Sercan Ö. Arik

Orcid: 0000-0001-6333-1729

Affiliations:
  • Google Cloud AI, Sunnyvale, CA, USA
  • Stanford University, Department of Electrical Engineering, CA, USA (PhD 2016)
  • Bilkent University, Depertment of Electrical and Electronics Engineering, Ankara, Turkey


According to our database1, Sercan Ö. Arik authored at least 94 papers between 2011 and 2024.

Collaborative distances:

Timeline

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2020
2022
2024
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Legend:

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Bibliography

2024
Provable Membership Inference Privacy.
Trans. Mach. Learn. Res., 2024

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction.
Trans. Mach. Learn. Res., 2024

Data-Centric Improvements for Enhancing Multi-Modal Understanding in Spoken Conversation Modeling.
CoRR, 2024

Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models.
CoRR, 2024

Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG.
CoRR, 2024

CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL.
CoRR, 2024

SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging.
CoRR, 2024

CROME: Cross-Modal Adapters for Efficient Multimodal LLM.
CoRR, 2024

BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval.
CoRR, 2024

Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization.
CoRR, 2024

Learned Feature Importance Scores for Automated Feature Engineering.
CoRR, 2024

Chain of Agents: Large Language Models Collaborating on Long-Context Tasks.
CoRR, 2024

Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training.
CoRR, 2024

Mitigating Object Hallucination via Data Augmented Contrastive Tuning.
CoRR, 2024

Effective Large Language Model Adaptation for Improved Grounding and Citation Generation.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Search-Adaptor: Embedding Customization for Information Retrieval.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

TextGenSHAP: Scalable Post-Hoc Explanations in Text Generation with Long Documents.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch.
Trans. Mach. Learn. Res., 2023

Interpretable Mixture of Experts.
Trans. Mach. Learn. Res., 2023

Invariant Structure Learning for Better Generalization and Causal Explainability.
Trans. Mach. Learn. Res., 2023

Test-Time Adaptation for Visual Document Understanding.
Trans. Mach. Learn. Res., 2023

TSMixer: An All-MLP Architecture for Time Series Forecast-ing.
Trans. Mach. Learn. Res., 2023

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

Effective Large Language Model Adaptation for Improved Grounding.
CoRR, 2023

COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning.
CoRR, 2023

Search-Adaptor: Text Embedding Customization for Information Retrieval.
CoRR, 2023

PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series.
CoRR, 2023

Business Metric-Aware Forecasting for Inventory Management.
CoRR, 2023

SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL.
CoRR, 2023

LANISTR: Multimodal Learning from Structured and Unstructured Data.
CoRR, 2023

SLM: End-to-end Feature Selection via Sparse Learnable Masks.
CoRR, 2023

TSMixer: An all-MLP Architecture for Time Series Forecasting.
CoRR, 2023

Data-Efficient and Interpretable Tabular Anomaly Detection.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Universal Self-Adaptive Prompting.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Better Zero-Shot Reasoning with Self-Adaptive Prompting.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

Neural Spline Search for Quantile Probabilistic Modeling.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection.
Trans. Mach. Learn. Res., 2022

LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling.
Trans. Mach. Learn. Res., 2022

Algorithmic fairness in pandemic forecasting: lessons from COVID-19.
npj Digit. Medicine, 2022

Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts.
CoRR, 2022

Interpretable Mixture of Experts for Structured Data.
CoRR, 2022

Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series.
CoRR, 2022

Self-Supervised Learning with an Information Maximization Criterion.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Decoupling Local and Global Representations of Time Series.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

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

Self-Trained One-class Classification for Unsupervised Anomaly Detection.
CoRR, 2021

Controlling Neural Networks with Rule Representations.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

TabNet: Attentive Interpretable Tabular Learning.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
ProtoAttend: Attention-Based Prototypical Learning.
J. Mach. Learn. Res., 2020

Interpretable Sequence Learning for COVID-19 Forecasting.
CoRR, 2020

Explaining Deep Neural Networks using Unsupervised Clustering.
CoRR, 2020

On Completeness-aware Concept-Based Explanations in Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Interpretable Sequence Learning for Covid-19 Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Data Valuation using Reinforcement Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Distance-Based Learning from Errors for Confidence Calibration.
Proceedings of the 8th International Conference on Learning Representations, 2020

Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning.
Proceedings of the Computer Vision - ECCV 2020, 2020

Consistency-Based Semi-supervised Active Learning: Towards Minimizing Labeling Cost.
Proceedings of the Computer Vision - ECCV 2020, 2020

Distilling Effective Supervision From Severe Label Noise.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Fast Spectrogram Inversion Using Multi-Head Convolutional Neural Networks.
IEEE Signal Process. Lett., 2019

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting.
CoRR, 2019

On Concept-Based Explanations in Deep Neural Networks.
CoRR, 2019

IEG: Robust Neural Network Training to Tackle Severe Label Noise.
CoRR, 2019

RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling.
CoRR, 2019

Learning to Transfer Learn.
CoRR, 2019

EPNAS: Efficient Progressive Neural Architecture Search.
CoRR, 2019

Attention-Based Prototypical Learning Towards Interpretable, Confident and Robust Deep Neural Networks.
CoRR, 2019

2018
Scaling SDM Optical Networks Using Full-Spectrum Spatial Switching.
JOCN, 2018

Resource-Efficient Neural Architect.
CoRR, 2018

Neural Voice Cloning with a Few Samples.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Deep Voice 3: 2000-Speaker Neural Text-to-Speech.
CoRR, 2017

Low-complexity implementation of convex optimization-based phase retrieval.
CoRR, 2017

Deep Voice: Real-time Neural Text-to-Speech.
CoRR, 2017

Capacity limits of space-division multiplexed submarine links subject to nonlinearities and power feed constraints.
Proceedings of the Optical Fiber Communications Conference and Exhibition, 2017

Deep Voice 2: Multi-Speaker Neural Text-to-Speech.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting.
Proceedings of the 18th Annual Conference of the International Speech Communication Association, 2017

Optical MIMO signal processing for direct-detection mode-division multiplexing.
Proceedings of the 2017 19th International Conference on Transparent Optical Networks (ICTON), 2017

Deep Voice: Real-time Neural Text-to-Speech.
Proceedings of the 34th International Conference on Machine Learning, 2017

Scaling optical networks using full-spectrum spatial switching.
Proceedings of the 18th IEEE International Conference on High Performance Switching and Routing, 2017

2015
MIMO channel statistics and signal processing in mode-division multiplexing systems.
Proceedings of the 16th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2015

MIMO DSP complexity in mode-division multiplexing.
Proceedings of the Optical Fiber Communications Conference and Exhibition, 2015

Group delay statistics and management in mode-division multiplexing.
Proceedings of the 49th Asilomar Conference on Signals, Systems and Computers, 2015

2014
MIMO Signal Processing for Mode-Division Multiplexing: An overview of channel models and signal processing architectures.
IEEE Signal Process. Mag., 2014

Supervised classification-based stock prediction and portfolio optimization.
CoRR, 2014

Comparison of quaternary block-coding and sphere-cutting for high-dimensional modulation.
Proceedings of the Optical Fiber Communications Conference and Exhibition, 2014

High-dimensional modulation for mode-division multiplexing.
Proceedings of the Optical Fiber Communications Conference and Exhibition, 2014

2011
Alignment of uncalibrated images for multi-view classification.
Proceedings of the 18th IEEE International Conference on Image Processing, 2011


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