptions. ISMIR 2022: 44-51 - [c15]Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, Jesse H. Engel:
Multi-instrument Music Synthesis with Spectrogram Diffusion. ISMIR 2022: 598-607 - [c14]Josh Gardner, Zoran Popovic, Ludwig Schmidt:
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. NeurIPS 2022 - [i13]Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, Jesse H. Engel:
Multi-instrument Music Synthesis with Spectrogram Diffusion. CoRR abs/2206.05408 (2022) - [i12]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 abs/2209.14458 (2022) - [i11]Josh Gardner, Zoran Popovic, Ludwig Schmidt:
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. CoRR abs/2211.12703 (2022) - 2021
- [j5]Christopher Brooks
Towards Culturally Relevant Personalization at Scale: Experiments with Data Science Learners. Int. J. Artif. Intell. Educ. 31(3): 516-537 (2021) - [j4]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [i10]Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse H. Engel:
MT3: Multi-Task Multitrack Music Transcription. CoRR abs/2111.03017 (2021) - 2020
- [c13]Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi
Driving with Data in the Motor City: Understanding and Predicting Fleet Maintenance Patterns. DSAA 2020: 380-389 - [i9]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 abs/2002.10010 (2020)
2010 – 2019
- 2019
- [c12]Josh Gardner, Yuming Yang, Ryan S. Baker, Christopher Brooks:
Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective. EDM 2019 - [c11]Josh Gardner, Christopher Brooks, Ryan Baker:
Evaluating the Fairness of Predictive Student Models Through Slicing Analysis. LAK 2019: 225-234 - [c10]Timothy NeCamp, Josh Gardner, Christopher Brooks:
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments. LAK 2019: 539-548 - [i8]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
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [j3]Joshua Patrick Gardner, Christopher Brooks:
Evaluating Predictive Models of Student Success: Closing the Methodological Gap. J. Learn. Anal. 5(2): 105-125 (2018) - [j2]Joshua Patrick Gardner, Christopher Brooks, Warren Li:
Learn From Your (Markov) Neighbor: Coenrollment, Assortativity, and Grade Prediction in Undergraduate Courses. J. Learn. Anal. 5(3) (2018) - [j1]Josh Gardner
Student success prediction in MOOCs. User Model. User Adapt. Interact. 28(2): 127-203 (2018) - [c9]Josh Gardner, Christopher Brooks:
Dropout Model Evaluation in MOOCs. AAAI 2018: 7906-7912 - [c8]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: 3235-3244 - [c7]Christopher Brooks, Josh Gardner, Kaifeng Chen:
How Gender Cues in Educational Video Impact Participation and Retention. ICLS 2018 - [c6]Josh Gardner, Christopher Brooks:
Coenrollment networks and their relationship to grades in undergraduate education. LAK 2018: 295-304 - [c5]Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan Baker:
Replicating MOOC predictive models at scale. L@S 2018: 1:1-1:10 - [i7]Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan S. Baker:
MORF: A Framework for MOOC Predictive Modeling and Replication At Scale. CoRR abs/1801.05236 (2018) - [i6]Josh Gardner, Christopher Brooks:
Evaluating Predictive Models of Student Success: Closing the Methodological Gap. CoRR abs/1801.08494 (2018) - [i5]Josh Gardner, Christopher Brooks:
Dropout Model Evaluation in MOOCs. CoRR abs/1802.06009 (2018) - [i4]Josh Gardner, Yuming Yang, Ryan S. Baker, Christopher Brooks:
Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining. CoRR abs/1806.05208 (2018) - [i3]Timothy NeCamp, Josh Gardner, Christopher Brooks:
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments. CoRR abs/1810.11185 (2018) - 2017
- [c4]Josh Gardner, Christopher Brooks:
Toward Replicable Predictive Model Evaluation in MOOCs. EDM 2017 - [c3]Josh Gardner, Christopher Brooks:
Statistical Approaches to the Model Comparison Task in Learning Analytics. MLA/BLAC@LAK 2017 - [c2]Josh Gardner, Ogechi Onuoha, Christopher Brooks:
Integrating syllabus data into student success models. LAK 2017: 586-587 - [c1]Josh Gardner, Christopher Brooks:
A Statistical Framework for Predictive Model Evaluation in MOOCs. L@S 2017: 269-272 - [i2]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 abs/1710.06839 (2017) - [i1]Josh Gardner, Christopher Brooks:
Student Success Prediction in MOOCs. CoRR abs/1711.06349 (2017)
Coauthor Index
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