Samet Oymak

发表

Parallel Correlation Clustering on Big Graphs

Dimitris S. Papailiopoulos, Kannan Ramchandran, Michael I. Jordan, 2015, NIPS.

Sharp Time–Data Tradeoffs for Linear Inverse Problems

Benjamin Recht, Samet Oymak, Mahdi Soltanolkotabi, 2015, IEEE Transactions on Information Theory.

New Null Space Results and Recovery Thresholds for Matrix Rank Minimization pdf

Babak Hassibi, Samet Oymak, B. Hassibi, 2010, ArXiv.

Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices

Yonina C. Eldar, Babak Hassibi, Samet Oymak, 2012, IEEE Transactions on Information Theory.

Simple error bounds for regularized noisy linear inverse problems

Christos Thrampoulidis, Babak Hassibi, Samet Oymak, 2014, 2014 IEEE International Symposium on Information Theory.

Isometric sketching of any set via the Restricted Isometry Property pdf

Benjamin Recht, Samet Oymak, Mahdi Soltanolkotabi, 2015, ArXiv.

Asymptotically Exact Denoising in Relation to Compressed Sensing

Babak Hassibi, Samet Oymak, B. Hassibi, 2013, ArXiv.

Non-asymptotic Identification of LTI Systems from a Single Trajectory

Samet Oymak, Necmiye Ozay, Samet Oymak, 2018, 2019 American Control Conference (ACC).

A simpler approach to weighted ℓ1 minimization

Babak Hassibi, Samet Oymak, Anilesh K. Krishnaswamy, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Graph Clustering With Missing Data: Convex Algorithms and Analysis

Babak Hassibi, Samet Oymak, Ramya Korlakai Vinayak, 2014, NIPS.

Non-Stationary Representation Learning in Sequential Multi-Armed Bandits

Samet Oymak, F. Pasqualetti, S. Ching, 2021 .

WOLT: Auto-Configuration of Integrated Enterprise PLC-WiFi Networks

Samet Oymak, S. Krishnamurthy, K. Sundaresan, 2020, 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS).

Convex Relaxation for Low-Dimensional Representation: Phase Transitions and Limitations

Samet Oymak, 2015 .

Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing

B. Hassibi, Samet Oymak, Christos Thrampoulidis, 2015 .

Recovering Structured Signals in Noise : Precise Error Bounds 3 1 . 2 Structured Signals and Noisy Compressed Sensing

B. Hassibi, Samet Oymak, Christos Thrampoulidis, 2015 .

ROBUSTNESS ANALYSIS OF A LIST DECODING ALGORITHM FOR COMPRESSED SENSING

B. Hassibi, Samet Oymak, M. Khajehnejad, 2011 .

Label-Imbalanced and Group-Sensitive Classification under Overparameterization pdf

Christos Thrampoulidis, Samet Oymak, Ganesh Ramachandra Kini, 2021, NeurIPS.

Finite Sample System Identification: Optimal Rates and the Role of Regularization

Samet Oymak, Maryam Fazel, Yue Sun, 2020, L4DC.

Finite Sample System Identification: Improved Rates and the Role of Regularization

M. Fazel, Samet Oymak, Yue Sun, 2020 .

Revisiting Ho–Kalman-Based System Identification: Robustness and Finite-Sample Analysis

Samet Oymak, Necmiye Ozay, Samet Oymak, 2022, IEEE Transactions on Automatic Control.

FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization

Davoud Ataee Tarzanagh, Samet Oymak, Christos Thrampoulidis, 2022, ArXiv.

FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization

Davoud Ataee Tarzanagh, Samet Oymak, Christos Thrampoulidis, 2022, ArXiv.

Sharp MSE Bounds for Proximal Denoising

B. Hassibi, Samet Oymak, 2013, Found. Comput. Math..

Data-Driven Control of Markov Jump Systems: Sample Complexity and Regret Bounds

Davoud Ataee Tarzanagh, Samet Oymak, L. Balzano, 2022, 2022 American Control Conference (ACC).

Representation Learning for Context-Dependent Decision-Making

Samet Oymak, F. Pasqualetti, S. Ching, 2022, 2022 American Control Conference (ACC).

FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization

Davoud Ataee Tarzanagh, Samet Oymak, Christos Thrampoulidis, 2022, ICML.

Matrix Profile Index Approximation for Streaming Time Series

Eamonn J. Keogh, Samet Oymak, E. Papalexakis, 2021, 2021 IEEE International Conference on Big Data (Big Data).

Finite Sample Identification of Low-Order LTI Systems via Nuclear Norm Regularization

M. Fazel, Samet Oymak, Yue Sun, 2022, IEEE Open Journal of Control Systems.

Provable and Efficient Continual Representation Learning

M. Salman Asif, Samet Oymak, Mingchen Li, 2022, ArXiv.

Sharp MSE Bounds for Proximal Denoising

Samet Oymak, Babak Hassibi, 2015, Foundations of Computational Mathematics.

Data Enrichment : Multitask Learning in High Dimension with Theoretical Guarantees

Amir H. Asiaee, A. Banerjee, K. Coombes, 2019 .

A Tight Version of the Gaussian min-max theorem in the Presence of Convexity pdf

Christos Thrampoulidis, Babak Hassibi, Samet Oymak, 2014, ArXiv.

Regularized Linear Regression: A Precise Analysis of the Estimation Error

Christos Thrampoulidis, Babak Hassibi, Samet Oymak, 2015, COLT.

The squared-error of generalized LASSO: A precise analysis

Christos Thrampoulidis, Babak Hassibi, Samet Oymak, 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks

Samet Oymak, Mahdi Soltanolkotabi, M. Soltanolkotabi, 2019, IEEE Journal on Selected Areas in Information Theory.

Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View pdf

Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi, 2020, NeurIPS.

Sharp performance bounds for graph clustering via convex optimization

Babak Hassibi, Samet Oymak, Ramya Korlakai Vinayak, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Quickly Finding the Best Linear Model in High Dimensions via Projected Gradient Descent

Samet Oymak, Yahya Sattar, Samet Oymak, 2019, IEEE Transactions on Signal Processing.

Finding Dense Clusters via "Low Rank + Sparse" Decomposition pdf

Babak Hassibi, Samet Oymak, B. Hassibi, 2011, ArXiv.

Unsupervised Multi-source Domain Adaptation Without Access to Source Data

Dripta S. Raychaudhuri, Sk. Miraj Ahmed, Samet Oymak, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Universality laws for randomized dimension reduction, with applications pdf

Joel A. Tropp, Samet Oymak, J. Tropp, 2015, ArXiv.

Sparse phase retrieval: Convex algorithms and limitations

Babak Hassibi, Samet Oymak, Kishore Jaganathan, 2013, 2013 IEEE International Symposium on Information Theory.

Near-Optimal Bounds for Binary Embeddings of Arbitrary Sets pdf

Benjamin Recht, Samet Oymak, B. Recht, 2015, ArXiv.

Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks pdf

Samet Oymak, Mahdi Soltanolkotabi, Mingchen Li, 2019, AISTATS.

Phase retrieval for sparse signals using rank minimization

Babak Hassibi, Samet Oymak, Kishore Jaganathan, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? pdf

Samet Oymak, Mahdi Soltanolkotabi, M. Soltanolkotabi, 2018, ICML.

Transformers as Algorithms: Generalization and Implicit Model Selection in In-context Learning

Dimitris Papailiopoulos, Samet Oymak, Yingcong Li, 2023, ArXiv.

AutoBalance: Optimized Loss Functions for Imbalanced Data pdf

Christos Thrampoulidis, Samet Oymak, Xuechen Zhang, 2022, NeurIPS.

Transformers as Algorithms: Generalization and Stability in In-context Learning

Dimitris Papailiopoulos, Samet Oymak, Yingcong Li, 2023, ICML.

On the Marginal Benefit of Active Learning: Does Self-Supervision Eat its Cake? pdf

Samet Oymak, Mingchen Li, Yao-Chun Chan, 2020, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Learning Compact Neural Networks with Regularization

Samet Oymak, Samet Oymak, 2018, ICML.

Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks pdf

Christos Thrampoulidis, Xiangyu Chang, Samet Oymak, 2020, AAAI.

Non-Stationary Representation Learning in Sequential Linear Bandits

Samet Oymak, F. Pasqualetti, S. Ching, 2022, IEEE Open Journal of Control Systems.

Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems pdf

Samet Oymak, Yahya Sattar, Samet Oymak, 2020, J. Mach. Learn. Res..

Sparse Phase Retrieval: Uniqueness Guarantees and Recovery Algorithms

Babak Hassibi, Samet Oymak, Kishore Jaganathan, 2013, IEEE Transactions on Signal Processing.

On robust phase retrieval for sparse signals

Babak Hassibi, Samet Oymak, Kishore Jaganathan, 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

A Theoretical Characterization of Semi-supervised Learning with Self-training for Gaussian Mixture Models

Samet Oymak, Talha Cihad Gulcu, Samet Oymak, 2021, AISTATS.

Learning a deep convolutional neural network via tensor decomposition

Samet Oymak, Mahdi Soltanolkotabi, M. Soltanolkotabi, 2021 .

End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition pdf

Samet Oymak, Mahdi Soltanolkotabi, M. Soltanolkotabi, 2018, ArXiv.

A Simple Framework for Learning Stabilizable Systems

Samet Oymak, Yahya Sattar, Samet Oymak, 2019, 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

Stochastic Gradient Descent Learns State Equations with Nonlinear Activations

Samet Oymak, Samet Oymak, 2018, COLT.

Unsupervised Paraphrasing via Deep Reinforcement Learning

Vagelis Hristidis, Samet Oymak, A. B. Siddique, 2020, KDD.

Learning Feature Nonlinearities with Regularized Binned Regression

Samet Oymak, Mehrdad Mahdavi, Jiasi Chen, 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression pdf

Samet Oymak, Mehrdad Mahdavi, Jiasi Chen, 2017, ArXiv.

Generalization, Adaptation and Low-Rank Representation in Neural Networks

Mahdi Soltanolkotabi, Zalan Fabian, Samet Oymak, 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.

Sample Efficient Subspace-Based Representations for Nonlinear Meta-Learning

Samet Oymak, Maryam Fazel, Yue Sun, 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Max-Margin Token Selection in Attention Mechanism

Davoud Ataee Tarzanagh, Samet Oymak, Yingcong Li, 2023, ArXiv.

Margin Maximization in Attention Mechanism

Davoud Ataee Tarzanagh, Samet Oymak, Yingcong Li, 2023 .

Exploring the Role of Loss Functions in Multiclass Classification

Samet Oymak, Jiasi Chen, Ahmet Demirkaya, 2020, 2020 54th Annual Conference on Information Sciences and Systems (CISS).

Stochastic Contextual Bandits with Long Horizon Rewards

Samet Oymak, F. Pasqualetti, Yuzhen Qin, 2023, AAAI.

A-GWR: Fast and Accurate Geospatial Inference via Augmented Geographically Weighted Regression

Samet Oymak, Amr Magdy, Mohammad Reza Shahneh, 2021, SIGSPATIAL/GIS.

Recovery of sparse 1-D signals from the magnitudes of their Fourier transform

Babak Hassibi, Samet Oymak, Kishore Jaganathan, 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

Post-hoc Models for Performance Estimation of Machine Learning Inference pdf

Samet Oymak, Jiasi Chen, Xuechen Zhang, 2021, ArXiv.

On the Role of Dataset Quality and Heterogeneity in Model Confidence pdf

Yuan Zhao, Samet Oymak, Jiasi Chen, 2020, ArXiv.

Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation

Davoud Ataee Tarzanagh, Samet Oymak, Pranay Sharma, 2023, ArXiv.

FEDNEST: Federated Bilevel Optimization

Davoud Ataee Tarzanagh, Samet Oymak, Christos Thrampoulidis, 2022 .

Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile

Eamonn J. Keogh, Evangelos E. Papalexakis, Philip Brisk, 2019, 2019 IEEE International Conference on Data Mining (ICDM).

Recovering Jointly Sparse Signals via Joint Basis Pursuit pdf

Babak Hassibi, Samet Oymak, B. Hassibi, 2012, ArXiv.

Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog Systems

Nhat X. T. Le, Samet Oymak, Vagelis Hristidis, 2021, Int. J. Semantic Comput..

Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs

Samet Oymak, F. Pasqualetti, Karthik Elamvazhuthi, 2023, L4DC.

Provable Super-Convergence With a Large Cyclical Learning Rate

Samet Oymak, Samet Oymak, 2021, IEEE Signal Processing Letters.

Exploring Weight Importance and Hessian Bias in Model Pruning pdf

Christos Thrampoulidis, Samet Oymak, Mingchen Li, 2020, ArXiv.

Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian pdf

Samet Oymak, Mahdi Soltanolkotabi, Zalan Fabian, 2019, ArXiv.

Super-Convergence with an Unstable Learning Rate pdf

Samet Oymak, Samet Oymak, 2021, ArXiv.

Super-Convergence with an Unstably Large Learning Rate

Samet Oymak, 2021 .

A simplified approach to recovery conditions for low rank matrices

Babak Hassibi, Samet Oymak, Maryam Fazel, 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training pdf

Samet Oymak, Talha Cihad Gulcu, Samet Oymak, 2020, ArXiv.

Fast and Reliable Parameter Estimation from Nonlinear Observations

Samet Oymak, Mahdi Soltanolkotabi, M. Soltanolkotabi, 2016, SIAM J. Optim..

Simple Bounds for Noisy Linear Inverse Problems with Exact Side Information pdf

Christos Thrampoulidis, Babak Hassibi, Samet Oymak, 2013, ArXiv.

Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs

Kartik K. Sreenivasan, Dimitris Papailiopoulos, Samet Oymak, 2023, ArXiv.

Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds pdf

Laura Balzano, Samet Oymak, Necmiye Ozay, 2021, ArXiv.

Transformers as Support Vector Machines

Davoud Ataee Tarzanagh, Samet Oymak, Christos Thrampoulidis, 2023, ArXiv.

On the Role of Attention in Prompt-tuning

A. Rawat, M. Soltanolkotabi, Samet Oymak, 2023, ICML.

Max-Margin Token Selection in Attention Mechanism

Davoud Ataee Tarzanagh, Samet Oymak, Yingcong Li, 2023, NeurIPS.

High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient pdf

Arindam Banerjee, Samet Oymak, Kevin R. Coombes, 2018, ArXiv.

The proportional mean decomposition: A bridge between the Gaussian and bernoulli ensembles

Babak Hassibi, Samet Oymak, B. Hassibi, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Provable Pathways: Learning Multiple Tasks over Multiple Paths

Samet Oymak, Yingcong Li, 2023, AAAI.

System Identification via Nuclear Norm Regularization

Samet Oymak, Yue Sun, Maryam Fazel, 2022, ArXiv.

Noise in the reverse process improves the approximation capabilities of diffusion models

Samet Oymak, Karthik Elamvazhuthi, Fabio Pasqualetti, 2023, 2312.07851.

Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning

Kartik K. Sreenivasan, Dimitris Papailiopoulos, Samet Oymak, 2023, NeurIPS.

Text-to-3D Generative AI on Mobile Devices: Measurements and Optimizations

Samet Oymak, Jiasi Chen, Zheng Li, 2023, EMS@SIGCOMM.

Plug-and-Play Transformer Modules for Test-Time Adaptation

Sk. Miraj Ahmed, A. Swami, Samet Oymak, 2024, ArXiv.

Unsupervised Multi-source Domain Adaptation Without Access to Source Data

Amit K. Roy-Chowdhury, Samet Oymak, Sujoy Paul, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

FedYolo: Augmenting Federated Learning with Pretrained Transformers

A. Suresh, Samet Oymak, A. Roy-Chowdhury, 2023, ArXiv.

Mechanics of Next Token Prediction with Self-Attention

A. Rawat, Samet Oymak, Yingcong Li, 2024, AISTATS.

From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers

A. Rawat, Samet Oymak, Yingcong Li, 2024, 2402.13512.

Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Dimitris Papailiopoulos, Samet Oymak, Nayoung Lee, 2024, ArXiv.

Sharp MSE Bounds for Proximal Denoising

B. Hassibi, Samet Oymak, 2013, Foundations of Computational Mathematics.

Finding Dense Clusters via "Low Rank + Sparse" Decomposition

B. Hassibi, Samet Oymak, 2011, ArXiv.

On The Fairness of Multitask Representation Learning

Samet Oymak, Yingcong Li, 2023, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

MeTA: Multi-source Test Time Adaptation

Dripta S. Raychaudhuri, Sk. Miraj Ahmed, Fahim Faisal Niloy, 2024, ArXiv.

Effective Restoration of Source Knowledge in Continual Test Time Adaptation

Dripta S. Raychaudhuri, Sk. Miraj Ahmed, Fahim Faisal Niloy, 2023, 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).