b'Mert Pilanci'

research

∙ 09/01/2023

Randomized Polar Codes for Anytime Distributed Machine Learning

We present a novel distributed computing framework that is robust to slo...

0 Burak Bartan, et al. ∙

research

∙ 08/08/2023

Iterative Sketching for Secure Coded Regression

In this work, we propose methods for speeding up linear regression distr...

0 Neophytos Charalambides, et al. ∙

research

∙ 08/06/2023

Gradient Coding through Iterative Block Leverage Score Sampling

We generalize the leverage score sampling sketch for ℓ_2-subspace embedd...

0 Neophytos Charalambides, et al. ∙

research

∙ 05/31/2023

Optimal Sets and Solution Paths of ReLU Networks

We develop an analytical framework to characterize the set of optimal Re...

0 Aaron Mishkin, et al. ∙

research

∙ 03/06/2023

Globally Optimal Training of Neural Networks with Threshold Activation Functions

Threshold activation functions are highly preferable in neural networks ...

0 Tolga Ergen, et al. ∙

research

∙ 02/27/2023

Complex Clipping for Improved Generalization in Machine Learning

For many machine learning applications, a common input representation is...

0 Les Atlas, et al. ∙

research

∙ 01/09/2023

Federated Coded Matrix Inversion

Federated learning (FL) is a decentralized model for training data distr...

0 Neophytos Charalambides, et al. ∙

research

∙ 09/30/2022

Overparameterized ReLU Neural Networks Learn the Simplest Models: Neural Isometry and Exact Recovery

The practice of deep learning has shown that neural networks generalize ...

21 Yifei Wang, et al. ∙

research

∙ 07/18/2022

GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction

Unrolled neural networks have recently achieved state-of-the-art acceler...

9 Batu Ozturkler, et al. ∙

research

∙ 07/13/2022

Secure Linear MDS Coded Matrix Inversion

A cumbersome operation in many scientific fields, is inverting large ful...

0 Neophytos Charalambides, et al. ∙

research

∙ 05/17/2022

Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers

Vision transformers using self-attention or its proposed alternatives ha...

14 Arda Sahiner, et al. ∙

research

∙ 04/21/2022

Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction

Unrolled neural networks have enabled state-of-the-art reconstruction pe...

6 Beliz Gunel, et al. ∙

research

∙ 03/18/2022

Approximate Function Evaluation via Multi-Armed Bandits

We study the problem of estimating the value of a known smooth function ...

8 Tavor Z. Baharav, et al. ∙

research

∙ 03/18/2022

Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds

We consider distributed optimization methods for problems where forming ...

3 Burak Bartan, et al. ∙

research

∙ 02/23/2022

Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms

We consider the problem of quantizing a linear model learned from measur...

3 Rajarshi Saha, et al. ∙

research

∙ 02/02/2022

Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions

We develop fast algorithms and robust software for convex optimization o...

5 Aaron Mishkin, et al. ∙

research

∙ 01/21/2022

Orthonormal Sketches for Secure Coded Regression

In this work, we propose a method for speeding up linear regression dist...

0 Neophytos Charalambides, et al. ∙

research

∙ 01/21/2022

Using a Novel COVID-19 Calculator to Measure Positive U.S. Socio-Economic Impact of a COVID-19 Pre-Screening Solution (AI/ML)

The COVID-19 pandemic has been a scourge upon humanity, claiming the liv...

3 Richard Swartzbaugh, et al. ∙

research

∙ 01/05/2022

Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough

The Covid-19 pandemic has been a scourge upon humanity, claiming the liv...

8 Esin Darici, et al. ∙

research

∙ 10/18/2021

Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks

Despite several attempts, the fundamental mechanisms behind the success ...

20 Tolga Ergen, et al. ∙

research

∙ 10/13/2021

The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program

We study non-convex subgradient flows for training two-layer ReLU neural...

25 Yifei Wang, et al. ∙

research

∙ 10/13/2021

Parallel Deep Neural Networks Have Zero Duality Gap

Training deep neural networks is a well-known highly non-convex problem....

5 Yifei Wang, et al. ∙

research

∙ 10/11/2021

Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs

Understanding the fundamental mechanism behind the success of deep neura...

29 Tolga Ergen, et al. ∙

research

∙ 09/08/2021

Computational Polarization: An Information-theoretic Method for Resilient Computing

We introduce an error resilient distributed computing method based on an...

0 Mert Pilanci, et al. ∙

research

∙ 07/15/2021

Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update

In second-order optimization, a potential bottleneck can be computing th...

9 Michał Dereziński, et al. ∙

research

∙ 07/12/2021

Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions

Generative Adversarial Networks (GANs) are commonly used for modeling co...

13 Arda Sahiner, et al. ∙

research

∙ 05/15/2021

Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality

We propose a randomized algorithm with quadratic convergence rate for co...

6 Jonathan Lacotte, et al. ∙

research

∙ 05/04/2021

Training Quantized Neural Networks to Global Optimality via Semidefinite Programming

Neural networks (NNs) have been extremely successful across many tasks i...

9 Burak Bartan, et al. ∙

research

∙ 04/29/2021

Fast Convex Quadratic Optimization Solvers with Adaptive Sketching-based Preconditioners

We consider least-squares problems with quadratic regularization and pro...

7 Jonathan Lacotte, et al. ∙

research

∙ 03/13/2021

Distributed Learning and Democratic Embeddings: Polynomial-Time Source Coding Schemes Can Achieve Minimax Lower Bounds for Distributed Gradient Descent under Communication Cons

In this work, we consider the distributed optimization setting where inf...

11 Rajarshi Saha, et al. ∙

research

∙ 03/02/2021

Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization

Batch Normalization (BN) is a commonly used technique to accelerate and ...

16 Tolga Ergen, et al. ∙

research

∙ 01/07/2021

Neural Spectrahedra and Semidefinite Lifts: Global Convex Optimization of Polynomial Activation Neural Networks in Fully Polynomial-Time

The training of two-layer neural networks with nonlinear activation func...

11 Burak Bartan, et al. ∙

research

∙ 12/24/2020

Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms

We describe the convex semi-infinite dual of the two-layer vector-output...

16 Arda Sahiner, et al. ∙

research

∙ 12/13/2020

Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds

We propose novel randomized optimization methods for high-dimensional co...

9 Jonathan Lacotte, et al. ∙

research

∙ 12/09/2020

Convex Regularization Behind Neural Reconstruction

Neural networks have shown tremendous potential for reconstructing high-...

4 Arda Sahiner, et al. ∙

research

∙ 11/19/2020

Approximate Weighted CR Coded Matrix Multiplication

One of the most common, but at the same time expensive operations in lin...

0 Neophytos Charalambides, et al. ∙

research

∙ 07/02/2020

Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization

In distributed second order optimization, a standard strategy is to aver...

1 Michał Dereziński, et al. ∙

research

∙ 06/26/2020

Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time

We study training of Convolutional Neural Networks (CNNs) with ReLU acti...

19 Tolga Ergen, et al. ∙

research

∙ 06/15/2020

Lower Bounds and a Near-Optimal Shrinkage Estimator for Least Squares using Random Projections

In this work, we consider the deterministic optimization using random pr...

0 Srivatsan Sridhar, et al. ∙

research

∙ 06/10/2020

All Local Minima are Global for Two-Layer ReLU Neural Networks: The Hidden Convex Optimization Landscape

We are interested in two-layer ReLU neural networks from an optimization...

12 Jonathan Lacotte, et al. ∙

research

∙ 06/10/2020

Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization

We propose a new randomized algorithm for solving L2-regularized least-s...

3 Jonathan Lacotte, et al. ∙

research

∙ 05/21/2020

Global Multiclass Classification from Heterogeneous Local Models

Multiclass classification problems are most often solved by either train...

5 Surin Ahn, et al. ∙

research

∙ 03/05/2020

Straggler Robust Distributed Matrix Inverse Approximation

A cumbersome operation in numerical analysis and linear algebra, optimiz...

0 Neophytos Charalambides, et al. ∙

research

∙ 02/25/2020

Convex Geometry and Duality of Over-parameterized Neural Networks

We develop a convex analytic framework for ReLU neural networks which el...

3 Tolga Ergen, et al. ∙

research

∙ 02/25/2020

Separating the Effects of Batch Normalization on CNN Training Speed and Stability Using Classical Adaptive Filter Theory

Batch Normalization (BatchNorm) is commonly used in Convolutional Neural...

26 Elaina Chai, et al. ∙

research

∙ 02/24/2020

Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-Layer Networks

We develop exact representations of two layer neural networks with recti...

38 Mert Pilanci, et al. ∙

research

∙ 02/22/2020

Convex Duality of Deep Neural Networks

We study regularized deep neural networks and introduce an analytic fram...

23 Tolga Ergen, et al. ∙

research

∙ 02/21/2020

Optimal Randomized First-Order Methods for Least-Squares Problems

We provide an exact analysis of a class of randomized algorithms for sol...

0 Jonathan Lacotte, et al. ∙

research

∙ 02/16/2020

Distributed Averaging Methods for Randomized Second Order Optimization

We consider distributed optimization problems where forming the Hessian ...

0 Burak Bartan, et al. ∙

research

∙ 02/16/2020

Distributed Sketching Methods for Privacy Preserving Regression

In this work, we study distributed sketching methods for large scale reg...

0 Burak Bartan, et al. ∙

Mert Pilanci

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