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Tomas Pfister
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Publications
- 2023
- [j10]Jinsung Yoon, Michel J. Mizrahi, Nahid Farhady Ghalaty, Thomas Jarvinen, Ashwin S. Ravi, Peter Brune, Fanyu Kong, Dave Anderson, George Lee, Arie Meir, Farhana Bandukwala, Elli Kanal, Sercan Ö. Arik, Tomas Pfister:
EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records. npj Digit. Medicine 6 (2023) - [j9]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. Trans. Mach. Learn. Res. 2023 (2023) - [c51]Ruoxi Sun, Chun-Liang Li, Sercan Ö. Arik, Michael W. Dusenberry, Chen-Yu Lee, Tomas Pfister:
Neural Spline Search for Quantile Probabilistic Modeling. AAAI 2023: 9927-9934 - [c50]Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister:
Better Zero-Shot Reasoning with Self-Adaptive Prompting. ACL (Findings) 2023: 3493-3514 - [c44]Ruoxi Sun, Sercan Ö. Arik, Rajarishi Sinha, Hootan Nakhost, Hanjun Dai, Pengcheng Yin, Tomas Pfister:
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data. EMNLP (Findings) 2023: 542-550 - [c43]Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister, Somesh Jha:
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. EMNLP (Findings) 2023: 5190-5213 - [c42]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Eisenschlos, Sercan Ö. Arik, Tomas Pfister:
Universal Self-Adaptive Prompting. EMNLP 2023: 7437-7462 - [c41]Chun-Hao Chang, Jinsung Yoon, Sercan Ö. Arik, Madeleine Udell, Tomas Pfister:
Data-Efficient and Interpretable Tabular Anomaly Detection. KDD 2023: 190-201 - [i64]Ruoxi Sun, Chun-Liang Li, Sercan Ö. Arik, Michael W. Dusenberry, Chen-Yu Lee, Tomas Pfister:
Neural Spline Search for Quantile Probabilistic Modeling. CoRR abs/2301.04857 (2023) - [i62]Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan Ö. Arik, Tomas Pfister:
TSMixer: An all-MLP Architecture for Time Series Forecasting. CoRR abs/2303.06053 (2023) - [i61]Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister:
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. CoRR abs/2304.03870 (2023) - [i58]Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister:
Better Zero-Shot Reasoning with Self-Adaptive Prompting. CoRR abs/2305.14106 (2023) - [i57]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arik, Tomas Pfister:
Universal Self-adaptive Prompting. CoRR abs/2305.14926 (2023) - [i56]Sayna Ebrahimi, Sercan Ö. Arik, Yihe Dong, Tomas Pfister:
LANISTR: Multimodal Learning from Structured and Unstructured Data. CoRR abs/2305.16556 (2023) - [i55]Ruoxi Sun, Sercan Ö. Arik, Hootan Nakhost, Hanjun Dai, Rajarishi Sinha, Pengcheng Yin, Tomas Pfister:
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL. CoRR abs/2306.00739 (2023) - [i53]Nicasia Beebe-Wang, Sayna Ebrahimi, Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister:
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series. CoRR abs/2308.13703 (2023) - [i52]Defu Cao, Furong Jia, Sercan Ö. Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu:
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting. CoRR abs/2310.04948 (2023) - [i51]Jinsung Yoon, Sercan Ö. Arik, Yanfei Chen, Tomas Pfister:
Search-Adaptor: Text Embedding Customization for Information Retrieval. CoRR abs/2310.08750 (2023) - [i50]Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister, Somesh Jha:
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. CoRR abs/2310.11689 (2023) - [i49]Chuizheng Meng, Yihe Dong, Sercan Ö. Arik, Yan Liu, Tomas Pfister:
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning. CoRR abs/2311.00886 (2023) - [i48]Ruoxi Sun, Sercan Ö. Arik, Rajarishi Sinha, Hootan Nakhost, Hanjun Dai, Pengcheng Yin, Tomas Pfister:
SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data. CoRR abs/2311.02883 (2023) - [i47]Xi Ye, Ruoxi Sun, Sercan Ö. Arik, Tomas Pfister:
Effective Large Language Model Adaptation for Improved Grounding. CoRR abs/2311.09533 (2023) - [i46]James Enouen, Hootan Nakhost, Sayna Ebrahimi, Sercan Ö. Arik, Yan Liu, Tomas Pfister:
TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents. CoRR abs/2312.01279 (2023) - 2022
- [j8]Thomas C. Tsai, Sercan Ö. Arik, Benjamin H. Jacobson, Jinsung Yoon, Nate Yoder, Dario Sava, Margaret Mitchell, Garth Graham, Tomas Pfister:
Algorithmic fairness in pandemic forecasting: lessons from COVID-19. npj Digit. Medicine 5 (2022) - [j7]Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister:
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling. Trans. Mach. Learn. Res. 2022 (2022) - [j6]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Chen-Yu Lee, Tomas Pfister:
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. Trans. Mach. Learn. Res. 2022 (2022) - [c37]Zizhao Zhang, Han Zhang, Long Zhao, Ting Chen, Sercan Ö. Arik, Tomas Pfister:
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding. AAAI 2022: 3417-3425 - [c35]Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister:
Decoupling Local and Global Representations of Time Series. AISTATS 2022: 8700-8714 - [i44]Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister:
Decoupling Local and Global Representations of Time Series. CoRR abs/2202.02262 (2022) - [i43]Sercan Ö. Arik, Nathanael C. Yoder, Tomas Pfister:
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series. CoRR abs/2202.02403 (2022) - [i42]Chun-Hao Chang, Jinsung Yoon, Sercan Ö. Arik, Madeleine Udell, Tomas Pfister:
Data-Efficient and Interpretable Tabular Anomaly Detection. CoRR abs/2203.02034 (2022) - [i37]Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister:
Interpretable Mixture of Experts for Structured Data. CoRR abs/2206.02107 (2022) - [i36]Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister:
Invariant Structure Learning for Better Generalization and Causal Explainability. CoRR abs/2206.06469 (2022) - [i35]Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister:
Test-Time Adaptation for Visual Document Understanding. CoRR abs/2206.07240 (2022) - [i33]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. CoRR abs/2212.00173 (2022) - 2021
- [j5]Sercan Ö. Arik, Joel Shor, Rajarishi Sinha, Jinsung Yoon, Joseph R. Ledsam, Long T. Le, Michael W. Dusenberry, Nathanael C. Yoder, Kris Popendorf, Arkady Epshteyn, Johan Euphrosine, Elli Kanal, Isaac Jones, Chun-Liang Li, Beth Luan, Joe Mckenna, Vikas Menon, Shashank Singh, Mimi Sun, Ashwin Sura Ravi, Leyou Zhang, Dario Sava, Kane Cunningham, Hiroki Kayama, Thomas C. Tsai, Daisuke Yoneoka, Shuhei Nomura, Hiroaki Miyata, Tomas Pfister:
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. npj Digit. Medicine 4 (2021) - [c31]Sercan Ö. Arik, Tomas Pfister:
TabNet: Attentive Interpretable Tabular Learning. AAAI 2021: 6679-6687 - [c25]Sungyong Seo, Sercan Ö. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister:
Controlling Neural Networks with Rule Representations. NeurIPS 2021: 11196-11207 - [i29]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Chen-Yu Lee, Tomas Pfister:
Self-Trained One-class Classification for Unsupervised Anomaly Detection. CoRR abs/2106.06115 (2021) - [i28]Sungyong Seo, Sercan Ö. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister:
Controlling Neural Networks with Rule Representations. CoRR abs/2106.07804 (2021) - 2020
- [j4]Sercan Ömer Arik, Tomas Pfister:
ProtoAttend: Attention-Based Prototypical Learning. J. Mach. Learn. Res. 21: 210:1-210:35 (2020) - [c24]Zizhao Zhang, Han Zhang, Sercan Ömer Arik, Honglak Lee, Tomas Pfister:
Distilling Effective Supervision From Severe Label Noise. CVPR 2020: 9291-9300 - [c23]Linchao Zhu, Sercan Ömer Arik, Yi Yang, Tomas Pfister:
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning. ECCV (27) 2020: 342-358 - [c22]Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ömer Arik, Larry S. Davis, Tomas Pfister:
Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost. ECCV (10) 2020: 510-526 - [c21]Chen Xing, Sercan Ömer Arik, Zizhao Zhang, Tomas Pfister:
Distance-Based Learning from Errors for Confidence Calibration. ICLR 2020 - [c20]Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
Data Valuation using Reinforcement Learning. ICML 2020: 10842-10851 - [c19]Sercan Ömer Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister:
Interpretable Sequence Learning for Covid-19 Forecasting. NeurIPS 2020 - [c17]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar:
On Completeness-aware Concept-Based Explanations in Deep Neural Networks. NeurIPS 2020 - [i21]Sercan Ömer Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Nate Yoder, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister:
Interpretable Sequence Learning for COVID-19 Forecasting. CoRR abs/2008.00646 (2020) - 2019
- [i18]Sercan Ömer Arik, Tomas Pfister:
Attention-Based Prototypical Learning Towards Interpretable, Confident and Robust Deep Neural Networks. CoRR abs/1902.06292 (2019) - [i15]Sercan Ömer Arik, Tomas Pfister:
TabNet: Attentive Interpretable Tabular Learning. CoRR abs/1908.07442 (2019) - [i14]Linchao Zhu, Sercan Ömer Arik, Yi Yang, Tomas Pfister:
Learning to Transfer Learn. CoRR abs/1908.11406 (2019) - [i12]Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
Data Valuation using Reinforcement Learning. CoRR abs/1909.11671 (2019) - [i11]Jinsung Yoon, Sercan Ömer Arik, Tomas Pfister:
RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling. CoRR abs/1909.12367 (2019) - [i10]Zizhao Zhang, Han Zhang, Sercan Ömer Arik, Honglak Lee, Tomas Pfister:
IEG: Robust Neural Network Training to Tackle Severe Label Noise. CoRR abs/1910.00701 (2019) - [i9]Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ömer Arik, Larry S. Davis, Tomas Pfister:
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Cost. CoRR abs/1910.07153 (2019) - [i7]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister:
On Concept-Based Explanations in Deep Neural Networks. CoRR abs/1910.07969 (2019) - [i6]Chen Xing, Sercan Ömer Arik, Zizhao Zhang, Tomas Pfister:
Distance-Based Learning from Errors for Confidence Calibration. CoRR abs/1912.01730 (2019) - [i5]Bryan Lim, Sercan Ömer Arik, Nicolas Loeff, Tomas Pfister:
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. CoRR abs/1912.09363 (2019)
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