Toward Replicable Predictive Model Evaluation in MOOCs. - Josh Gardner 001; Christopher Brooks 001 (EDM, 2017)

Statistical Approaches to the Model Comparison Task in Learning Analytics. - Josh Gardner 001; Christopher Brooks 001 (MLA/BLAC@LAK, 2017)

Integrating syllabus data into student success models. - Josh Gardner 001; Ogechi Onuoha; Christopher Brooks 001 (LAK, 2017)

A Statistical Framework for Predictive Model Evaluation in MOOCs. - Josh Gardner 001; Christopher Brooks 001 (L@S, 2017)

Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit. - Josh Gardner 001; Danai Koutra; Jawad Mroueh; Victor Pang; Arya Farahi; Sam Krassenstein; Jared Webb (CoRR, 2017)

Dropout Model Evaluation in MOOCs. - Josh Gardner 001; Christopher Brooks 001 (AAAI, 2018)

MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data. - Josh Gardner 001; Christopher Brooks 001; Juan Miguel L. Andres; Ryan S. Baker 001 (IEEE BigData, 2018)

How Gender Cues in Educational Video Impact Participation and Retention. - Christopher Brooks 001; Josh Gardner 001; Kaifeng Chen (ICLS, 2018)

Coenrollment networks and their relationship to grades in undergraduate education. - Josh Gardner 001; Christopher Brooks 001 (LAK, 2018)

Replicating MOOC predictive models at scale. - Josh Gardner 001; Christopher Brooks 001; Juan Miguel L. Andres; Ryan Baker 001 (L@S, 2018)

MORF: A Framework for MOOC Predictive Modeling and Replication At Scale. - Josh Gardner 001; Christopher Brooks 001; Juan Miguel L. Andres; Ryan S. Baker 001 (CoRR, 2018)

Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining. - Josh Gardner 001; Yuming Yang; Ryan S. Baker 001; Christopher Brooks 001 (CoRR, 2018)

Student success prediction in MOOCs. - Josh Gardner 001; Christopher Brooks 001 (User Model. User Adapt. Interact., 2018)

Evaluating Predictive Models of Student Success: Closing the Methodological Gap. - Joshua Patrick Gardner; Christopher Brooks 001 (J. Learn. Anal., 2018)

Learn From Your (Markov) Neighbor: Coenrollment, Assortativity, and Grade Prediction in Undergraduate Courses. - Joshua Patrick Gardner; Christopher Brooks 001; Warren Li (J. Learn. Anal., 2018)

Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective. - Josh Gardner 001; Yuming Yang; Ryan S. Baker 001; Christopher Brooks 001 (EDM, 2019)

Evaluating the Fairness of Predictive Student Models Through Slicing Analysis. - Josh Gardner 001; Christopher Brooks 001; Ryan Baker 001 (LAK, 2019)

Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments. - Timothy NeCamp; Josh Gardner 001; Christopher Brooks 001 (LAK, 2019)

Advances and Open Problems in Federated Learning. - 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; et al. (CoRR, 2019)

Driving with Data in the Motor City: Understanding and Predicting Fleet Maintenance Patterns. - Josh Gardner 001; Jawad Mroueh; Natalia Jenuwine; Noah Weaverdyck; Samuel Krassenstein; Arya Farahi; Danai Koutra (DSAA, 2020)

Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data. - Josh Gardner 001; Jawad Mroueh; Natalia Jenuwine; Noah Weaverdyck; Samuel Krassenstein; Arya Farahi; Danai Koutra (CoRR, 2020)

Towards Culturally Relevant Personalization at Scale: Experiments with Data Science Learners. - Christopher Brooks 001; Rebecca M. Quintana; Heeryung Choi; Chris Quintana; Timothy NeCamp; Joshua Gardner 001 (Int. J. Artif. Intell. Educ., 2021)

Advances and Open Problems in Federated Learning. - 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; et al. (Found. Trends Mach. Learn., 2021)

MT3: Multi-Task Multitrack Music Transcription. - Josh Gardner 001; Ian Simon; Ethan Manilow; Curtis Hawthorne; Jesse H. Engel (ICLR, 2022)

Multi-instrument Music Synthesis with Spectrogram Diffusion. - Curtis Hawthorne; Ian Simon; Adam Roberts; Neil Zeghidour; Josh Gardner 001; Ethan Manilow; Jesse H. Engel (ISMIR, 2022)

Scaling Polyphonic Transcription with Mixtures of Monophonic Transcriptions. - Ian Simon; Josh Gardner 001; Curtis Hawthorne; Ethan Manilow; Jesse H. Engel (ISMIR, 2022)

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. - Josh Gardner 001; Zoran Popovic; Ludwig Schmidt (NeurIPS, 2022)

The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling. - Yusong Wu; Josh Gardner 001; Ethan Manilow; Ian Simon; Curtis Hawthorne; Jesse H. Engel (CoRR, 2022)

Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity. - Joshua Gardner 001; Renzhe Yu; Quan Nguyen 003; Christopher Brooks 001; René F. Kizilcec (FAccT, 2023)

VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models. - Yonatan Bitton; Hritik Bansal; Jack Hessel; Rulin Shao; Wanrong Zhu; Anas Awadalla; Josh Gardner 001; Rohan Taori; Ludwig Schmidt (NeurIPS, 2023)

Benchmarking Distribution Shift in Tabular Data with TableShift. - Josh Gardner 001; Zoran Popovic; Ludwig Schmidt (NeurIPS, 2023)

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models. - Anas Awadalla; Irena Gao; Josh Gardner 001; Jack Hessel; Yusuf Hanafy; Wanrong Zhu; Kalyani Marathe; Yonatan Bitton; Samir Yitzhak Gadre; Shiori Sagawa; Jenia Jitsev; Simon Kornblith; et al. (CoRR, 2023)

VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use. - Yonatan Bitton; Hritik Bansal; Jack Hessel; Rulin Shao; Wanrong Zhu; Anas Awadalla; Josh Gardner 001; Rohan Taori; Ludwig Schmidt (CoRR, 2023)

LLark: A Multimodal Foundation Model for Music. - Josh Gardner 001; Simon Durand; Daniel Stoller; Rachel M. Bittner (CoRR, 2023)

LLark: A Multimodal Instruction-Following Language Model for Music. - Joshua Patrick Gardner; Simon Durand; Daniel Stoller; Rachel M. Bittner (ICML, 2024)

Large Scale Transfer Learning for Tabular Data via Language Modeling. - Josh Gardner 001; Juan C. Perdomo; Ludwig Schmidt (NeurIPS, 2024)

DataComp-LM: In search of the next generation of training sets for language models. - 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; et al. (NeurIPS, 2024)

Toward Robust, Reliable, and Generalizable Models for Tabular Data - Joshua Gardner 001 (2024)

Language Models Improve When Pretraining Data Matches Target Tasks. - David Mizrahi; Anders Boesen Lindbo Larsen; Jesse Allardice; Suzie Petryk; Yuri Gorokhov; Jeffrey Li; Alex Fang; Josh Gardner 001; Tom Gunter; Afshin Dehghan (CoRR, 2025)