Sercan Ö. Arik

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 65

Top Topics

Anomaly Detection

Structured And Unstructured Data

Top Venues

Trans. Mach. Learn. Res.

npj Digit. Medicine

Publications

Zachary Izzo, Jinsung Yoon, Sercan Ö. Arik, James Zou
Provable Membership Inference Privacy. Trans. Mach. Learn. Res. 2024 (2024)
Jinsung Yoon, Yanfei Chen, Sercan Ö. Arik, Tomas Pfister
Search-Adaptor: Embedding Customization for Information Retrieval. ACL (1) (2024)
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. Trans. Mach. Learn. Res. 2024 (2024)
Yusen Zhang, Ruoxi Sun, Yanfei Chen, Tomas Pfister, Rui Zhang, Sercan Ö. Arik
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks. CoRR (2024)
Pritam Sarkar, Sayna Ebrahimi, Ali Etemad, Ahmad Beirami, Sercan Ö. Arik, Tomas Pfister
Mitigating Object Hallucination via Data Augmented Contrastive Tuning. CoRR (2024)
Sungwon Han, Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning. CoRR (2024)
Maximillian Chen, Ruoxi Sun, Sercan Ö. Arik, Tomas Pfister
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training. CoRR (2024)
Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Haisu Liu, Quan Shi, Zachary S. Siegel, Michael Tang, Ruoxi Sun, Jinsung Yoon, Sercan Ö. Arik, Danqi Chen, Tao Yu
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval. CoRR (2024)
James Enouen, Hootan Nakhost, Sayna Ebrahimi, Sercan Ö. Arik, Yan Liu, Tomas Pfister
TextGenSHAP: Scalable Post-Hoc Explanations in Text Generation with Long Documents. ACL (Findings) (2024)
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. ICLR (2024)
Jinsung Yoon, Rajarishi Sinha, Sercan Ö. Arik, Tomas Pfister
Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions. CoRR (2024)
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan Ö. Arik
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization. CoRR (2024)
Yihe Dong, Sercan Ö. Arik, Nathanael C. Yoder, Tomas Pfister
Learned Feature Importance Scores for Automated Feature Engineering. CoRR (2024)
Xi Ye, Ruoxi Sun, Sercan Ö. Arik, Tomas Pfister
Effective Large Language Model Adaptation for Improved Grounding. CoRR (2023)
Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister
Better Zero-Shot Reasoning with Self-Adaptive Prompting. ACL (Findings) (2023)
Chun-Hao Chang, Jinsung Yoon, Sercan Ö. Arik, Madeleine Udell, Tomas Pfister
Data-Efficient and Interpretable Tabular Anomaly Detection. KDD (2023)
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)
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 (2023)
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arik, Tomas Pfister
Universal Self-adaptive Prompting. CoRR (2023)
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister, Somesh Jha
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. CoRR (2023)
Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister
Better Zero-Shot Reasoning with Self-Adaptive Prompting. CoRR (2023)
Chuizheng Meng, Yihe Dong, Sercan Ö. Arik, Yan Liu, Tomas Pfister
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning. CoRR (2023)
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 (2023)
Ruoxi Sun, Chun-Liang Li, Sercan Ö. Arik, Michael W. Dusenberry, Chen-Yu Lee, Tomas Pfister
Neural Spline Search for Quantile Probabilistic Modeling. CoRR (2023)
Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister
Invariant Structure Learning for Better Generalization and Causal Explainability. Trans. Mach. Learn. Res. 2023 (2023)
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. CoRR (2023)
Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister
Test-Time Adaptation for Visual Document Understanding. Trans. Mach. Learn. Res. 2023 (2023)
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)
Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister
Interpretable Mixture of Experts. Trans. Mach. Learn. Res. 2023 (2023)
Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan Ö. Arik, Tomas Pfister
TSMixer: An all-MLP Architecture for Time Series Forecasting. CoRR (2023)
Jinsung Yoon, Sercan Ö. Arik, Yanfei Chen, Tomas Pfister
Search-Adaptor: Text Embedding Customization for Information Retrieval. CoRR (2023)
Si-An Chen, Chun-Liang Li, Sercan Ö. Arik, Nathanael C. Yoder, Tomas Pfister
TSMixer: An All-MLP Architecture for Time Series Forecast-ing. Trans. Mach. Learn. Res. 2023 (2023)
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)
Sayna Ebrahimi, Sercan Ö. Arik, Yihe Dong, Tomas Pfister
LANISTR: Multimodal Learning from Structured and Unstructured Data. CoRR (2023)
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Eisenschlos, Sercan Ö. Arik, Tomas Pfister
Universal Self-Adaptive Prompting. EMNLP (2023)
Yihe Dong, Sercan Ö. Arik
SLM: End-to-end Feature Selection via Sparse Learnable Masks. CoRR (2023)
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)
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 (2023)
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 (2023)
Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu
Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts. ICLR (2023)
Nicasia Beebe-Wang, Sayna Ebrahimi, Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series. CoRR (2023)
Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister
Interpretable Mixture of Experts for Structured Data. CoRR (2022)
Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik, Deniz Yuret, Alper T. Erdogan
Self-Supervised Learning with an Information Maximization Criterion. NeurIPS (2022)
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)
Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister
Decoupling Local and Global Representations of Time Series. CoRR (2022)
Chun-Hao Chang, Jinsung Yoon, Sercan Ö. Arik, Madeleine Udell, Tomas Pfister
Data-Efficient and Interpretable Tabular Anomaly Detection. CoRR (2022)
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)
Sercan Ö. Arik, Nathanael C. Yoder, Tomas Pfister
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series. CoRR (2022)
Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister
Decoupling Local and Global Representations of Time Series. AISTATS (2022)
Sayna Ebrahimi, Sercan Ö. Arik, Tomas Pfister
Test-Time Adaptation for Visual Document Understanding. CoRR (2022)
Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik, Deniz Yuret, Alper T. Erdogan
Self-Supervised Learning with an Information Maximization Criterion. CoRR (2022)
Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister
Invariant Structure Learning for Better Generalization and Causal Explainability. CoRR (2022)
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)
Zachary Izzo, Jinsung Yoon, Sercan Ö. Arik, James Zou
Provable Membership Inference Privacy. CoRR (2022)
Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts. CoRR (2022)
Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling. Trans. Mach. Learn. Res. 2022 (2022)
Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. CoRR (2022)
Sungyong Seo, Sercan Ö. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister
Controlling Neural Networks with Rule Representations. NeurIPS (2021)
Sercan Ö. Arik, Tomas Pfister
TabNet: Attentive Interpretable Tabular Learning. AAAI (2021)
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 (2021)
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)
Sungyong Seo, Sercan Ö. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister
Controlling Neural Networks with Rule Representations. CoRR (2021)
Yu-Han Liu, Sercan Ö. Arik
Explaining Deep Neural Networks using Unsupervised Clustering. CoRR (2020)
Alaelson C. Jatoba-Neto, Darli A. A. Mello, Christian Esteve Rothenberg, Sercan Ö. Arik, Joseph M. Kahn
Scaling SDM Optical Networks Using Full-Spectrum Spatial Switching. JOCN 10 (12) (2018)
Karthik Choutagunta, Sercan Ö. Arik, Mehrad Moradshahi, Joseph M. Kahn
Optical MIMO signal processing for direct-detection mode-division multiplexing. ICTON (2017)
Sercan Ö. Arik, Sukru Burc Eryilmaz, Adam Goldberg
Supervised classification-based stock prediction and portfolio optimization. CoRR (2014)
David S. Millar, Toshiaki Koike-Akino, Sercan Ö. Arik, Keisuke Kojima, Kieran Parsons
Comparison of quaternary block-coding and sphere-cutting for high-dimensional modulation. OFC (2014)
Sercan Ö. Arik, David S. Millar, Toshiaki Koike-Akino, Keisuke Kojima, Kieran Parsons
High-dimensional modulation for mode-division multiplexing. OFC (2014)