Principal Differences Analysis: Interpretable Characterization of Differences between Distributions. - Jonas Mueller 001; Tommi S. Jaakkola (NIPS, 2015)

Siamese Recurrent Architectures for Learning Sentence Similarity. - Jonas Mueller 001; Aditya Thyagarajan (AAAI, 2016)

Learning Optimal Interventions. - Jonas Mueller 001; David Reshef; George Du; Tommi S. Jaakkola (AISTATS, 2017)

Sequence to Better Sequence: Continuous Revision of Combinatorial Structures. - Jonas Mueller 001; David K. Gifford; Tommi S. Jaakkola (ICML, 2017)

Flexible models for understanding and optimizing complex populations. - Jonas Mueller 001 (2018)

What made you do this? Understanding black-box decisions with sufficient input subsets. - Brandon Carter 001; Jonas Mueller 001; Siddhartha Jain 001; David K. Gifford (AISTATS, 2019)

IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation. - Zhijing Jin 001; Di Jin; Jonas Mueller 001; Nicholas Matthews; Enrico Santus (EMNLP/IJCNLP (1), 2019)

Recognizing Variables from Their Data via Deep Embeddings of Distributions. - Jonas Mueller 001; Alex Smola (ICDM, 2019)

Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing. - Jonas Mueller 001; Vasilis Syrgkanis; Matt Taddy (NeurIPS, 2019)

Unsupervised Text Style Transfer via Iterative Matching and Translation. - Zhijing Jin 001; Di Jin; Jonas Mueller 001; Nicholas Matthews; Enrico Santus (CoRR, 2019)

Latent Space Secrets of Denoising Text-Autoencoders. - Tianxiao Shen; Jonas Mueller 001; Regina Barzilay; Tommi S. Jaakkola (CoRR, 2019)

Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles. - Siddhartha Jain 001; Ge Liu; Jonas Mueller 001; David Gifford 001 (AAAI, 2020)

Educating Text Autoencoders: Latent Representation Guidance via Denoising. - Tianxiao Shen; Jonas Mueller 001; Regina Barzilay; Tommi S. Jaakkola (ICML, 2020)

Faster, Simpler, More Accurate: Practical Automated Machine Learning with Tabular, Text, and Image Data. - Jonas Mueller 001; Xingjian Shi; Alexander J. Smola (KDD, 2020)

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. - Rasool Fakoor; Jonas Mueller 001; Nick Erickson; Pratik Chaudhari; Alexander J. Smola (NeurIPS, 2020)

Antibody complementarity determining region design using high-capacity machine learning. - Ge Liu; Haoyang Zeng; Jonas Mueller 001; Brandon Carter 001; Ziheng Wang; Jonas Schilz; Geraldine Horny; Michael E. Birnbaum; Stefan Ewert; David K. Gifford (Bioinform., 2020)

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. - Nick Erickson; Jonas Mueller 001; Alexander Shirkov; Hang Zhang 005; Pedro Larroy; Mu Li 003; Alexander J. Smola (CoRR, 2020)

TraDE: Transformers for Density Estimation. - Rasool Fakoor; Pratik Chaudhari; Jonas Mueller 001; Alexander J. Smola (CoRR, 2020)

Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. - Haoyu He; Xingjian Shi; Jonas Mueller 001; Sheng Zha; Mu Li 003; George Karypis (SustaiNLP@EMNLP, 2021)

Deep Learning for Functional Data Analysis with Adaptive Basis Layers. - Junwen Yao; Jonas Mueller 001; Jane-Ling Wang (ICML, 2021)

Overinterpretation reveals image classification model pathologies. - Brandon Carter 001; Siddhartha Jain 001; Jonas Mueller 001; David Gifford 001 (NeurIPS, 2021)

Continuous Doubly Constrained Batch Reinforcement Learning. - Rasool Fakoor; Jonas Mueller 001; Kavosh Asadi; Pratik Chaudhari; Alexander J. Smola (NeurIPS, 2021)

Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. - Curtis G. Northcutt; Anish Athalye; Jonas Mueller 001 (NeurIPS Datasets and Benchmarks, 2021)

Benchmarking Multimodal AutoML for Tabular Data with Text Fields. - Xingjian Shi; Jonas Mueller 001; Nick Erickson; Mu Li 003; Alexander J. Smola (NeurIPS Datasets and Benchmarks, 2021)

Deep Extended Hazard Models for Survival Analysis. - Qixian Zhong; Jonas Mueller 001; Jane-Ling Wang (NeurIPS, 2021)

Continuous Doubly Constrained Batch Reinforcement Learning. - Rasool Fakoor; Jonas Mueller 001; Pratik Chaudhari; Alexander J. Smola (CoRR, 2021)

Deep Quantile Aggregation. - Taesup Kim; Rasool Fakoor; Jonas Mueller 001; Alexander J. Smola; Ryan J. Tibshirani (CoRR, 2021)

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features. - Jiuhai Chen; Jonas Mueller 001; Vassilis N. Ioannidis; Soji Adeshina; Yangkun Wang; Tom Goldstein; David Wipf (CoRR, 2021)

ResNeSt: Split-Attention Networks. - Hang Zhang 005; Chongruo Wu; Zhongyue Zhang; Yi Zhu 001; Haibin Lin; Zhi Zhang 005; Yue Sun; Tong He 002; Jonas Mueller 001; R. Manmatha; Mu Li 003; Alexander J. Smola (CVPR Workshops, 2022)

Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features. - Jiuhai Chen; Jonas Mueller 001; Vassilis N. Ioannidis; Soji Adeshina; Yangkun Wang; Tom Goldstein; David Wipf (ICLR, 2022)

Adaptive Interest for Emphatic Reinforcement Learning. - Martin Klissarov; Rasool Fakoor; Jonas W. Mueller; Kavosh Asadi; Taesup Kim; Alexander J. Smola (NeurIPS, 2022)

Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline. - Massimo Caccia; Jonas Mueller 001; Taesup Kim; Laurent Charlin; Rasool Fakoor (CoRR, 2022)

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features. - Jiuhai Chen; Jonas Mueller 001; Vassilis N. Ioannidis; Tom Goldstein; David Wipf (CoRR, 2022)

Back to the Basics: Revisiting Out-of-Distribution Detection Baselines. - Johnson Kuan; Jonas Mueller 001 (CoRR, 2022)

DataPerf: Benchmarks for Data-Centric AI Development. - 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; et al. (CoRR, 2022)

Data drift correction via time-varying importance weight estimator. - Rasool Fakoor; Jonas Mueller 001; Zachary C. Lipton; Pratik Chaudhari; Alexander J. Smola (CoRR, 2022)

Detecting Label Errors in Token Classification Data. - Wei-Chen Wang 003; Jonas Mueller 001 (CoRR, 2022)

Utilizing supervised models to infer consensus labels and their quality from data with multiple annotators. - Hui Wen Goh; Ulyana Tkachenko; Jonas Mueller 001 (CoRR, 2022)

Identifying Incorrect Annotations in Multi-Label Classification Data. - Aditya Thyagarajan; Elías Snorrason; Curtis G. Northcutt; Jonas Mueller 001 (CoRR, 2022)

Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges. - Massimo Caccia; Jonas Mueller 001; Taesup Kim; Laurent Charlin; Rasool Fakoor (CoLLAs, 2023)

DataPerf: Benchmarks for Data-Centric AI Development. - 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; et al. (NeurIPS, 2023)

ActiveLab: Active Learning with Re-Labeling by Multiple Annotators. - Hui Wen Goh; Jonas Mueller 001 (CoRR, 2023)

Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors. - Jesse Cummings; Elías Snorrason; Jonas Mueller 001 (CoRR, 2023)

Detecting Errors in Numerical Data via any Regression Model. - Hang Zhou 008; Jonas Mueller 001; Mayank Kumar; Jane-Ling Wang; Jing Lei (CoRR, 2023)

Estimating label quality and errors in semantic segmentation data via any model. - Vedang Lad; Jonas Mueller 001 (CoRR, 2023)

Quantifying Uncertainty in Answers from any Language Model via Intrinsic and Extrinsic Confidence Assessment. - Jiuhai Chen; Jonas Mueller 001 (CoRR, 2023)

ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data. - Ulyana Tkachenko; Aditya Thyagarajan; Jonas Mueller 001 (CoRR, 2023)

Flexible Model Aggregation for Quantile Regression. - Rasool Fakoor; Taesup Kim; Jonas Mueller 001; Alexander J. Smola; Ryan J. Tibshirani (J. Mach. Learn. Res., 2023)

Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness. - Jiuhai Chen; Jonas Mueller 001 (ACL (1), 2024)

Time-Varying Propensity Score to Bridge the Gap between the Past and Present. - Rasool Fakoor; Jonas Mueller 001; Zachary Chase Lipton; Pratik Chaudhari; Alex Smola (ICLR, 2024)

SAND: Smooth imputation of sparse and noisy functional data with Transformer networks. - Ju-Sheng Hong; Junwen Yao; Jonas W. Mueller; Jane-Ling Wang (NeurIPS, 2024)

Automated Data Curation for Robust Language Model Fine-Tuning. - Jiuhai Chen; Jonas Mueller 001 (CoRR, 2024)

Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models. - Jiuhai Chen; Jonas Mueller 001; Vassilis N. Ioannidis; Tom Goldstein; David Wipf (Trans. Mach. Learn. Res., 2024)