Alignment of uncalibrated images for multi-view classification. - Sercan Ömer Arik; Elif Vural; Pascal Frossard (ICIP, 2011)

High-dimensional modulation for mode-division multiplexing. - Sercan Ö. Arik; David S. Millar; Toshiaki Koike-Akino; Keisuke Kojima; Kieran Parsons (OFC, 2014)

Comparison of quaternary block-coding and sphere-cutting for high-dimensional modulation. - David S. Millar; Toshiaki Koike-Akino; Sercan Ö. Arik; Keisuke Kojima; Kieran Parsons (OFC, 2014)

Supervised classification-based stock prediction and portfolio optimization. - Sercan Ö. Arik; Sukru Burc Eryilmaz; Adam Goldberg (CoRR, 2014)

MIMO Signal Processing for Mode-Division Multiplexing: An overview of channel models and signal processing architectures. - Sercan Ömer Arik; Joseph M. Kahn; Keang-Po Ho (IEEE Signal Process. Mag., 2014)

Group delay statistics and management in mode-division multiplexing. - Sercan Ömer Arik; Keang-Po Ho; Joseph M. Kahn (ACSSC, 2015)

MIMO DSP complexity in mode-division multiplexing. - Sercan Ömer Arik; Daulet Askarov; Joseph M. Kahn (OFC, 2015)

MIMO channel statistics and signal processing in mode-division multiplexing systems. - Joseph M. Kahn; Sercan Ömer Arik; Keang-Po Ho (SPAWC, 2015)

Scaling optical networks using full-spectrum spatial switching. - Alaelson C. Jatoba-Neto; Christian Esteve Rothenberg; Darli A. A. Mello; Sercan Ömer Arik; Joseph M. Kahn (HPSR, 2017)

Deep Voice: Real-time Neural Text-to-Speech. - Sercan Ömer Arik; Mike Chrzanowski; Adam Coates 002; Gregory Frederick Diamos; Andrew Gibiansky; Yongguo Kang; Xian Li; John Miller 001; Andrew Y. Ng; Jonathan Raiman; Shubho Sengupta; Mohammad Shoeybi (ICML, 2017)

Optical MIMO signal processing for direct-detection mode-division multiplexing. - Karthik Choutagunta; Sercan Ö. Arik; Mehrad Moradshahi; Joseph M. Kahn (ICTON, 2017)

Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting. - Sercan Ömer Arik; Markus Kliegl; Rewon Child; Joel Hestness; Andrew Gibiansky; Christopher Fougner; Ryan Prenger; Adam Coates 002 (INTERSPEECH, 2017)

Deep Voice 2: Multi-Speaker Neural Text-to-Speech. - Andrew Gibiansky; Sercan Ömer Arik; Gregory Frederick Diamos; John Miller 001; Kainan Peng; Wei Ping; Jonathan Raiman; Yanqi Zhou (NIPS, 2017)

Capacity limits of space-division multiplexed submarine links subject to nonlinearities and power feed constraints. - Omar Darwiche Domingues; Darli A. A. Mello; Reginaldo Silva; Sercan Ömer Arik; Joseph M. Kahn (OFC, 2017)

Deep Voice: Real-time Neural Text-to-Speech. - Sercan Ömer Arik; Mike Chrzanowski; Adam Coates 002; Greg Diamos; Andrew Gibiansky; Yongguo Kang; Xian Li; John Miller 001; Jonathan Raiman; Shubho Sengupta; Mohammad Shoeybi (CoRR, 2017)

Low-complexity implementation of convex optimization-based phase retrieval. - Sercan Ömer Arik; Joseph M. Kahn (CoRR, 2017)

Deep Voice 3: 2000-Speaker Neural Text-to-Speech. - Wei Ping; Kainan Peng; Andrew Gibiansky; Sercan Ömer Arik; Ajay Kannan; Sharan Narang; Jonathan Raiman; John Miller 001 (CoRR, 2017)

Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning. - Wei Ping; Kainan Peng; Andrew Gibiansky; Sercan Ömer Arik; Ajay Kannan; Sharan Narang; Jonathan Raiman; John Miller 001 (ICLR (Poster), 2018)

Neural Voice Cloning with a Few Samples. - Sercan Ömer Arik; Jitong Chen; Kainan Peng; Wei Ping; Yanqi Zhou (NeurIPS, 2018)

Resource-Efficient Neural Architect. - Yanqi Zhou; Siavash Ebrahimi; Sercan Ömer Arik; Haonan Yu; Hairong Liu; Greg Diamos (CoRR, 2018)

Scaling SDM Optical Networks Using Full-Spectrum Spatial Switching. - Alaelson C. Jatoba-Neto; Darli A. A. Mello; Christian Esteve Rothenberg; Sercan Ö. Arik; Joseph M. Kahn (JOCN, 2018)

Attention-Based Prototypical Learning Towards Interpretable, Confident and Robust Deep Neural Networks. - Sercan Ömer Arik; Tomas Pfister (CoRR, 2019)

EPNAS: Efficient Progressive Neural Architecture Search. - Yanqi Zhou; Peng Wang; Sercan Ömer Arik; Haonan Yu; Syed Zawad; Feng Yan 001; Greg Diamos (CoRR, 2019)

Learning to Transfer Learn. - Linchao Zhu; Sercan Ömer Arik; Yi Yang 001; Tomas Pfister (CoRR, 2019)

RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling. - Jinsung Yoon; Sercan Ömer Arik; Tomas Pfister (CoRR, 2019)

IEG: Robust Neural Network Training to Tackle Severe Label Noise. - Zizhao Zhang; Han Zhang 010; Sercan Ömer Arik; Honglak Lee; Tomas Pfister (CoRR, 2019)

On Concept-Based Explanations in Deep Neural Networks. - Chih-Kuan Yeh; Been Kim; Sercan Ömer Arik; Chun-Liang Li; Pradeep Ravikumar; Tomas Pfister (CoRR, 2019)

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. - Bryan Lim; Sercan Ömer Arik; Nicolas Loeff; Tomas Pfister (CoRR, 2019)

Fast Spectrogram Inversion Using Multi-Head Convolutional Neural Networks. - Sercan Ömer Arik; Heewoo Jun; Gregory Frederick Diamos (IEEE Signal Process. Lett., 2019)

Distilling Effective Supervision From Severe Label Noise. - Zizhao Zhang; Han Zhang 010; Sercan Ömer Arik; Honglak Lee; Tomas Pfister (CVPR, 2020)

Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost. - Mingfei Gao; Zizhao Zhang; Guo Yu; Sercan Ömer Arik; Larry S. Davis; Tomas Pfister (ECCV (10), 2020)

Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning. - Linchao Zhu; Sercan Ömer Arik; Yi Yang 001; Tomas Pfister (ECCV (27), 2020)

Distance-Based Learning from Errors for Confidence Calibration. - Chen Xing; Sercan Ömer Arik; Zizhao Zhang; Tomas Pfister (ICLR, 2020)

Data Valuation using Reinforcement Learning. - Jinsung Yoon; Sercan Ömer Arik; Tomas Pfister (ICML, 2020)

Interpretable Sequence Learning for Covid-19 Forecasting. - Sercan Ömer Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh 005; Leyou Zhang; Martin Nikoltchev; Yash Sonthalia; Hootan Nakhost; et al. (NeurIPS, 2020)

On Completeness-aware Concept-Based Explanations in Deep Neural Networks. - Chih-Kuan Yeh; Been Kim; Sercan Ömer Arik; Chun-Liang Li; Tomas Pfister; Pradeep Ravikumar (NeurIPS, 2020)

Explaining Deep Neural Networks using Unsupervised Clustering. - Yu-Han Liu; Sercan Ö. Arik (CoRR, 2020)

Interpretable Sequence Learning for COVID-19 Forecasting. - Sercan Ömer Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh 005; Leyou Zhang; Nate Yoder; Martin Nikoltchev; Yash Sonthalia; et al. (CoRR, 2020)

ProtoAttend: Attention-Based Prototypical Learning. - Sercan Ömer Arik; Tomas Pfister (J. Mach. Learn. Res., 2020)

TabNet: Attentive Interpretable Tabular Learning. - Sercan Ö. Arik; Tomas Pfister (AAAI, 2021)

Controlling Neural Networks with Rule Representations. - Sungyong Seo; Sercan Ö. Arik; Jinsung Yoon; Xiang Zhang; Kihyuk Sohn; Tomas Pfister (NeurIPS, 2021)

Self-Trained One-class Classification for Unsupervised Anomaly Detection. - Jinsung Yoon; Kihyuk Sohn; Chun-Liang Li; Sercan Ö. Arik; Chen-Yu Lee; Tomas Pfister (CoRR, 2021)

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. - 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; et al. (npj Digit. Medicine, 2021)

Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding. - Zizhao Zhang; Han Zhang 010; Long Zhao 003; Ting Chen 001; Sercan Ö. Arik; Tomas Pfister (AAAI, 2022)

Decoupling Local and Global Representations of Time Series. - Sana Tonekaboni; Chun-Liang Li; Sercan Ö. Arik; Anna Goldenberg; Tomas Pfister (AISTATS, 2022)

Self-Supervised Learning with an Information Maximization Criterion. - Serdar Ozsoy; Shadi Hamdan; Sercan Ö. Arik; Deniz Yuret; Alper T. Erdogan (NeurIPS, 2022)

Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series. - Sercan Ö. Arik; Nathanael C. Yoder; Tomas Pfister (CoRR, 2022)

Interpretable Mixture of Experts for Structured Data. - Aya Abdelsalam Ismail; Sercan Ö. Arik; Jinsung Yoon; Ankur Taly; Soheil Feizi; Tomas Pfister (CoRR, 2022)

Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts. - Rui Wang 086; Yihe Dong; Sercan Ö. Arik; Rose Yu (CoRR, 2022)

Algorithmic fairness in pandemic forecasting: lessons from COVID-19. - Thomas C. Tsai; Sercan Ö. Arik; Benjamin H. Jacobson; Jinsung Yoon; Nate Yoder; Dario Sava; Margaret Mitchell; Garth Graham; Tomas Pfister (npj Digit. Medicine, 2022)

LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling. - Jinsung Yoon; Sercan Ö. Arik; Tomas Pfister (Trans. Mach. Learn. Res., 2022)

Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. - Jinsung Yoon; Kihyuk Sohn; Chun-Liang Li; Sercan Ö. Arik; Chen-Yu Lee; Tomas Pfister (Trans. Mach. Learn. Res., 2022)

Neural Spline Search for Quantile Probabilistic Modeling. - Ruoxi Sun 002; Chun-Liang Li; Sercan Ö. Arik; Michael W. Dusenberry; Chen-Yu Lee; Tomas Pfister (AAAI, 2023)

Better Zero-Shot Reasoning with Self-Adaptive Prompting. - Xingchen Wan; Ruoxi Sun 002; Hanjun Dai; Sercan Ö. Arik; Tomas Pfister (ACL (Findings), 2023)

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. - Jiefeng Chen 001; Jinsung Yoon; Sayna Ebrahimi; Sercan Ö. Arik; Tomas Pfister; Somesh Jha (EMNLP (Findings), 2023)

SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data. - Ruoxi Sun 002; Sercan Ö. Arik; Rajarishi Sinha; Hootan Nakhost; Hanjun Dai; Pengcheng Yin; Tomas Pfister (EMNLP (Findings), 2023)

Universal Self-Adaptive Prompting. - Xingchen Wan; Ruoxi Sun 002; Hootan Nakhost; Hanjun Dai; Julian Eisenschlos; Sercan Ö. Arik; Tomas Pfister (EMNLP, 2023)

Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts. - Rui Wang 086; Yihe Dong; Sercan Ö. Arik; Rose Yu (ICLR, 2023)

Data-Efficient and Interpretable Tabular Anomaly Detection. - Chun-Hao Chang; Jinsung Yoon; Sercan Ö. Arik; Madeleine Udell; Tomas Pfister (KDD, 2023)

TSMixer: An all-MLP Architecture for Time Series Forecasting. - Si-An Chen; Chun-Liang Li; Nate Yoder; Sercan Ö. Arik; Tomas Pfister (CoRR, 2023)

SLM: End-to-end Feature Selection via Sparse Learnable Masks. - Yihe Dong; Sercan Ö. Arik (CoRR, 2023)

LANISTR: Multimodal Learning from Structured and Unstructured Data. - Sayna Ebrahimi; Sercan Ö. Arik; Yihe Dong; Tomas Pfister (CoRR, 2023)

SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL. - Ruoxi Sun 002; Sercan Ö. Arik; Hootan Nakhost; Hanjun Dai; Rajarishi Sinha; Pengcheng Yin; Tomas Pfister (CoRR, 2023)

Business Metric-Aware Forecasting for Inventory Management. - Helen Zhou; Sercan Ö. Arik; Jingtao Wang (CoRR, 2023)

PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series. - Nicasia Beebe-Wang; Sayna Ebrahimi; Jinsung Yoon; Sercan Ö. Arik; Tomas Pfister (CoRR, 2023)

Search-Adaptor: Text Embedding Customization for Information Retrieval. - Jinsung Yoon; Sercan Ö. Arik; Yanfei Chen; Tomas Pfister (CoRR, 2023)

COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning. - Chuizheng Meng; Yihe Dong; Sercan Ö. Arik; Yan Liu 002; Tomas Pfister (CoRR, 2023)

Effective Large Language Model Adaptation for Improved Grounding. - Xi Ye; Ruoxi Sun 002; Sercan Ö. Arik; Tomas Pfister (CoRR, 2023)

EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records. - 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; et al. (npj Digit. Medicine, 2023)

TSMixer: An All-MLP Architecture for Time Series Forecast-ing. - Si-An Chen; Chun-Liang Li; Sercan Ö. Arik; Nathanael C. Yoder; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

Test-Time Adaptation for Visual Document Understanding. - Sayna Ebrahimi; Sercan Ö. Arik; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

Invariant Structure Learning for Better Generalization and Causal Explainability. - Yunhao Ge; Sercan Ö. Arik; Jinsung Yoon; Ao Xu; Laurent Itti; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

Interpretable Mixture of Experts. - Aya Abdelsalam Ismail; Sercan Ö. Arik; Jinsung Yoon; Ankur Taly; Soheil Feizi; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. - Jinsung Yoon; Kihyuk Sohn; Chun-Liang Li; Sercan Ö. Arik; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

TextGenSHAP: Scalable Post-Hoc Explanations in Text Generation with Long Documents. - James Enouen; Hootan Nakhost; Sayna Ebrahimi; Sercan Ö. Arik; Yan Liu 002; Tomas Pfister (ACL (Findings), 2024)

Search-Adaptor: Embedding Customization for Information Retrieval. - Jinsung Yoon; Yanfei Chen; Sercan Ö. Arik; Tomas Pfister (ACL (1), 2024)

Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions. - Jinsung Yoon; Rajarishi Sinha; Sercan Ömer Arik; Tomas Pfister (EMNLP, 2024)

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting. - Defu Cao; Furong Jia 002; Sercan Ö. Arik; Tomas Pfister; Yixiang Zheng; Wen Ye 001; Yan Liu 002 (ICLR, 2024)

Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning. - Sungwon Han 001; Jinsung Yoon; Sercan Ö. Arik; Tomas Pfister (ICML, 2024)

Effective Large Language Model Adaptation for Improved Grounding and Citation Generation. - Xi Ye; Ruoxi Sun 002; Sercan Ö. Arik; Tomas Pfister (NAACL-HLT, 2024)

Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization. - Xingchen Wan; Ruoxi Sun 002; Hootan Nakhost; Sercan Ö. Arik (NeurIPS, 2024)

Chain of Agents: Large Language Models Collaborating on Long-Context Tasks. - Yusen Zhang 001; Ruoxi Sun 002; Yanfei Chen; Tomas Pfister; Rui Zhang 037; Sercan Ö. Arik (NeurIPS, 2024)

Mitigating Object Hallucination via Data Augmented Contrastive Tuning. - Pritam Sarkar; Sayna Ebrahimi; Ali Etemad; Ahmad Beirami; Sercan Ö. Arik; Tomas Pfister (CoRR, 2024)

Learned Feature Importance Scores for Automated Feature Engineering. - Yihe Dong; Sercan Ö. Arik; Nathanael C. Yoder; Tomas Pfister (CoRR, 2024)

CROME: Cross-Modal Adapters for Efficient Multimodal LLM. - Sayna Ebrahimi; Sercan Ö. Arik; Tejas Nama; Tomas Pfister (CoRR, 2024)

SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging. - Mohammadreza Pourreza; Ruoxi Sun 002; Hailong Li; Lesly Miculicich; Tomas Pfister; Sercan Ö. Arik (CoRR, 2024)

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. - Jiefeng Chen 001; Jinsung Yoon; Sayna Ebrahimi; Sercan Ö. Arik; Somesh Jha; Tomas Pfister (Trans. Mach. Learn. Res., 2024)

SQL-PaLM: Improved large language model adaptation for Text-to-SQL. - Ruoxi Sun 002; Sercan Ö. Arik; Alexandre Muzio; Lesly Miculicich; Satya Kesav Gundabathula; Pengcheng Yin; Hanjun Dai; Hootan Nakhost; Rajarishi Sinha; Zifeng Wang 002; Tomas Pfister (Trans. Mach. Learn. Res., 2024)

Provable Membership Inference Privacy. - Zachary Izzo; Jinsung Yoon; Sercan Ö. Arik; James Zou 001 (Trans. Mach. Learn. Res., 2024)

Data-Centric Improvements for Enhancing Multi-Modal Understanding in Spoken Conversation Modeling. - Maximillian Chen; Ruoxi Sun 002; Sercan Ö. Arik (ACL (Findings), 2025)

Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models. - Fei Wang; Xingchen Wan; Ruoxi Sun 002; Jiefeng Chen 001; Sercan Ö. Arik (ACL (1), 2025)

Embedding-Converter: A Unified Framework for Cross-Model Embedding Transformation. - Jinsung Yoon; Sercan Ö. Arik (ACL (1), 2025)

Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training. - Maximillian Chen; Ruoxi Sun 002; Tomas Pfister; Sercan Ö. Arik (ICLR, 2025)

Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG. - Bowen Jin; Jinsung Yoon; Jiawei Han 001; Sercan Ö. Arik (ICLR, 2025)

CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL. - Mohammadreza Pourreza; Hailong Li; Ruoxi Sun 002; Yeounoh Chung; Shayan Talaei; Gaurav Tarlok Kakkar; Yu Gan; Amin Saberi; Fatma Ozcan; Sercan Ö. Arik (ICLR, 2025)

Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment. - Pritam Sarkar; Sayna Ebrahimi; Ali Etemad; Ahmad Beirami; Sercan Ö. Arik; Tomas Pfister (ICLR, 2025)

Learn-by-interact: A Data-Centric Framework For Self-Adaptive Agents in Realistic Environments. - Hongjin Su; Ruoxi Sun 002; Jinsung Yoon; Pengcheng Yin; Tao Yu 009; Sercan Ö. Arik (ICLR, 2025)

BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval. - Hongjin Su; Howard Yen; Mengzhou Xia; Weijia Shi; Niklas Muennighoff; Han-yu Wang; Haisu Liu; Quan Shi; Zachary S. Siegel; Michael Tang; Ruoxi Sun 002; Jinsung Yoon; et al. (ICLR, 2025)

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation. - Xingchen Wan; Han Zhou 010; Ruoxi Sun 002; Sercan Ö. Arik (ICLR, 2025)

LLM-Guided Self-Supervised Tabular Learning With Task-Specific Pre-text Tasks. - Sungwon Han 001; Seungeon Lee 001; Meeyoung Cha; Sercan Ö. Arik; Jinsung Yoon (Trans. Mach. Learn. Res., 2025)

SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling. - Jiefeng Chen 001; Jie Ren; Xinyun Chen; Chengrun Yang; Ruoxi Sun 002; Sercan Ö. Arik (CoRR, 2025)

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation. - Xingchen Wan; Han Zhou 010; Ruoxi Sun 002; Hootan Nakhost; Ke Jiang; Sercan Ö. Arik (CoRR, 2025)

Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies. - Han Zhou 010; Xingchen Wan; Ruoxi Sun 002; Hamid Palangi; Shariq Iqbal; Ivan Vulic; Anna Korhonen; Sercan Ö. Arik (CoRR, 2025)

LLM Alignment as Retriever Optimization: An Information Retrieval Perspective. - Bowen Jin; Jinsung Yoon; Zhen Qin 001; Ziqi Wang 003; Wei Xiong 015; Yu Meng 001; Jiawei Han 001; Sercan Ö. Arik (CoRR, 2025)

Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL. - Mohammadreza Pourreza; Shayan Talaei; Ruoxi Sun 002; Xingchen Wan; Hailong Li; Azalia Mirhoseini; Amin Saberi; Sercan Ö. Arik (CoRR, 2025)

Retrieval Augmented Time Series Forecasting. - Sungwon Han 001; Seungeon Lee 001; Meeyoung Cha; Sercan Ö. Arik; Jinsung Yoon (CoRR, 2025)

An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agents. - Bowen Jin; Jinsung Yoon; Priyanka Kargupta; Sercan Ö. Arik; Jiawei Han 001 (CoRR, 2025)

MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement. - Jaehyun Nam; Jinsung Yoon; Jiefeng Chen 001; Jinwoo Shin; Sercan Ö. Arik; Tomas Pfister (CoRR, 2025)

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling. - Fei Wang; Xingchen Wan; Ruoxi Sun 002; Jiefeng Chen 001; Sercan Ö. Arik (CoRR, 2025)