Samet Oymak

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 118

Top Venues

Publications

Sk Miraj Ahmed, Fahim Faisal Niloy, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury
MeTA: Multi-source Test Time Adaptation. CoRR (2024)
Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler, Ananthram Swami, Samet Oymak, Amit K. Roy-Chowdhury
Plug-and-Play Transformer Modules for Test-Time Adaptation. CoRR (2024)
Muhammed Emrullah Ildiz, Yixiao Huang, Yingcong Li, Ankit Singh Rawat, Samet Oymak
From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers. CoRR (2024)
Xuechen Zhang, Mingchen Li, Jiasi Chen, Christos Thrampoulidis, Samet Oymak
Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective. CoRR (2024)
Karthik Elamvazhuthi, Xuechen Zhang, Matthew Jacobs, Samet Oymak, Fabio Pasqualetti
A Score-Based Deterministic Diffusion Algorithm with Smooth Scores for General Distributions. AAAI (2024)
Yingcong Li, Yixiao Huang, Muhammed Emrullah Ildiz, Ankit Singh Rawat, Samet Oymak
Mechanics of Next Token Prediction with Self-Attention. CoRR (2024)
Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler, Ananthram Swami, Samet Oymak, Amit K. Roy-Chowdhury
FLASH: Federated Learning Across Simultaneous Heterogeneities. CoRR (2024)
Xuechen Zhang, Zijian Huang, Ege Onur Taga, Carlee Joe-Wong, Samet Oymak, Jiasi Chen
TREACLE: Thrifty Reasoning via Context-Aware LLM and Prompt Selection. CoRR (2024)
Yingcong Li, Yixiao Huang, Muhammed Emrullah Ildiz, Ankit Singh Rawat, Samet Oymak
Mechanics of Next Token Prediction with Self-Attention. AISTATS (2024)
Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks. CoRR (2024)
Xuechen Zhang, Mingchen Li, Jiasi Chen, Christos Thrampoulidis, Samet Oymak
Class-Attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective. AAAI (2024)
Mingchen Li, Xuechen Zhang, Yixiao Huang, Samet Oymak
On the Power of Convolution Augmented Transformer. CoRR (2024)
Muhammed Emrullah Ildiz, Zhe Zhao, Samet Oymak
Understanding Inverse Scaling and Emergence in Multitask Representation Learning. AISTATS (2024)
Fahim Faisal Niloy, Sk Miraj Ahmed, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury
Effective Restoration of Source Knowledge in Continual Test Time Adaptation. WACV (2024)
Yingcong Li, Ankit Singh Rawat, Samet Oymak
Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond. CoRR (2024)
Zhe Du, Haldun Balim, Samet Oymak, Necmiye Ozay
Can Transformers Learn Optimal Filtering for Unknown Systems? IEEE Control. Syst. Lett. 7 (2023)
Yingcong Li, Samet Oymak
On The Fairness of Multitask Representation Learning. ICASSP (2023)
Davoud Ataee Tarzanagh, Yingcong Li, Xuechen Zhang, Samet Oymak
Max-Margin Token Selection in Attention Mechanism. CoRR (2023)
Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris Papailiopoulos, Samet Oymak
Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning. NeurIPS (2023)
Yuzhen Qin, Yingcong Li, Fabio Pasqualetti, Maryam Fazel, Samet Oymak
Stochastic Contextual Bandits with Long Horizon Rewards. CoRR (2023)
Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, Christos Thrampoulidis
On the Role of Attention in Prompt-tuning. ICML (2023)
Xuechen Zhang, Mingchen Li, Xiangyu Chang, Jiasi Chen, Amit K. Roy-Chowdhury, Ananda Theertha Suresh, Samet Oymak
FedYolo: Augmenting Federated Learning with Pretrained Transformers. CoRR (2023)
Yingcong Li, Samet Oymak
Provable Pathways: Learning Multiple Tasks over Multiple Paths. CoRR (2023)
Davoud Ataee Tarzanagh, Yingcong Li, Christos Thrampoulidis, Samet Oymak
Transformers as Support Vector Machines. CoRR (2023)
Xuechen Zhang, Zheng Li, Samet Oymak, Jiasi Chen
Text-to-3D Generative AI on Mobile Devices: Measurements and Optimizations. EMS@SIGCOMM (2023)
Yuzhen Qin, Yingcong Li, Fabio Pasqualetti, Maryam Fazel, Samet Oymak
Stochastic Contextual Bandits with Long Horizon Rewards. AAAI (2023)
Davoud Ataee Tarzanagh, Mingchen Li, Pranay Sharma, Samet Oymak
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation. CoRR (2023)
Karthik Elamvazhuthi, Samet Oymak, Fabio Pasqualetti
Noise in the reverse process improves the approximation capabilities of diffusion models. CoRR (2023)
Yingcong Li, M. Emrullah Ildiz, Dimitris S. Papailiopoulos, Samet Oymak
Transformers as Algorithms: Generalization and Implicit Model Selection in In-context Learning. CoRR (2023)
Yingcong Li, Samet Oymak
Provable Pathways: Learning Multiple Tasks over Multiple Paths. AAAI (2023)
Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, Samet Oymak
Transformers as Algorithms: Generalization and Stability in In-context Learning. ICML (2023)
Haldun Balim, Zhe Du, Samet Oymak, Necmiye Ozay
Can Transformers Learn Optimal Filtering for Unknown Systems? CoRR (2023)
Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris S. Papailiopoulos, Samet Oymak
Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs. CoRR (2023)
Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs. CoRR (2023)
Davoud Ataee Tarzanagh, Yingcong Li, Xuechen Zhang, Samet Oymak
Max-Margin Token Selection in Attention Mechanism. NeurIPS (2023)
Fahim Faisal Niloy, Sk Miraj Ahmed, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury
Effective Restoration of Source Knowledge in Continual Test Time Adaptation. CoRR (2023)
Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs. L4DC (2023)
Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, Christos Thrampoulidis
On the Role of Attention in Prompt-tuning. CoRR (2023)
Yingcong Li, Mingchen Li, M. Salman Asif, Samet Oymak
Provable and Efficient Continual Representation Learning. CoRR (2022)
Yahya Sattar, Samet Oymak
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems. J. Mach. Learn. Res. 23 (2022)
Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak
FedNest: Federated Bilevel, Minimax, and Compositional Optimization. ICML (2022)
Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak
FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization. CoRR (2022)
Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti
Representation Learning for Context-Dependent Decision-Making. CoRR (2022)
Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk, Samet Oymak, Maryam Fazel
Towards Sample-efficient Overparameterized Meta-learning. CoRR (2022)
Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti
Non-Stationary Representation Learning in Sequential Linear Bandits. CoRR (2022)
Yahya Sattar, Samet Oymak, Necmiye Ozay
Finite Sample Identification of Bilinear Dynamical Systems. CoRR (2022)
Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti
Representation Learning for Context-Dependent Decision-Making. ACC (2022)
Yahya Sattar, Samet Oymak, Necmiye Ozay
Finite Sample Identification of Bilinear Dynamical Systems. CDC (2022)
Mingchen Li, Xuechen Zhang, Christos Thrampoulidis, Jiasi Chen, Samet Oymak
AutoBalance: Optimized Loss Functions for Imbalanced Data. CoRR (2022)
Samet Oymak, Necmiye Ozay
Revisiting Ho-Kalman-Based System Identification: Robustness and Finite-Sample Analysis. IEEE Trans. Autom. Control. 67 (4) (2022)
Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh, Laura Balzano, Necmiye Ozay, Samet Oymak
Data-Driven Control of Markov Jump Systems: Sample Complexity and Regret Bounds. ACC (2022)
Yue Sun, Samet Oymak, Maryam Fazel
System Identification via Nuclear Norm Regularization. CoRR (2022)
Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh, Samet Oymak, Laura Balzano, Necmiye Ozay
Certainty Equivalent Quadratic Control for Markov Jump Systems. ACC (2022)
Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi
Generalization Guarantees for Neural Architecture Search with Train-Validation Split. ICML (2021)
Maryam Shahcheraghi, Trevor Cappon, Samet Oymak, Evangelos E. Papalexakis, Eamonn J. Keogh, Zachary Zimmerman, Philip Brisk
Matrix Profile Index Approximation for Streaming Time Series. IEEE BigData (2021)
Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh, Laura Balzano, Samet Oymak, Necmiye Ozay
Certainty Equivalent Quadratic Control for Markov Jump Systems. CoRR (2021)
Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul, Samet Oymak, Amit K. Roy-Chowdhury
Unsupervised Multi-Source Domain Adaptation Without Access to Source Data. CVPR (2021)
Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh, Laura Balzano, Necmiye Ozay, Samet Oymak
Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds. CoRR (2021)
Samet Oymak
Super-Convergence with an Unstable Learning Rate. CoRR (2021)
Nhat X. T. Le, A. B. Siddique, Fuad T. Jamour, Samet Oymak, Vagelis Hristidis
Predictable and Adaptive Goal-oriented Dialog Policy Generation. ICSC (2021)
Mohammad Reza Shahneh, Samet Oymak, Amr Magdy
A-GWR: Fast and Accurate Geospatial Inference via Augmented Geographically Weighted Regression. SIGSPATIAL/GIS (2021)
Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, Christos Thrampoulidis
Label-Imbalanced and Group-Sensitive Classification under Overparameterization. NeurIPS (2021)
Xuechen Zhang, Samet Oymak, Jiasi Chen
Post-hoc Models for Performance Estimation of Machine Learning Inference. CoRR (2021)
Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul, Samet Oymak, Amit K. Roy-Chowdhury
Unsupervised Multi-source Domain Adaptation Without Access to Source Data. CoRR (2021)
Xiangyu Chang, Yingcong Li, Samet Oymak, Christos Thrampoulidis
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks. AAAI (2021)
Yao-Chun Chan, Mingchen Li, Samet Oymak
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat its Cake? ICASSP (2021)
Samet Oymak, Talha Cihad Gulcu
A Theoretical Characterization of Semi-supervised Learning with Self-training for Gaussian Mixture Models. AISTATS (2021)
Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, Christos Thrampoulidis
Label-Imbalanced and Group-Sensitive Classification under Overparameterization. CoRR (2021)
Halil Ibrahim Gulluk, Yue Sun, Samet Oymak, Maryam Fazel
Sample Efficient Subspace-Based Representations for Nonlinear Meta-Learning. ICASSP (2021)
Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk, Samet Oymak, Maryam Fazel
Towards Sample-efficient Overparameterized Meta-learning. NeurIPS (2021)
Halil Ibrahim Gulluk, Yue Sun, Samet Oymak, Maryam Fazel
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning. CoRR (2021)
Nhat X. T. Le, A. B. Siddique, Fuad T. Jamour, Samet Oymak, Vagelis Hristidis
Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog Systems. Int. J. Semantic Comput. 15 (4) (2021)
Samet Oymak
Provable Super-Convergence With a Large Cyclical Learning Rate. IEEE Signal Process. Lett. 28 (2021)
Mingchen Li, Xuechen Zhang, Christos Thrampoulidis, Jiasi Chen, Samet Oymak
AutoBalance: Optimized Loss Functions for Imbalanced Data. NeurIPS (2021)
Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi
Generalization Guarantees for Neural Architecture Search with Train-Validation Split. CoRR (2021)
Yue Sun, Samet Oymak, Maryam Fazel
Finite Sample System Identification: Optimal Rates and the Role of Regularization. L4DC (2020)
Xiangyu Chang, Yingcong Li, Samet Oymak, Christos Thrampoulidis
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks. CoRR (2020)
Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View. CoRR (2020)
Yahya Sattar, Samet Oymak
Quickly Finding the Best Linear Model in High Dimensions via Projected Gradient Descent. IEEE Trans. Signal Process. 68 (2020)
Ahmet Demirkaya, Jiasi Chen, Samet Oymak
Exploring the Role of Loss Functions in Multiclass Classification. CISS (2020)
Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks. AISTATS (2020)
Samet Oymak, Mahdi Soltanolkotabi
Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks. IEEE J. Sel. Areas Inf. Theory 1 (1) (2020)
Mingchen Li, Yahya Sattar, Christos Thrampoulidis, Samet Oymak
Exploring Weight Importance and Hessian Bias in Model Pruning. CoRR (2020)
Yuan Zhao, Jiasi Chen, Samet Oymak
On the Role of Dataset Quality and Heterogeneity in Model Confidence. CoRR (2020)
Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View. NeurIPS (2020)
Yahya Sattar, Samet Oymak
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems. CoRR (2020)
A. B. Siddique, Samet Oymak, Vagelis Hristidis
Unsupervised Paraphrasing via Deep Reinforcement Learning. KDD (2020)
A. B. Siddique, Samet Oymak, Vagelis Hristidis
Unsupervised Paraphrasing via Deep Reinforcement Learning. CoRR (2020)
Samet Oymak, Talha Cihad Gulcu
Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training. CoRR (2020)
Yao-Chun Chan, Mingchen Li, Samet Oymak
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake? CoRR (2020)
Hisham Alhulayyil, Kittipat Apicharttrisorn, Jiasi Chen, Karthikeyan Sundaresan, Samet Oymak, Srikanth V. Krishnamurthy
WOLT: Auto-Configuration of Integrated Enterprise PLC-WiFi Networks. ICDCS (2020)
Samet Oymak, M. Salman Asif
Exactly Decoding a Vector through Relu Activation. ICASSP (2019)
Samet Oymak, Necmiye Ozay
Non-asymptotic Identification of LTI Systems from a Single Trajectory. ACC (2019)
Samet Oymak, Mehrdad Mahdavi, Jiasi Chen
Learning Feature Nonlinearities with Regularized Binned Regression. ISIT (2019)
Zachary Zimmerman, Nader Shakibay Senobari, Gareth J. Funning, Evangelos E. Papalexakis, Samet Oymak, Philip Brisk, Eamonn J. Keogh
Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile. ICDM (2019)
Samet Oymak, Zalan Fabian, Mingchen Li, Mahdi Soltanolkotabi
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian. CoRR (2019)
Samet Oymak, Mahdi Soltanolkotabi
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? ICML (2019)
Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks. CoRR (2019)
Samet Oymak, Zalan Fabian, Mingchen Li, Mahdi Soltanolkotabi
Generalization, Adaptation and Low-Rank Representation in Neural Networks. ACSSC (2019)
Samet Oymak, Mahdi Soltanolkotabi
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Samet Oymak