← Authors

Mert Pilanci

44 papers · 2012–2025 · 5 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (5) πŸƒ Academic Marathon (13) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (13) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (14) 🀝 Dynamic Duo (15) πŸ‘‘ Triple Crown 🧬 Topic Evolution πŸ”¬ Deep Specialist (23) πŸ† Keyword Champion (3) ⚑ Prolific Year (9) πŸ—ƒοΈ Keyword Collector (141) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (7) πŸ’Ž Century Club (44)

Conferences

ICML (15) NIPS (13) ICLR (12) AISTATS (2) JMLR (2)

Top co-authors

Tolga Ergen (15) Jonathan Lacotte (8) Arda Sahiner (7) Fangzhao Zhang (5) Morteza Mardani (5) John M. Pauly (5) Batu Ozturkler (5) Yifei Wang (4) Burak Bartan (4) Aaron Mishkin (3)

Keywords

convex optimization (15) relu network (6) neural network (5) spline interpolation (3) adaptive sampling (3) dimensionality reduction (3) second-order optimization (3) randomized sketching (3) convergence rate (3) randomized algorithm (3) convex duality (2) effective dimension (2) representation learning (2) newton method (2) iterative hessian sketch (2) deep neural network (2) weight decay (2) matrix sketching (2) distributed optimization (2) matrix approximation (2)

Papers

Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing ICLR 2025
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes ICML 2025
Geometric Algebra Planes: Convex Implicit Neural Volumes ICML 2025
Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality ICLR 2025
Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time ICML 2024
Compressing Large Language Models using Low Rank and Low Precision Decomposition NIPS 2024
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks NIPS 2024
Adaptive Sampling for Efficient Softmax Approximation NIPS 2024
Spectral Adapter: Fine-Tuning in Spectral Space NIPS 2024
Scaling Convex Neural Networks with Burer-Monteiro Factorization ICLR 2024
Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models ICML 2024
Parallel Deep Neural Networks Have Zero Duality Gap ICLR 2023
Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs NIPS 2023
Matrix Compression via Randomized Low Rank and Low Precision Factorization NIPS 2023
Optimal Sets and Solution Paths of ReLU Networks ICML 2023
Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks NIPS 2023
Optimal Shrinkage for Distributed Second-Order Optimization ICML 2023
Globally Optimal Training of Neural Networks with Threshold Activation Functions ICLR 2023
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization ICLR 2022
The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions ICLR 2022
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers ICML 2022
Approximate Function Evaluation via Multi-Armed Bandits AISTATS 2022
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions ICML 2022
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions ICLR 2022
The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program ICLR 2022
Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time ICML 2022
Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time ICLR 2021
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update NIPS 2021
Convex Regularization behind Neural Reconstruction ICLR 2021
Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms ICLR 2021
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming ICML 2021
Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs ICML 2021
Revealing the Structure of Deep Neural Networks via Convex Duality ICML 2021
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality ICML 2021
Convex Geometry and Duality of Over-parameterized Neural Networks JMLR 2021
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks ICML 2020
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform NIPS 2020
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization NIPS 2020
Optimal Randomized First-Order Methods for Least-Squares Problems ICML 2020
Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models AISTATS 2020
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization NIPS 2020
High-Dimensional Optimization in Adaptive Random Subspaces NIPS 2019
Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares JMLR 2016
Recovery of Sparse Probability Measures via Convex Programming NIPS 2012