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ORCIDJonas Mueller 0001
Person information
- affiliation: Amazon Web Services
- affiliation (PhD 2018): Massachusetts Institute of Technology, Cambridge, MA, USA
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2020 – today
- 2024
[j3]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models. Trans. Mach. Learn. Res. 2024 (2024)- [c26]Jiuhai Chen, Jonas Mueller:
Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness. ACL (1) 2024: 5186-5200 - [c25]Rasool Fakoor, Jonas Mueller, Zachary Chase Lipton, Pratik Chaudhari, Alex Smola:
Time-Varying Propensity Score to Bridge the Gap between the Past and Present. ICLR 2024 - [i35]Jiuhai Chen, Jonas Mueller:
Automated Data Curation for Robust Language Model Fine-Tuning. CoRR abs/2403.12776 (2024) - 2023
- [j2]Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani:
Flexible Model Aggregation for Quantile Regression. J. Mach. Learn. Res. 24: 162:1-162:45 (2023) - [c24]Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, Rasool Fakoor:
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges. CoLLAs 2023: 89-119 - [c23]Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Will Cukierski, Juan Ciro, Lora Aroyo, Bilge Acun, Lingjiao Chen, Mehul Raje, Max Bartolo, Evan Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Addison Howard, Oana Inel, Tariq Kane, Christine R. Kirkpatrick, D. Sculley, Tzu-Sheng Kuo, Jonas W. Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Y. Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi:
DataPerf: Benchmarks for Data-Centric AI Development. NeurIPS 2023 - [i34]Hui Wen Goh, Jonas Mueller:
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators. CoRR abs/2301.11856 (2023) - [i33]Jesse Cummings, Elías Snorrason, Jonas Mueller:
Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors. CoRR abs/2305.15696 (2023) - [i32]Hang Zhou, Jonas Mueller, Mayank Kumar, Jane-Ling Wang, Jing Lei:
Detecting Errors in Numerical Data via any Regression Model. CoRR abs/2305.16583 (2023) - [i31]Vedang Lad, Jonas Mueller:
Estimating label quality and errors in semantic segmentation data via any model. CoRR abs/2307.05080 (2023) - [i30]Jiuhai Chen, Jonas Mueller:
Quantifying Uncertainty in Answers from any Language Model via Intrinsic and Extrinsic Confidence Assessment. CoRR abs/2308.16175 (2023) - [i29]Ulyana Tkachenko, Aditya Thyagarajan, Jonas Mueller:
ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data. CoRR abs/2309.00832 (2023) - 2022
- [c22]Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander J. Smola:
ResNeSt: Split-Attention Networks. CVPR Workshops 2022: 2735-2745 - [c21]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features. ICLR 2022 - [c20]Martin Klissarov, Rasool Fakoor, Jonas W. Mueller, Kavosh Asadi, Taesup Kim, Alexander J. Smola:
Adaptive Interest for Emphatic Reinforcement Learning. NeurIPS 2022 - [i28]Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, Rasool Fakoor:
Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline. CoRR abs/2205.14495 (2022) - [i27]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features. CoRR abs/2206.08473 (2022) - [i26]Johnson Kuan, Jonas Mueller:
Back to the Basics: Revisiting Out-of-Distribution Detection Baselines. CoRR abs/2207.03061 (2022) - [i25]Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Juan Ciro, Lora Aroyo, Bilge Acun, Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Tariq Kane, Christine R. Kirkpatrick, Tzu-Sheng Kuo, Jonas Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi:
DataPerf: Benchmarks for Data-Centric AI Development. CoRR abs/2207.10062 (2022) - [i24]Rasool Fakoor, Jonas Mueller, Zachary C. Lipton, Pratik Chaudhari, Alexander J. Smola:
Data drift correction via time-varying importance weight estimator. CoRR abs/2210.01422 (2022) - [i23]Wei-Chen Wang, Jonas Mueller:
Detecting Label Errors in Token Classification Data. CoRR abs/2210.03920 (2022) - [i22]Hui Wen Goh, Ulyana Tkachenko, Jonas Mueller:
Utilizing supervised models to infer consensus labels and their quality from data with multiple annotators. CoRR abs/2210.06812 (2022) - [i21]Aditya Thyagarajan, Elías Snorrason, Curtis G. Northcutt, Jonas Mueller:
Identifying Incorrect Annotations in Multi-Label Classification Data. CoRR abs/2211.13895 (2022) - 2021
- [c19]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. SustaiNLP@EMNLP 2021: 119-133 - [c18]Junwen Yao, Jonas Mueller, Jane-Ling Wang:
Deep Learning for Functional Data Analysis with Adaptive Basis Layers. ICML 2021: 11898-11908 - [c17]Rasool Fakoor, Jonas Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J. Smola:
Continuous Doubly Constrained Batch Reinforcement Learning. NeurIPS 2021: 11260-11273 - [c16]Brandon Carter, Siddhartha Jain, Jonas Mueller, David Gifford:
Overinterpretation reveals image classification model pathologies. NeurIPS 2021: 15395-15407 - [c15]Curtis G. Northcutt, Anish Athalye, Jonas Mueller:
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. NeurIPS Datasets and Benchmarks 2021 - [c14]Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola:
Benchmarking Multimodal AutoML for Tabular Data with Text Fields. NeurIPS Datasets and Benchmarks 2021 - [c13]Qixian Zhong, Jonas Mueller, Jane-Ling Wang:
Deep Extended Hazard Models for Survival Analysis. NeurIPS 2021: 15111-15124 - [i20]Rasool Fakoor, Jonas Mueller, Pratik Chaudhari, Alexander J. Smola:
Continuous Doubly Constrained Batch Reinforcement Learning. CoRR abs/2102.09225 (2021) - [i19]Taesup Kim, Rasool Fakoor, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani:
Deep Quantile Aggregation. CoRR abs/2103.00083 (2021) - [i18]Curtis G. Northcutt, Anish Athalye, Jonas Mueller:
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. CoRR abs/2103.14749 (2021) - [i17]Junwen Yao, Jonas Mueller, Jane-Ling Wang:
Deep Learning for Functional Data Analysis with Adaptive Basis Layers. CoRR abs/2106.10414 (2021) - [i16]Haoyu He, Xingjian Shi, Jonas Mueller, Sheng Zha, Mu Li, George Karypis:
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. CoRR abs/2109.11105 (2021) - [i15]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features. CoRR abs/2110.13413 (2021) - [i14]Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola:
Benchmarking Multimodal AutoML for Tabular Data with Text Fields. CoRR abs/2111.02705 (2021) - 2020
- [j1]Ge Liu, Haoyang Zeng, Jonas Mueller, Brandon Carter
, Ziheng Wang, Jonas Schilz, Geraldine Horny, Michael E. Birnbaum, Stefan Ewert, David K. Gifford:
Antibody complementarity determining region design using high-capacity machine learning. Bioinform. 36(7): 2126-2133 (2020) - [c12]Siddhartha Jain, Ge Liu, Jonas Mueller, David Gifford:
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles. AAAI 2020: 4264-4271 - [c11]Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola:
Educating Text Autoencoders: Latent Representation Guidance via Denoising. ICML 2020: 8719-8729 - [c10]Jonas Mueller, Xingjian Shi, Alexander J. Smola:
Faster, Simpler, More Accurate: Practical Automated Machine Learning with Tabular, Text, and Image Data. KDD 2020: 3509-3510 - [c9]Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola:
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. NeurIPS 2020 - [i13]Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander J. Smola:
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. CoRR abs/2003.06505 (2020) - [i12]Brandon Carter, Siddhartha Jain, Jonas Mueller, David K. Gifford:
Overinterpretation reveals image classification model pathologies. CoRR abs/2003.08907 (2020) - [i11]Rasool Fakoor, Pratik Chaudhari, Jonas Mueller, Alexander J. Smola:
TraDE: Transformers for Density Estimation. CoRR abs/2004.02441 (2020) - [i10]Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander J. Smola:
ResNeSt: Split-Attention Networks. CoRR abs/2004.08955 (2020) - [i9]Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola:
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. CoRR abs/2006.14284 (2020)
2010 – 2019
- 2019
- [c8]Brandon Carter, Jonas Mueller, Siddhartha Jain, David K. Gifford:
What made you do this? Understanding black-box decisions with sufficient input subsets. AISTATS 2019: 567-576 - [c7]Zhijing Jin, Di Jin, Jonas Mueller, Nicholas Matthews, Enrico Santus:
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation. EMNLP/IJCNLP (1) 2019: 3095-3107 - [c6]Jonas Mueller, Alex Smola:
Recognizing Variables from Their Data via Deep Embeddings of Distributions. ICDM 2019: 1264-1269 - [c5]Jonas Mueller, Vasilis Syrgkanis, Matt Taddy:
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing. NeurIPS 2019: 15442-15452 - [i8]Zhijing Jin, Di Jin, Jonas Mueller, Nicholas Matthews, Enrico Santus:
Unsupervised Text Style Transfer via Iterative Matching and Translation. CoRR abs/1901.11333 (2019) - [i7]Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola:
Latent Space Secrets of Denoising Text-Autoencoders. CoRR abs/1905.12777 (2019) - [i6]Siddhartha Jain, Ge Liu, Jonas Mueller, David K. Gifford:
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles. CoRR abs/1906.07380 (2019) - [i5]Jonas Mueller, Alex Smola:
Recognizing Variables from their Data via Deep Embeddings of Distributions. CoRR abs/1909.04844 (2019) - 2018
- [b1]Jonas Mueller:
Flexible models for understanding and optimizing complex populations. Massachusetts Institute of Technology, Cambridge, USA, 2018 - [i4]Jonas Mueller, Vasilis Syrgkanis, Matt Taddy:
Low-rank Bandit Methods for High-dimensional Dynamic Pricing. CoRR abs/1801.10242 (2018) - [i3]Brandon Carter, Jonas Mueller, Siddhartha Jain, David K. Gifford:
What made you do this? Understanding black-box decisions with sufficient input subsets. CoRR abs/1810.03805 (2018) - 2017
- [c4]Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola:
Learning Optimal Interventions. AISTATS 2017: 1039-1047 - [c3]Jonas Mueller, David K. Gifford, Tommi S. Jaakkola:
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures. ICML 2017: 2536-2544 - 2016
- [c2]Jonas Mueller, Aditya Thyagarajan:
Siamese Recurrent Architectures for Learning Sentence Similarity. AAAI 2016: 2786-2792 - [i2]Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola:
Learning Optimal Interventions. CoRR abs/1606.05027 (2016) - 2015
- [c1]Jonas Mueller, Tommi S. Jaakkola:
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions. NIPS 2015: 1702-1710 - [i1]Jonas Mueller, Tommi S. Jaakkola:
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions. CoRR abs/1510.08956 (2015)
Coauthor Index
aka: Alex Smola
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