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2020 – today
- 2024
[c71]Karthik Elamvazhuthi, Xuechen Zhang, Matthew Jacobs, Samet Oymak, Fabio Pasqualetti:
A Score-Based Deterministic Diffusion Algorithm with Smooth Scores for General Distributions. AAAI 2024: 11866-11873- [c70]Xuechen Zhang, Mingchen Li, Jiasi Chen, Christos Thrampoulidis, Samet Oymak:
Class-Attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective. AAAI 2024: 16890-16898 - [c69]Yingcong Li, Yixiao Huang
, Muhammed Emrullah Ildiz, Ankit Singh Rawat, Samet Oymak:
Mechanics of Next Token Prediction with Self-Attention. AISTATS 2024: 685-693 - [c68]Muhammed Emrullah Ildiz, Zhe Zhao, Samet Oymak:
Understanding Inverse Scaling and Emergence in Multitask Representation Learning. AISTATS 2024: 4726-4734 - [c67]Muhammed Emrullah Ildiz, Yixiao Huang, Yingcong Li, Ankit Singh Rawat, Samet Oymak:
From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers. ICML 2024 - [c66]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. ICML 2024 - [c65]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: 2080-2089 - [i79]Sk Miraj Ahmed, Fahim Faisal Niloy, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury:
MeTA: Multi-source Test Time Adaptation. CoRR abs/2401.02561 (2024) - [i78]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 abs/2401.04130 (2024) - [i77]Xuechen Zhang, Mingchen Li, Jiasi Chen, Christos Thrampoulidis, Samet Oymak:
Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective. CoRR abs/2401.14343 (2024) - [i76]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 abs/2402.04248 (2024) - [i75]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 abs/2402.08769 (2024) - [i74]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 abs/2402.13512 (2024) - [i73]Yingcong Li, Yixiao Huang, Muhammed Emrullah Ildiz, Ankit Singh Rawat, Samet Oymak:
Mechanics of Next Token Prediction with Self-Attention. CoRR abs/2403.08081 (2024) - [i72]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 abs/2404.13082 (2024) - [i71]Mingchen Li, Xuechen Zhang, Yixiao Huang, Samet Oymak:
On the Power of Convolution Augmented Transformer. CoRR abs/2407.05591 (2024) - [i70]Yingcong Li, Ankit Singh Rawat, Samet Oymak:
Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond. CoRR abs/2407.10005 (2024) - 2023
- [j12]Zhe Du, Haldun Balim, Samet Oymak, Necmiye Ozay:
Can Transformers Learn Optimal Filtering for Unknown Systems? IEEE Control. Syst. Lett. 7: 3525-3530 (2023) - [c64]Yingcong Li, Samet Oymak:
Provable Pathways: Learning Multiple Tasks over Multiple Paths. AAAI 2023: 8701-8710 - [c63]Yuzhen Qin, Yingcong Li, Fabio Pasqualetti, Maryam Fazel, Samet Oymak:
Stochastic Contextual Bandits with Long Horizon Rewards. AAAI 2023: 9525-9533 - [c62]Yingcong Li, Samet Oymak:
On The Fairness of Multitask Representation Learning. ICASSP 2023: 1-5 - [c61]Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, Samet Oymak:
Transformers as Algorithms: Generalization and Stability in In-context Learning. ICML 2023: 19565-19594 - [c60]Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, Christos Thrampoulidis:
On the Role of Attention in Prompt-tuning. ICML 2023: 26724-26768 - [c59]Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti:
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs. L4DC 2023: 1-11 - [c58]Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris Papailiopoulos, Samet Oymak:
Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning. NeurIPS 2023 - [c57]Davoud Ataee Tarzanagh, Yingcong Li, Xuechen Zhang, Samet Oymak:
Max-Margin Token Selection in Attention Mechanism. NeurIPS 2023 - [c56]Xuechen Zhang, Zheng Li, Samet Oymak, Jiasi Chen:
Text-to-3D Generative AI on Mobile Devices: Measurements and Optimizations. EMS@SIGCOMM 2023: 8-14 - [i69]Yingcong Li, Muhammed Emrullah Ildiz, Dimitris S. Papailiopoulos, Samet Oymak:
Transformers as Algorithms: Generalization and Implicit Model Selection in In-context Learning. CoRR abs/2301.07067 (2023) - [i68]Yuzhen Qin, Yingcong Li, Fabio Pasqualetti, Maryam Fazel, Samet Oymak:
Stochastic Contextual Bandits with Long Horizon Rewards. CoRR abs/2302.00814 (2023) - [i67]Yingcong Li, Samet Oymak:
Provable Pathways: Learning Multiple Tasks over Multiple Paths. CoRR abs/2303.04338 (2023) - [i66]Karthik Elamvazhuthi, Xuechen Zhang, Samet Oymak, Fabio Pasqualetti:
Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs. CoRR abs/2305.08849 (2023) - [i65]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 abs/2305.18869 (2023) - [i64]Davoud Ataee Tarzanagh, Mingchen Li, Pranay Sharma, Samet Oymak:
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation. CoRR abs/2306.01648 (2023) - [i63]Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, Christos Thrampoulidis:
On the Role of Attention in Prompt-tuning. CoRR abs/2306.03435 (2023) - [i62]Davoud Ataee Tarzanagh, Yingcong Li, Xuechen Zhang, Samet Oymak:
Max-Margin Token Selection in Attention Mechanism. CoRR abs/2306.13596 (2023) - [i61]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 abs/2307.04905 (2023) - [i60]Haldun Balim, Zhe Du, Samet Oymak, Necmiye Ozay:
Can Transformers Learn Optimal Filtering for Unknown Systems? CoRR abs/2308.08536 (2023) - [i59]Davoud Ataee Tarzanagh, Yingcong Li, Christos Thrampoulidis, Samet Oymak:
Transformers as Support Vector Machines. CoRR abs/2308.16898 (2023) - [i58]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 abs/2311.04991 (2023) - [i57]Karthik Elamvazhuthi, Samet Oymak, Fabio Pasqualetti:
Noise in the reverse process improves the approximation capabilities of diffusion models. CoRR abs/2312.07851 (2023) - 2022
- [j11]Yahya Sattar, Samet Oymak:
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems. J. Mach. Learn. Res. 23: 140:1-140:49 (2022) - [j10]Samet Oymak, Necmiye Ozay:
Revisiting Ho-Kalman-Based System Identification: Robustness and Finite-Sample Analysis. IEEE Trans. Autom. Control. 67(4): 1914-1928 (2022) - [c55]Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti:
Representation Learning for Context-Dependent Decision-Making. ACC 2022: 2130-2135 - [c54]Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh, Samet Oymak, Laura Balzano, Necmiye Ozay:
Certainty Equivalent Quadratic Control for Markov Jump Systems. ACC 2022: 2871-2878 - [c53]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: 4901-4908 - [c52]Yahya Sattar, Samet Oymak, Necmiye Ozay:
Finite Sample Identification of Bilinear Dynamical Systems. CDC 2022: 6705-6711 - [c51]Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak:
FedNest: Federated Bilevel, Minimax, and Compositional Optimization. ICML 2022: 21146-21179 - [i56]Mingchen Li, Xuechen Zhang, Christos Thrampoulidis, Jiasi Chen, Samet Oymak:
AutoBalance: Optimized Loss Functions for Imbalanced Data. CoRR abs/2201.01212 (2022) - [i55]Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti:
Non-Stationary Representation Learning in Sequential Linear Bandits. CoRR abs/2201.04805 (2022) - [i54]Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk, Samet Oymak, Maryam Fazel:
Towards Sample-efficient Overparameterized Meta-learning. CoRR abs/2201.06142 (2022) - [i53]Yingcong Li, Mingchen Li, M. Salman Asif, Samet Oymak:
Provable and Efficient Continual Representation Learning. CoRR abs/2203.02026 (2022) - [i52]Yue Sun, Samet Oymak, Maryam Fazel:
System Identification via Nuclear Norm Regularization. CoRR abs/2203.16673 (2022) - [i51]Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak:
FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization. CoRR abs/2205.02215 (2022) - [i50]Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, Fabio Pasqualetti:
Representation Learning for Context-Dependent Decision-Making. CoRR abs/2205.05820 (2022) - [i49]Yahya Sattar, Samet Oymak, Necmiye Ozay:
Finite Sample Identification of Bilinear Dynamical Systems. CoRR abs/2208.13915 (2022) - 2021
- [j9]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): 419-439 (2021) - [j8]Samet Oymak:
Provable Super-Convergence With a Large Cyclical Learning Rate. IEEE Signal Process. Lett. 28: 1645-1649 (2021) - [c50]Xiangyu Chang, Yingcong Li, Samet Oymak, Christos Thrampoulidis:
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks. AAAI 2021: 6974-6983 - [c49]Samet Oymak, Talha Cihad Gulcu:
A Theoretical Characterization of Semi-supervised Learning with Self-training for Gaussian Mixture Models. AISTATS 2021: 3601-3609 - [c48]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: 2775-2784 - [c47]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: 10103-10112 - [c46]Mohammad Reza Shahneh, Samet Oymak, Amr Magdy:
A-GWR: Fast and Accurate Geospatial Inference via Augmented Geographically Weighted Regression. SIGSPATIAL/GIS 2021: 564-575 - [c45]Yao-Chun Chan, Mingchen Li, Samet Oymak:
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat its Cake? ICASSP 2021: 3455-3459 - [c44]Halil Ibrahim Gulluk, Yue Sun, Samet Oymak, Maryam Fazel:
Sample Efficient Subspace-Based Representations for Nonlinear Meta-Learning. ICASSP 2021: 3685-3689 - [c43]Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi:
Generalization Guarantees for Neural Architecture Search with Train-Validation Split. ICML 2021: 8291-8301 - [c42]Mingchen Li, Xuechen Zhang, Christos Thrampoulidis, Jiasi Chen, Samet Oymak:
AutoBalance: Optimized Loss Functions for Imbalanced Data. NeurIPS 2021: 3163-3177 - [c41]Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, Christos Thrampoulidis:
Label-Imbalanced and Group-Sensitive Classification under Overparameterization. NeurIPS 2021: 18970-18983 - [c40]Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk, Samet Oymak, Maryam Fazel:
Towards Sample-efficient Overparameterized Meta-learning. NeurIPS 2021: 28156-28168 - [c39]Nhat X. T. Le, A. B. Siddique, Fuad T. Jamour, Samet Oymak, Vagelis Hristidis:
Predictable and Adaptive Goal-oriented Dialog Policy Generation. ICSC 2021: 40-47 - [i48]Halil Ibrahim Gulluk, Yue Sun, Samet Oymak, Maryam Fazel:
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning. CoRR abs/2102.07206 (2021) - [i47]Samet Oymak:
Super-Convergence with an Unstable Learning Rate. CoRR abs/2102.10734 (2021) - [i46]Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak, Christos Thrampoulidis:
Label-Imbalanced and Group-Sensitive Classification under Overparameterization. CoRR abs/2103.01550 (2021) - [i45]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 abs/2104.01845 (2021) - [i44]Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi:
Generalization Guarantees for Neural Architecture Search with Train-Validation Split. CoRR abs/2104.14132 (2021) - [i43]Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh, Laura Balzano, Samet Oymak, Necmiye Ozay:
Certainty Equivalent Quadratic Control for Markov Jump Systems. CoRR abs/2105.12358 (2021) - [i42]Xuechen Zhang, Samet Oymak, Jiasi Chen:
Post-hoc Models for Performance Estimation of Machine Learning Inference. CoRR abs/2110.02459 (2021) - [i41]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 abs/2111.07018 (2021) - 2020
- [j7]Samet Oymak, Mahdi Soltanolkotabi:
Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks. IEEE J. Sel. Areas Inf. Theory 1(1): 84-105 (2020) - [j6]Yahya Sattar, Samet Oymak:
Quickly Finding the Best Linear Model in High Dimensions via Projected Gradient Descent. IEEE Trans. Signal Process. 68: 818-829 (2020) - [c38]Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak:
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks. AISTATS 2020: 4313-4324 - [c37]Ahmet Demirkaya, Jiasi Chen, Samet Oymak:
Exploring the Role of Loss Functions in Multiclass Classification. CISS 2020: 1-5 - [c36]Hisham Alhulayyil, Kittipat Apicharttrisorn, Jiasi Chen, Karthikeyan Sundaresan, Samet Oymak, Srikanth V. Krishnamurthy:
WOLT: Auto-Configuration of Integrated Enterprise PLC-WiFi Networks. ICDCS 2020: 563-573 - [c35]A. B. Siddique, Samet Oymak, Vagelis Hristidis:
Unsupervised Paraphrasing via Deep Reinforcement Learning. KDD 2020: 1800-1809 - [c34]Yue Sun, Samet Oymak, Maryam Fazel:
Finite Sample System Identification: Optimal Rates and the Role of Regularization. L4DC 2020: 16-25 - [c33]Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi:
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View. NeurIPS 2020 - [i40]Yahya Sattar, Samet Oymak:
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems. CoRR abs/2002.08538 (2020) - [i39]Yuan Zhao, Jiasi Chen, Samet Oymak:
On the Role of Dataset Quality and Heterogeneity in Model Confidence. CoRR abs/2002.09831 (2020) - [i38]Mingchen Li, Yahya Sattar, Christos Thrampoulidis, Samet Oymak:
Exploring Weight Importance and Hessian Bias in Model Pruning. CoRR abs/2006.10903 (2020) - [i37]Samet Oymak, Talha Cihad Gulcu:
Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training. CoRR abs/2006.11006 (2020) - [i36]A. B. Siddique, Samet Oymak, Vagelis Hristidis:
Unsupervised Paraphrasing via Deep Reinforcement Learning. CoRR abs/2007.02244 (2020) - [i35]Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi:
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View. CoRR abs/2011.07729 (2020) - [i34]Yao-Chun Chan, Mingchen Li, Samet Oymak:
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake? CoRR abs/2011.08121 (2020) - [i33]Xiangyu Chang, Yingcong Li, Samet Oymak, Christos Thrampoulidis:
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks. CoRR abs/2012.08749 (2020)
2010 – 2019
- 2019
- [c32]Samet Oymak, Zalan Fabian, Mingchen Li, Mahdi Soltanolkotabi:
Generalization, Adaptation and Low-Rank Representation in Neural Networks. ACSSC 2019: 581-585 - [c31]Samet Oymak, Necmiye Ozay:
Non-asymptotic Identification of LTI Systems from a Single Trajectory. ACC 2019: 5655-5661 - [c30]Yahya Sattar, Samet Oymak:
A Simple Framework for Learning Stabilizable Systems. CAMSAP 2019: 116-120 - [c29]Samet Oymak:
Stochastic Gradient Descent Learns State Equations with Nonlinear Activations. COLT 2019: 2551-2579 - [c28]Samet Oymak, M. Salman Asif:
Exactly Decoding a Vector through Relu Activation. ICASSP 2019: 3607-3611 - [c27]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: 936-945 - [c26]Samet Oymak, Mahdi Soltanolkotabi:
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? ICML 2019: 4951-4960 - [c25]Samet Oymak, Mehrdad Mahdavi, Jiasi Chen:
Learning Feature Nonlinearities with Regularized Binned Regression. ISIT 2019: 1452-1456 - [i32]Samet Oymak, Mahdi Soltanolkotabi:
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks. CoRR abs/1902.04674 (2019) - [i31]Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak:
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks. CoRR abs/1903.11680 (2019) - [i30]Samet Oymak, Zalan Fabian, Mingchen Li, Mahdi Soltanolkotabi:
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian. CoRR abs/1906.05392 (2019) - [i29]Yahya Sattar, Samet Oymak:
Quickly Finding the Best Linear Model in High Dimensions. CoRR abs/1907.01728 (2019) - 2018
- [j5]Samet Oymak, Benjamin Recht, Mahdi Soltanolkotabi:
Sharp Time-Data Tradeoffs for Linear Inverse Problems. IEEE Trans. Inf. Theory 64(6): 4129-4158 (2018) - [c24]Samet Oymak:
Learning Compact Neural Networks with Regularization. ICML 2018: 3963-3972 - [i28]Samet Oymak:
Learning Compact Neural Networks with Regularization. CoRR abs/1802.01223 (2018) - [i27]Samet Oymak, Mahdi Soltanolkotabi:
End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition. CoRR abs/1805.06523 (2018) - [i26]Amir Asiaee T., Samet Oymak, Kevin R. Coombes, Arindam Banerjee:
High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient. CoRR abs/1806.04047 (2018) - [i25]Samet Oymak, Necmiye Ozay:
Non-asymptotic Identification of LTI Systems from a Single Trajectory. CoRR abs/1806.05722 (2018) - [i24]Samet Oymak:
Stochastic Gradient Descent Learns State Equations with Nonlinear Activations. CoRR abs/1809.03019 (2018) - [i23]Samet Oymak, Mahdi Soltanolkotabi:
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? CoRR abs/1812.10004 (2018) - 2017
- [j4]Samet Oymak, Mahdi Soltanolkotabi:
Fast and Reliable Parameter Estimation from Nonlinear Observations. SIAM J. Optim. 27(4): 2276-2300 (2017) - [j3]Kishore Jaganathan, Samet Oymak, Babak Hassibi:
Sparse Phase Retrieval: Uniqueness Guarantees and Recovery Algorithms. IEEE Trans. Signal Process. 65(9): 2402-2410 (2017) - [c23]Samet Oymak, Christos Thrampoulidis, Babak Hassibi:
Near-optimal sample complexity bounds for circulant binary embedding. ICASSP 2017: 6359-6363 - [i22]Samet Oymak, Mehrdad Mahdavi, Jiasi Chen:
Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression. CoRR abs/1705.07256 (2017) - 2016
- [j2]Samet Oymak, Babak Hassibi:
Sharp MSE Bounds for Proximal Denoising. Found. Comput. Math. 16(4): 965-1029 (2016) - [i21]Samet Oymak:
Near-Optimal Sample Complexity Bounds for Circulant Binary Embedding. CoRR abs/1603.03178 (2016) - [i20]Samet Oymak, Mahdi Soltanolkotabi:
Fast and Reliable Parameter Estimation from Nonlinear Observations. CoRR abs/1610.07108 (2016) - 2015
- [j1]Samet Oymak, Amin Jalali, Maryam Fazel, Yonina C. Eldar, Babak Hassibi:
Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices. IEEE Trans. Inf. Theory 61(5): 2886-2908 (2015) - [c22]Christos Thrampoulidis, Samet Oymak, Babak Hassibi:
Regularized Linear Regression: A Precise Analysis of the Estimation Error. COLT 2015: 1683-1709 - [c21]Samet Oymak, Babak Hassibi:
The proportional mean decomposition: A bridge between the Gaussian and bernoulli ensembles. ICASSP 2015: 3322-3326 - [c20]Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Parallel Correlation Clustering on Big Graphs. NIPS 2015: 82-90 - [i19]Samet Oymak, Benjamin Recht, Mahdi Soltanolkotabi:
Isometric sketching of any set via the Restricted Isometry Property. CoRR abs/1506.03521 (2015) - [i18]Samet Oymak, Benjamin Recht, Mahdi Soltanolkotabi:
Sharp Time-Data Tradeoffs for Linear Inverse Problems. CoRR abs/1507.04793 (2015) - [i17]Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Parallel Correlation Clustering on Big Graphs. CoRR abs/1507.05086 (2015) - [i16]Samet Oymak, Joel A. Tropp:
Universality laws for randomized dimension reduction, with applications. CoRR abs/1511.09433 (2015) - [i15]Samet Oymak, Ben Recht:
Near-Optimal Bounds for Binary Embeddings of Arbitrary Sets. CoRR abs/1512.04433 (2015) - 2014
- [c19]Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi:
Sharp performance bounds for graph clustering via convex optimization. ICASSP 2014: 8297-8301 - [c18]Christos Thrampoulidis, Samet Oymak, Babak Hassibi:
Simple error bounds for regularized noisy linear inverse problems. ISIT 2014: 3007-3011 - [c17]Samet Oymak, Babak Hassibi:
A case for orthogonal measurements in linear inverse problems. ISIT 2014: 3175-3179 - [c16]Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi:
Graph Clustering With Missing Data: Convex Algorithms and Analysis. NIPS 2014: 2996-3004 - [i14]Christos Thrampoulidis, Samet Oymak, Babak Hassibi:
Simple Error Bounds for Regularized Noisy Linear Inverse Problems. CoRR abs/1401.6578 (2014) - [i13]Christos Thrampoulidis, Samet Oymak, Babak Hassibi:
A Tight Version of the Gaussian min-max theorem in the Presence of Convexity. CoRR abs/1408.4837 (2014) - 2013
- [c15]Samet Oymak, Christos Thrampoulidis, Babak Hassibi:
The squared-error of generalized LASSO: A precise analysis. Allerton 2013: 1002-1009 - [c14]Samet Oymak, Amin Jalali, Maryam Fazel, Babak Hassibi:
Noisy estimation of simultaneously structured models: Limitations of convex relaxation. CDC 2013: 6019-6024 - [c13]Kishore Jaganathan, Samet Oymak, Babak Hassibi:
Sparse phase retrieval: Convex algorithms and limitations. ISIT 2013: 1022-1026 - [i12]Kishore Jaganathan, Samet Oymak, Babak Hassibi:
Sparse Phase Retrieval: Convex Algorithms and Limitations. CoRR abs/1303.4128 (2013) - [i11]Samet Oymak, Babak Hassibi:
Asymptotically Exact Denoising in Relation to Compressed Sensing. CoRR abs/1305.2714 (2013) - [i10]Samet Oymak, Christos Thrampoulidis, Babak Hassibi:
The Squared-Error of Generalized LASSO: A Precise Analysis. CoRR abs/1311.0830 (2013) - [i9]Kishore Jaganathan, Samet Oymak, Babak Hassibi:
Sparse Phase Retrieval: Uniqueness Guarantees and Recovery Algorithms. CoRR abs/1311.2745 (2013) - [i8]Samet Oymak, Christos Thrampoulidis, Babak Hassibi:
Simple Bounds for Noisy Linear Inverse Problems with Exact Side Information. CoRR abs/1312.0641 (2013) - 2012
- [c12]Kishore Jaganathan, Samet Oymak, Babak Hassibi:
On robust phase retrieval for sparse signals. Allerton Conference 2012: 794-799 - [c11]Samet Oymak, Babak Hassibi:
On a relation between the minimax risk and the phase transitions of compressed recovery. Allerton Conference 2012: 1018-1025 - [c10]Kishore Jaganathan, Samet Oymak, Babak Hassibi:
Phase retrieval for sparse signals using rank minimization. ICASSP 2012: 3449-3452 - [c9]Anilesh K. Krishnaswamy, Samet Oymak, Babak Hassibi:
A simpler approach to weighted ℓ1 minimization. ICASSP 2012: 3621-3624 - [c8]Cheuk Ting Li, Samet Oymak, Babak Hassibi:
Deterministic phase guarantees for robust recovery in incoherent dictionaries. ICASSP 2012: 3817-3820 - [c7]Kishore Jaganathan, Samet Oymak, Babak Hassibi:
Recovery of sparse 1-D signals from the magnitudes of their Fourier transform. ISIT 2012: 1473-1477 - [c6]Samet Oymak, M. Amin Khajehnejad, Babak Hassibi:
Recovery threshold for optimal weight ℓ1 minimization. ISIT 2012: 2032-2036 - [i7]Samet Oymak, Babak Hassibi:
Recovering Jointly Sparse Signals via Joint Basis Pursuit. CoRR abs/1202.3531 (2012) - [i6]Kishore Jaganathan, Samet Oymak, Babak Hassibi:
Recovery of Sparse 1-D Signals from the Magnitudes of their Fourier Transform. CoRR abs/1206.1405 (2012) - [i5]Samet Oymak, Amin Jalali, Maryam Fazel, Yonina C. Eldar, Babak Hassibi:
Simultaneously Structured Models with Application to Sparse and Low-rank Matrices. CoRR abs/1212.3753 (2012) - 2011
- [c5]Samet Oymak, M. Amin Khajehnejad, Babak Hassibi:
Weighted compressed sensing and rank minimization. ICASSP 2011: 3736-3739 - [c4]Samet Oymak, M. Amin Khajehnejad, Babak Hassibi:
Improved thresholds for rank minimization. ICASSP 2011: 5988-5991 - [c3]Samet Oymak, M. Amin Khajehnejad, Babak Hassibi:
Subspace expanders and matrix rank minimization. ISIT 2011: 2308-2312 - [c2]Samet Oymak, Karthik Mohan, Maryam Fazel, Babak Hassibi:
A simplified approach to recovery conditions for low rank matrices. ISIT 2011: 2318-2322 - [c1]Samet Oymak, Babak Hassibi:
Tight recovery thresholds and robustness analysis for nuclear norm minimization. ISIT 2011: 2323-2327 - [i4]M. Amin Khajehnejad, Samet Oymak, Babak Hassibi:
Subspace Expanders and Matrix Rank Minimization. CoRR abs/1102.3947 (2011) - [i3]Samet Oymak, Karthik Mohan, Maryam Fazel, Babak Hassibi:
A Simplified Approach to Recovery Conditions for Low Rank Matrices. CoRR abs/1103.1178 (2011) - [i2]Samet Oymak, Babak Hassibi:
Finding Dense Clusters via "Low Rank + Sparse" Decomposition. CoRR abs/1104.5186 (2011) - 2010
- [i1]Samet Oymak, Babak Hassibi:
New Null Space Results and Recovery Thresholds for Matrix Rank Minimization. CoRR abs/1011.6326 (2010)
Coauthor Index
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