Repairing multiple failures for scalar MDS codes. - Burak Bartan; Mary Wootters (Allerton, 2017)

Digital computation of fractional Fourier and linear canonical transforms and sparse image representation. - Aykut Koç; Haldun M. Özaktas; Burak Bartan; Erhan Gundogdu; Tolga Çukur (APSIPA, 2017)

Sparse representation of two- and three-dimensional images with fractional Fourier, Hartley, linear canonical, and Haar wavelet transforms. - Aykut Koç; Burak Bartan; Erhan Gundogdu; Tolga Çukur; Haldun M. Özaktas (Expert Syst. Appl., 2017)

Distributed Black-Box optimization via Error Correcting Codes. - Burak Bartan; Mert Pilanci (Allerton, 2019)

Straggler Resilient Serverless Computing Based on Polar Codes. - Burak Bartan; Mert Pilanci (Allerton, 2019)

Convex Relaxations of Convolutional Neural Nets. - Burak Bartan; Mert Pilanci (ICASSP, 2019)

Adaptive Hashing for Model Counting. - Jonathan Kuck; Tri Dao; Shenjia Zhao; Burak Bartan; Ashish Sabharwal; Stefano Ermon (UAI, 2019)

Polar Coded Distributed Matrix Multiplication. - Burak Bartan; Mert Pilanci (CoRR, 2019)

Repairing Multiple Failures for Scalar MDS Codes. - Jay Mardia; Burak Bartan; Mary Wootters (IEEE Trans. Inf. Theory, 2019)

Discrete Linear Canonical Transform Based on Hyperdifferential Operators. - Aykut Koç; Burak Bartan; Haldun M. Özaktas (IEEE Trans. Signal Process., 2019)

Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. - Michal Derezinski; Burak Bartan; Mert Pilanci; Michael W. Mahoney (NeurIPS, 2020)

Distributed Sketching Methods for Privacy Preserving Regression. - Burak Bartan; Mert Pilanci (CoRR, 2020)

Distributed Averaging Methods for Randomized Second Order Optimization. - Burak Bartan; Mert Pilanci (CoRR, 2020)

Convex Neural Autoregressive Models: Towards Tractable, Expressive, and Theoretically-Backed Models for Sequential Forecasting and Generation. - Vikul Gupta; Burak Bartan; Tolga Ergen; Mert Pilanci (ICASSP, 2021)

Training Quantized Neural Networks to Global Optimality via Semidefinite Programming. - Burak Bartan; Mert Pilanci (ICML, 2021)

Neural Spectrahedra and Semidefinite Lifts: Global Convex Optimization of Polynomial Activation Neural Networks in Fully Polynomial-Time. - Burak Bartan; Mert Pilanci (CoRR, 2021)

Discrete scaling based on operator theory. - Aykut Koç; Burak Bartan; Haldun M. Özaktas (Digit. Signal Process., 2021)

Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions. - Arda Sahiner; Tolga Ergen; Batu Ozturkler; Burak Bartan; John M. Pauly; Morteza Mardani; Mert Pilanci (ICLR, 2022)

Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time. - Burak Bartan; Mert Pilanci (ICML, 2022)

Convex Optimization of Deep Polynomial and ReLU Activation Neural Networks. - Burak Bartan; Mert Pilanci (ICASSP, 2023)

Neural DAG Scheduling via One-Shot Priority Sampling. - Wonseok Jeon; Mukul Gagrani; Burak Bartan; Weiliang Will Zeng; Harris Teague; Piero Zappi; Christopher Lott (ICLR, 2023)

Moccasin: Efficient Tensor Rematerialization for Neural Networks. - Burak Bartan; Haoming Li 002; Harris Teague; Christopher Lott; Bistra Dilkina (ICML, 2023)

Randomized Polar Codes for Anytime Distributed Machine Learning. - Burak Bartan; Mert Pilanci (IEEE J. Sel. Areas Inf. Theory, 2023)

Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration, and Lower Bounds. - Burak Bartan; Mert Pilanci (IEEE Trans. Inf. Theory, 2023)

Neural spectrahedra and semidefinite lifts: global convex optimization of degree-two polynomial activation neural networks in polynomial-time. - Burak Bartan; Mert Pilanci (Math. Program., 2025)