Jonas Mueller

Affiliations:
  • Amazon Web Services
  • Massachusetts Institute of Technology, Cambridge, MA, USA (PhD 2018)


According to our database1, Jonas Mueller authored at least 52 papers between 2015 and 2024.

Collaborative distances:

Timeline

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Bibliography

2024
Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models.
Trans. Mach. Learn. Res., 2024

Automated Data Curation for Robust Language Model Fine-Tuning.
CoRR, 2024

Time-Varying Propensity Score to Bridge the Gap between the Past and Present.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023
Flexible Model Aggregation for Quantile Regression.
J. Mach. Learn. Res., 2023

ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data.
CoRR, 2023

Quantifying Uncertainty in Answers from any Language Model via Intrinsic and Extrinsic Confidence Assessment.
CoRR, 2023

Estimating label quality and errors in semantic segmentation data via any model.
CoRR, 2023

Detecting Errors in Numerical Data via any Regression Model.
CoRR, 2023

Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors.
CoRR, 2023

ActiveLab: Active Learning with Re-Labeling by Multiple Annotators.
CoRR, 2023


Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges.
Proceedings of the Conference on Lifelong Learning Agents, 2023

2022
Identifying Incorrect Annotations in Multi-Label Classification Data.
CoRR, 2022

Utilizing supervised models to infer consensus labels and their quality from data with multiple annotators.
CoRR, 2022

Detecting Label Errors in Token Classification Data.
CoRR, 2022

Data drift correction via time-varying importance weight estimator.
CoRR, 2022

DataPerf: Benchmarks for Data-Centric AI Development.
CoRR, 2022

Back to the Basics: Revisiting Out-of-Distribution Detection Baselines.
CoRR, 2022

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features.
CoRR, 2022

Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline.
CoRR, 2022

Adaptive Interest for Emphatic Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features.
Proceedings of the Tenth International Conference on Learning Representations, 2022

ResNeSt: Split-Attention Networks.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features.
CoRR, 2021

Deep Quantile Aggregation.
CoRR, 2021

Continuous Doubly Constrained Batch Reinforcement Learning.
CoRR, 2021

Deep Extended Hazard Models for Survival Analysis.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Benchmarking Multimodal AutoML for Tabular Data with Text Fields.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Continuous Doubly Constrained Batch Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Overinterpretation reveals image classification model pathologies.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Deep Learning for Functional Data Analysis with Adaptive Basis Layers.
Proceedings of the 38th International Conference on Machine Learning, 2021

Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing.
Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing, 2021

2020
TraDE: Transformers for Density Estimation.
CoRR, 2020

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.
CoRR, 2020

Antibody complementarity determining region design using high-capacity machine learning.
Bioinform., 2020

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Faster, Simpler, More Accurate: Practical Automated Machine Learning with Tabular, Text, and Image Data.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Educating Text Autoencoders: Latent Representation Guidance via Denoising.
Proceedings of the 37th International Conference on Machine Learning, 2020

Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Latent Space Secrets of Denoising Text-Autoencoders.
CoRR, 2019

Unsupervised Text Style Transfer via Iterative Matching and Translation.
CoRR, 2019

Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Recognizing Variables from Their Data via Deep Embeddings of Distributions.
Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

What made you do this? Understanding black-box decisions with sufficient input subsets.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Flexible models for understanding and optimizing complex populations.
PhD thesis, 2018

2017
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures.
Proceedings of the 34th International Conference on Machine Learning, 2017

Learning Optimal Interventions.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Siamese Recurrent Architectures for Learning Sentence Similarity.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015


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