Mert Pilanci
Orcid: 0000-0002-0870-9992
According to our database1,
Mert Pilanci
authored at least 107 papers
between 2009 and 2024.
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Bibliography
2024
Correction to: Sketching the Krylov subspace: faster computation of the entire ridge regularization path.
J. Supercomput., January, 2024
A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features.
CoRR, 2024
Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time.
CoRR, 2024
Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization.
CoRR, 2024
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers.
CoRR, 2024
2023
Sketching the Krylov subspace: faster computation of the entire ridge regularization path.
J. Supercomput., November, 2023
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration, and Lower Bounds.
IEEE Trans. Inf. Theory, June, 2023
IEEE J. Sel. Areas Inf. Theory, 2023
The Convex Landscape of Neural Networks: Characterizing Global Optima and Stationary Points via Lasso Models.
CoRR, 2023
Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI.
CoRR, 2023
Polynomial-Time Solutions for ReLU Network Training: A Complexity Classification via Max-Cut and Zonotopes.
CoRR, 2023
From Complexity to Clarity: Analytical Expressions of Deep Neural Network Weights via Clifford's Geometric Algebra and Convexity.
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Proceedings of the IEEE International Conference on Acoustics, 2023
Proceedings of the IEEE International Conference on Acoustics, 2023
2022
IEEE Trans. Inf. Theory, 2022
Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds.
IEEE Trans. Inf. Theory, 2022
A Data-Driven Waveform Adaptation Method for Mm-Wave Gait Classification at the Edge.
IEEE Signal Process. Lett., 2022
Efficient Randomized Subspace Embeddings for Distributed Optimization Under a Communication Budget.
IEEE J. Sel. Areas Inf. Theory, 2022
CoRR, 2022
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization.
CoRR, 2022
Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms.
CoRR, 2022
Using a Novel COVID-19 Calculator to Measure Positive U.S. Socio-Economic Impact of a COVID-19 Pre-Screening Solution (AI/ML).
CoRR, 2022
Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough.
CoRR, 2022
Proceedings of the 2022 5th International Conference on Signal Processing and Machine Learning, 2022
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022
Proceedings of the IEEE International Symposium on Information Theory, 2022
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers.
Proceedings of the International Conference on Machine Learning, 2022
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions.
Proceedings of the International Conference on Machine Learning, 2022
Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time.
Proceedings of the International Conference on Machine Learning, 2022
The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program.
Proceedings of the Tenth International Conference on Learning Representations, 2022
The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions.
Proceedings of the Tenth International Conference on Learning Representations, 2022
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions.
Proceedings of the Tenth International Conference on Learning Representations, 2022
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization.
Proceedings of the Tenth International Conference on Learning Representations, 2022
Proceedings of the 58th Annual Allerton Conference on Communication, 2022
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
J. Mach. Learn. Res., 2021
Fast Convex Quadratic Optimization Solvers with Adaptive Sketching-based Preconditioners.
CoRR, 2021
Distributed Learning and Democratic Embeddings: Polynomial-Time Source Coding Schemes Can Achieve Minimax Lower Bounds for Distributed Gradient Descent under Communication Constraints.
CoRR, 2021
Neural Spectrahedra and Semidefinite Lifts: Global Convex Optimization of Polynomial Activation Neural Networks in Fully Polynomial-Time.
CoRR, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality.
Proceedings of the 38th International Conference on Machine Learning, 2021
Proceedings of the 38th International Conference on Machine Learning, 2021
Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs.
Proceedings of the 38th International Conference on Machine Learning, 2021
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming.
Proceedings of the 38th International Conference on Machine Learning, 2021
Proceedings of the 9th International Conference on Learning Representations, 2021
Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms.
Proceedings of the 9th International Conference on Learning Representations, 2021
Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time.
Proceedings of the 9th International Conference on Learning Representations, 2021
Convex Neural Autoregressive Models: Towards Tractable, Expressive, and Theoretically-Backed Models for Sequential Forecasting and Generation.
Proceedings of the IEEE International Conference on Acoustics, 2021
Proceedings of the IEEE International Conference on Acoustics, 2021
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
2020
Lower Bounds and a Near-Optimal Shrinkage Estimator for Least Squares Using Random Projections.
IEEE J. Sel. Areas Inf. Theory, 2020
Global Multiclass Classification and Dataset Construction via Heterogeneous Local Experts.
IEEE J. Sel. Areas Inf. Theory, 2020
Training Convolutional ReLU Neural Networks in Polynomial Time: Exact Convex Optimization Formulations.
CoRR, 2020
All Local Minima are Global for Two-Layer ReLU Neural Networks: The Hidden Convex Optimization Landscape.
CoRR, 2020
Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods.
CoRR, 2020
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks.
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
Separating the Effects of Batch Normalization on CNN Training Speed and Stability Using Classical Adaptive Filter Theory.
Proceedings of the 54th Asilomar Conference on Signals, Systems, and Computers, 2020
2019
Regularized Momentum Iterative Hessian Sketch for Large Scale Linear System of Equations.
CoRR, 2019
Proceedings of the 27th Signal Processing and Communications Applications Conference, 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the IEEE International Conference on Acoustics, 2019
Proceedings of the IEEE International Conference on Acoustics, 2019
Proceedings of the 57th Annual Allerton Conference on Communication, 2019
Proceedings of the 57th Annual Allerton Conference on Communication, 2019
Proceedings of the 57th Annual Allerton Conference on Communication, 2019
2017
Newton Sketch: A Near Linear-Time Optimization Algorithm with Linear-Quadratic Convergence.
SIAM J. Optim., 2017
2016
PhD thesis, 2016
Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares.
J. Mach. Learn. Res., 2016
2015
IEEE Trans. Inf. Theory, 2015
CoRR, 2015
Newton Sketch: A Linear-time Optimization Algorithm with Linear-Quadratic Convergence.
CoRR, 2015
2012
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012
Proceedings of the 2012 IEEE International Conference on Acoustics, 2012
2011
Proceedings of the IEEE International Conference on Acoustics, 2011
2010
IEEE Trans. Signal Process., 2010
2009
Proceedings of the IEEE International Conference on Acoustics, 2009