Samet Oymak

According to our database, Samet Oymak authored at least 123 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws.

Muhammed Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga, Marco Mondelli, Samet Oymak

CoRR, 2024

Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition.

Zheyang Xiong, Ziyang Cai, John Cooper, Albert Ge, Vasilis Papageorgiou, Zack Sifakis, Angeliki Giannou, Ziqian Lin, Liu Yang, Saurabh Agarwal, Grigorios G. Chrysos, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos

CoRR, 2024

Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond.

Yingcong Li, Ankit Singh Rawat, Samet Oymak

CoRR, 2024

On the Power of Convolution Augmented Transformer.

Mingchen Li, Xuechen Zhang, Yixiao Huang, Samet Oymak

CoRR, 2024

TREACLE: Thrifty Reasoning via Context-Aware LLM and Prompt Selection.

Xuechen Zhang, Zijian Huang, Ege Onur Taga, Carlee Joe-Wong, Samet Oymak, Jiasi Chen

CoRR, 2024

FLASH: Federated Learning Across Simultaneous Heterogeneities.

Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler, Ananthram Swami, Samet Oymak, Amit K. Roy-Chowdhury

CoRR, 2024

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

Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler, Ananthram Swami, Samet Oymak, Amit K. Roy-Chowdhury

CoRR, 2024

MeTA: Multi-source Test Time Adaptation.

Sk Miraj Ahmed, Fahim Faisal Niloy, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury

CoRR, 2024

Effective Restoration of Source Knowledge in Continual Test Time Adaptation.

Fahim Faisal Niloy, Sk Miraj Ahmed, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

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

Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Muhammed Emrullah Ildiz, Yixiao Huang, Yingcong Li, Ankit Singh Rawat, Samet Oymak

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Pyneapple-L: Scalable Expressive Learning-based Spatial Analysis.

Yongyi Liu, Nicolas Lee, Yunfan Kang, Mohammad Reza Shahneh, Ahmed Mahmood, Vishal Rohith Chinnam, Aparna Vivek Sarawadekar, Samet Oymak, Ibrahim Sabek, Amr Magdy

Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, 2024

Mechanics of Next Token Prediction with Self-Attention.

Yingcong Li, Yixiao Huang, Muhammed Emrullah Ildiz, Ankit Singh Rawat, Samet Oymak

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Understanding Inverse Scaling and Emergence in Multitask Representation Learning.

Muhammed Emrullah Ildiz, Zhe Zhao, Samet Oymak

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Class-Attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective.

Xuechen Zhang, Mingchen Li, Jiasi Chen, Christos Thrampoulidis, Samet Oymak

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

A Score-Based Deterministic Diffusion Algorithm with Smooth Scores for General Distributions.

Karthik Elamvazhuthi, Xuechen Zhang, Matthew Jacobs, Samet Oymak, Fabio Pasqualetti

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Can Transformers Learn Optimal Filtering for Unknown Systems?

Zhe Du, Haldun Balim, Samet Oymak, Necmiye Ozay

IEEE Control. Syst. Lett., 2023

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

Karthik Elamvazhuthi, Samet Oymak, Fabio Pasqualetti

CoRR, 2023

Transformers as Support Vector Machines.

Davoud Ataee Tarzanagh, Yingcong Li, Christos Thrampoulidis, Samet Oymak

CoRR, 2023

FedYolo: Augmenting Federated Learning with Pretrained Transformers.

Xuechen Zhang, Mingchen Li, Xiangyu Chang, Jiasi Chen, Amit K. Roy-Chowdhury, Ananda Theertha Suresh, Samet Oymak

CoRR, 2023

Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation.

Davoud Ataee Tarzanagh, Mingchen Li, Pranay Sharma, Samet Oymak

CoRR, 2023

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

Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris S. Papailiopoulos, Samet Oymak

CoRR, 2023

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

Yingcong Li, Muhammed Emrullah Ildiz, Dimitris S. Papailiopoulos, Samet Oymak

CoRR, 2023

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

Xuechen Zhang, Zheng Li, Samet Oymak, Jiasi Chen

Proceedings of the 2023 Workshop on Emerging Multimedia Systems, 2023

Max-Margin Token Selection in Attention Mechanism.

Davoud Ataee Tarzanagh, Yingcong Li, Xuechen Zhang, Samet Oymak

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris Papailiopoulos, Samet Oymak

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti

Proceedings of the Learning for Dynamics and Control Conference, 2023

On the Role of Attention in Prompt-tuning.

Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, Christos Thrampoulidis

Proceedings of the International Conference on Machine Learning, 2023

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

Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, Samet Oymak

Proceedings of the International Conference on Machine Learning, 2023

On The Fairness of Multitask Representation Learning.

Yingcong Li, Samet Oymak

Proceedings of the IEEE International Conference on Acoustics, 2023

Stochastic Contextual Bandits with Long Horizon Rewards.

Yuzhen Qin, Yingcong Li, Fabio Pasqualetti, Maryam Fazel, Samet Oymak

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Provable Pathways: Learning Multiple Tasks over Multiple Paths.

Yingcong Li, Samet Oymak

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

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

Samet Oymak, Necmiye Ozay

IEEE Trans. Autom. Control., 2022

Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems.

Yahya Sattar, Samet Oymak

J. Mach. Learn. Res., 2022

System Identification via Nuclear Norm Regularization.

Yue Sun, Samet Oymak, Maryam Fazel

CoRR, 2022

Provable and Efficient Continual Representation Learning.

Yingcong Li, Mingchen Li, M. Salman Asif, Samet Oymak

CoRR, 2022

Non-Stationary Representation Learning in Sequential Linear Bandits.

Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti

CoRR, 2022

FedNest: Federated Bilevel, Minimax, and Compositional Optimization.

Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak

Proceedings of the International Conference on Machine Learning, 2022

Finite Sample Identification of Bilinear Dynamical Systems.

Yahya Sattar, Samet Oymak, Necmiye Ozay

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Certainty Equivalent Quadratic Control for Markov Jump Systems.

Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh, Samet Oymak, Laura Balzano, Necmiye Ozay

Proceedings of the American Control Conference, 2022

Representation Learning for Context-Dependent Decision-Making.

Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti

Proceedings of the American Control Conference, 2022

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

Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh, Laura Balzano, Necmiye Ozay, Samet Oymak

Proceedings of the American Control Conference, 2022

2021

Provable Super-Convergence With a Large Cyclical Learning Rate.

Samet Oymak

IEEE Signal Process. Lett., 2021

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

Nhat X. T. Le, A. B. Siddique, Fuad T. Jamour, Samet Oymak, Vagelis Hristidis

Int. J. Semantic Comput., 2021

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

Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh, Laura Balzano, Necmiye Ozay, Samet Oymak

CoRR, 2021

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

Xuechen Zhang, Samet Oymak, Jiasi Chen

CoRR, 2021

Super-Convergence with an Unstable Learning Rate.

Samet Oymak

CoRR, 2021

Predictable and Adaptive Goal-oriented Dialog Policy Generation.

Nhat X. T. Le, A. B. Siddique, Fuad T. Jamour, Samet Oymak, Vagelis Hristidis

Proceedings of the 15th IEEE International Conference on Semantic Computing, 2021

Towards Sample-efficient Overparameterized Meta-learning.

Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk, Samet Oymak, Maryam Fazel

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

AutoBalance: Optimized Loss Functions for Imbalanced Data.

Mingchen Li, Xuechen Zhang, Christos Thrampoulidis, Jiasi Chen, Samet Oymak

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Label-Imbalanced and Group-Sensitive Classification under Overparameterization.

Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, Christos Thrampoulidis

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Generalization Guarantees for Neural Architecture Search with Train-Validation Split.

Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi

Proceedings of the 38th International Conference on Machine Learning, 2021

Sample Efficient Subspace-Based Representations for Nonlinear Meta-Learning.

Halil Ibrahim Gulluk, Yue Sun, Samet Oymak, Maryam Fazel

Proceedings of the IEEE International Conference on Acoustics, 2021

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

Yao-Chun Chan, Mingchen Li, Samet Oymak

Proceedings of the IEEE International Conference on Acoustics, 2021

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

Mohammad Reza Shahneh, Samet Oymak, Amr Magdy

Proceedings of the SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems, 2021

Unsupervised Multi-Source Domain Adaptation Without Access to Source Data.

Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul, Samet Oymak, Amit K. Roy-Chowdhury

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Matrix Profile Index Approximation for Streaming Time Series.

Maryam Shahcheraghi, Trevor Cappon, Samet Oymak, Evangelos E. Papalexakis, Eamonn J. Keogh, Zachary Zimmerman, Philip Brisk

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

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

Samet Oymak, Talha Cihad Gulcu

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

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

Xiangyu Chang, Yingcong Li, Samet Oymak, Christos Thrampoulidis

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

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

Yahya Sattar, Samet Oymak

IEEE Trans. Signal Process., 2020

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

Samet Oymak, Mahdi Soltanolkotabi

IEEE J. Sel. Areas Inf. Theory, 2020

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

Samet Oymak, Talha Cihad Gulcu

CoRR, 2020

Exploring Weight Importance and Hessian Bias in Model Pruning.

Mingchen Li, Yahya Sattar, Christos Thrampoulidis, Samet Oymak

CoRR, 2020

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

Yuan Zhao, Jiasi Chen, Samet Oymak

CoRR, 2020

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

Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Yue Sun, Samet Oymak, Maryam Fazel

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Unsupervised Paraphrasing via Deep Reinforcement Learning.

A. B. Siddique, Samet Oymak, Vagelis Hristidis

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

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

Hisham Alhulayyil, Kittipat Apicharttrisorn, Jiasi Chen, Karthikeyan Sundaresan, Samet Oymak, Srikanth V. Krishnamurthy

Proceedings of the 40th IEEE International Conference on Distributed Computing Systems, 2020

Exploring the Role of Loss Functions in Multiclass Classification.

Ahmet Demirkaya, Jiasi Chen, Samet Oymak

Proceedings of the 54th Annual Conference on Information Sciences and Systems, 2020

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

Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Quickly Finding the Best Linear Model in High Dimensions.

Yahya Sattar, Samet Oymak

CoRR, 2019

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

Samet Oymak, Zalan Fabian, Mingchen Li, Mahdi Soltanolkotabi

CoRR, 2019

Towards moderate overparameterization: global convergence guarantees for training shallow neural networks.

Samet Oymak, Mahdi Soltanolkotabi

CoRR, 2019

Learning Feature Nonlinearities with Regularized Binned Regression.

Samet Oymak, Mehrdad Mahdavi, Jiasi Chen

Proceedings of the IEEE International Symposium on Information Theory, 2019

Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?

Samet Oymak, Mahdi Soltanolkotabi

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Zachary Zimmerman, Nader Shakibay Senobari, Gareth J. Funning, Evangelos E. Papalexakis, Samet Oymak, Philip Brisk, Eamonn J. Keogh

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

Exactly Decoding a Vector through Relu Activation.

Samet Oymak, M. Salman Asif

Proceedings of the IEEE International Conference on Acoustics, 2019

Stochastic Gradient Descent Learns State Equations with Nonlinear Activations.

Samet Oymak

Proceedings of the Conference on Learning Theory, 2019

A Simple Framework for Learning Stabilizable Systems.

Yahya Sattar, Samet Oymak

Proceedings of the 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019

Non-asymptotic Identification of LTI Systems from a Single Trajectory.

Samet Oymak, Necmiye Ozay

Proceedings of the 2019 American Control Conference, 2019

Generalization, Adaptation and Low-Rank Representation in Neural Networks.

Samet Oymak, Zalan Fabian, Mingchen Li, Mahdi Soltanolkotabi

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

Sharp Time-Data Tradeoffs for Linear Inverse Problems.

Samet Oymak, Benjamin Recht, Mahdi Soltanolkotabi

IEEE Trans. Inf. Theory, 2018

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

Amir Asiaee T., Samet Oymak, Kevin R. Coombes, Arindam Banerjee

CoRR, 2018

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

Samet Oymak, Mahdi Soltanolkotabi

CoRR, 2018

Learning Compact Neural Networks with Regularization.

Samet Oymak

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Sparse Phase Retrieval: Uniqueness Guarantees and Recovery Algorithms.

Kishore Jaganathan, Samet Oymak, Babak Hassibi

IEEE Trans. Signal Process., 2017

Fast and Reliable Parameter Estimation from Nonlinear Observations.

Samet Oymak, Mahdi Soltanolkotabi

SIAM J. Optim., 2017

Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression.

Samet Oymak, Mehrdad Mahdavi, Jiasi Chen

CoRR, 2017

Near-optimal sample complexity bounds for circulant binary embedding.

Samet Oymak, Christos Thrampoulidis, Babak Hassibi

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016

Sharp MSE Bounds for Proximal Denoising.

Samet Oymak, Babak Hassibi

Found. Comput. Math., 2016

Near-Optimal Sample Complexity Bounds for Circulant Binary Embedding.

Samet Oymak

CoRR, 2016

2015

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

Samet Oymak, Amin Jalali, Maryam Fazel, Yonina C. Eldar, Babak Hassibi

IEEE Trans. Inf. Theory, 2015

Universality laws for randomized dimension reduction, with applications.

Samet Oymak, Joel A. Tropp

CoRR, 2015

Isometric sketching of any set via the Restricted Isometry Property.

Samet Oymak, Benjamin Recht, Mahdi Soltanolkotabi

CoRR, 2015

Near-Optimal Bounds for Binary Embeddings of Arbitrary Sets.

Samet Oymak, Ben Recht

CoRR, 2015

Parallel Correlation Clustering on Big Graphs.

Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

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

Samet Oymak, Babak Hassibi

Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Regularized Linear Regression: A Precise Analysis of the Estimation Error.

Christos Thrampoulidis, Samet Oymak, Babak Hassibi

Proceedings of The 28th Conference on Learning Theory, 2015

2014

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

Christos Thrampoulidis, Samet Oymak, Babak Hassibi

CoRR, 2014

Graph Clustering With Missing Data: Convex Algorithms and Analysis.

Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Simple error bounds for regularized noisy linear inverse problems.

Christos Thrampoulidis, Samet Oymak, Babak Hassibi

Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

A case for orthogonal measurements in linear inverse problems.

Samet Oymak, Babak Hassibi

Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Sharp performance bounds for graph clustering via convex optimization.

Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi

Proceedings of the IEEE International Conference on Acoustics, 2014

2013

Asymptotically Exact Denoising in Relation to Compressed Sensing

Samet Oymak, Babak Hassibi

CoRR, 2013

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

Samet Oymak, Christos Thrampoulidis, Babak Hassibi

CoRR, 2013

Sparse phase retrieval: Convex algorithms and limitations.

Kishore Jaganathan, Samet Oymak, Babak Hassibi

Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

Noisy estimation of simultaneously structured models: Limitations of convex relaxation.

Samet Oymak, Amin Jalali, Maryam Fazel, Babak Hassibi

Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

The squared-error of generalized LASSO: A precise analysis.

Samet Oymak, Christos Thrampoulidis, Babak Hassibi

Proceedings of the 51st Annual Allerton Conference on Communication, 2013

2012

Recovering Jointly Sparse Signals via Joint Basis Pursuit

Samet Oymak, Babak Hassibi

CoRR, 2012

Recovery threshold for optimal weight ℓ1 minimization.

Samet Oymak, M. Amin Khajehnejad, Babak Hassibi

Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

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

Kishore Jaganathan, Samet Oymak, Babak Hassibi

Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

Deterministic phase guarantees for robust recovery in incoherent dictionaries.

Cheuk Ting Li, Samet Oymak, Babak Hassibi

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

A simpler approach to weighted ℓ1 minimization.

Anilesh K. Krishnaswamy, Samet Oymak, Babak Hassibi

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

Phase retrieval for sparse signals using rank minimization.

Kishore Jaganathan, Samet Oymak, Babak Hassibi

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

On a relation between the minimax risk and the phase transitions of compressed recovery.

Samet Oymak, Babak Hassibi

Proceedings of the 50th Annual Allerton Conference on Communication, 2012

On robust phase retrieval for sparse signals.

Kishore Jaganathan, Samet Oymak, Babak Hassibi

Proceedings of the 50th Annual Allerton Conference on Communication, 2012

2011

Finding Dense Clusters via "Low Rank + Sparse" Decomposition

Samet Oymak, Babak Hassibi

CoRR, 2011

A simplified approach to recovery conditions for low rank matrices.

Samet Oymak, Karthik Mohan, Maryam Fazel, Babak Hassibi

Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

Subspace expanders and matrix rank minimization.

Samet Oymak, M. Amin Khajehnejad, Babak Hassibi

Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

Tight recovery thresholds and robustness analysis for nuclear norm minimization.

Samet Oymak, Babak Hassibi

Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

Improved thresholds for rank minimization.

Samet Oymak, M. Amin Khajehnejad, Babak Hassibi

Proceedings of the IEEE International Conference on Acoustics, 2011

Weighted compressed sensing and rank minimization.

Samet Oymak, M. Amin Khajehnejad, Babak Hassibi

Proceedings of the IEEE International Conference on Acoustics, 2011

2010

New Null Space Results and Recovery Thresholds for Matrix Rank Minimization

Samet Oymak, Babak Hassibi

CoRR, 2010