Josh Gardner

Orcid: 0000-0002-4998-5918

Affiliations:
  • University of Washington, USA
  • Google Research, Brain Team, USA
  • University of Michigan, Ann Arbor, MI, USA (former)


According to our database1, Josh Gardner authored at least 37 papers between 2017 and 2024.

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Bibliography

2024
Large Scale Transfer Learning for Tabular Data via Language Modeling.
CoRR, 2024

DataComp-LM: In search of the next generation of training sets for language models.
CoRR, 2024

LLark: A Multimodal Instruction-Following Language Model for Music.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
LLark: A Multimodal Foundation Model for Music.
CoRR, 2023

VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use.
CoRR, 2023

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.
CoRR, 2023

Benchmarking Distribution Shift in Tabular Data with TableShift.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

2022
The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling.
CoRR, 2022

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Scaling Polyphonic Transcription with Mixtures of Monophonic Transcriptions.
Proceedings of the 23rd International Society for Music Information Retrieval Conference, 2022

Multi-instrument Music Synthesis with Spectrogram Diffusion.
Proceedings of the 23rd International Society for Music Information Retrieval Conference, 2022

MT3: Multi-Task Multitrack Music Transcription.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Advances and Open Problems in Federated Learning.
Found. Trends Mach. Learn., 2021

Towards Culturally Relevant Personalization at Scale: Experiments with Data Science Learners.
Int. J. Artif. Intell. Educ., 2021

2020
Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data.
CoRR, 2020

Driving with Data in the Motor City: Understanding and Predicting Fleet Maintenance Patterns.
Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

2019
Advances and Open Problems in Federated Learning.
CoRR, 2019

Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments.
Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 2019

Evaluating the Fairness of Predictive Student Models Through Slicing Analysis.
Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 2019

Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective.
Proceedings of the 12th International Conference on Educational Data Mining, 2019

2018
Learn From Your (Markov) Neighbor: Coenrollment, Assortativity, and Grade Prediction in Undergraduate Courses.
J. Learn. Anal., November, 2018

Evaluating Predictive Models of Student Success: Closing the Methodological Gap.
J. Learn. Anal., August, 2018

Student success prediction in MOOCs.
User Model. User Adapt. Interact., 2018

Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining.
CoRR, 2018

MORF: A Framework for MOOC Predictive Modeling and Replication At Scale.
CoRR, 2018

Replicating MOOC predictive models at scale.
Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 2018

Coenrollment networks and their relationship to grades in undergraduate education.
Proceedings of the 8th International Conference on Learning Analytics and Knowledge, 2018

How Gender Cues in Educational Video Impact Participation and Retention.
Proceedings of the Rethinking learning in the digital age: Making the Learning Sciences count, 2018

MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

Dropout Model Evaluation in MOOCs.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit.
CoRR, 2017

A Statistical Framework for Predictive Model Evaluation in MOOCs.
Proceedings of the Fourth ACM Conference on Learning @ Scale, 2017

Integrating syllabus data into student success models.
Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017

Statistical Approaches to the Model Comparison Task in Learning Analytics.
Proceedings of the Joint Proceedings of the Workshop on Methodology in Learning Analytics (MLA) and the Workshop on Building the Learning Analytics Curriculum (BLAC) co-located with 7th International Learning Analytics and Knowledge Conference (LAK 2017), 2017

Toward Replicable Predictive Model Evaluation in MOOCs.
Proceedings of the 10th International Conference on Educational Data Mining, 2017


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