Martin Takáč

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Lehigh University

Tenure Track Assistant Professor in the Department of Industrial and Systems Engineering at Lehigh University, USA, Ph.D. (2014) degree in Mathematics from The University of Edinburgh, United Kingdom, Best Ph.D. Dissertation Award by the OR Society (2014), Leslie Fox Prize (2nd Prize; 2013) by the Institute for Mathematics and its Applications, and INFORMS Computing Society Best Student Paper Award (runner up; 2012), Java & Oracle developer at Plaut Slovensko, a.s. from 2007-2010.

Featured Co-authors

Peter Richtarik
126 publications
Michael W. Mahoney
112 publications
Alejandro Ribeiro
109 publications
Junzhou Huang
102 publications
Shuang Li
83 publications
Jie Liu
83 publications
Deanna Needell
60 publications
Yu Rong
59 publications
Jing Dong
48 publications
Ngai-Man Cheung
46 publications
Lam M. Nguyen
34 publications

∙ 07/17/2022

SP2: A Second Order Stochastic Polyak Method

Recently the "SP" (Stochastic Polyak step size) method has emerged as a ...

Shuang Li, et al. ∙

∙ 07/15/2022

Suppressing Poisoning Attacks on Federated Learning for Medical Imaging

Collaboration among multiple data-owning entities (e.g., hospitals) can ...

Naif Alkhunaizi, et al. ∙

∙ 06/09/2022

Learning to generalize Dispatching rules on the Job Shop Scheduling

This paper introduces a Reinforcement Learning approach to better genera...

Zangir Iklassov, et al. ∙

∙ 06/06/2022

Learning to Control under Time-Varying Environment

This paper investigates the problem of regret minimization in linear tim...

Yuzhen Han, et al. ∙

∙ 06/01/2022

Stochastic Gradient Methods with Preconditioned Updates

This work considers non-convex finite sum minimization. There are a numb...

Abdurakhmon Sadiev, et al. ∙

∙ 03/10/2022

Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input

For a given stable recurrent neural network (RNN) that is trained to per...

Guangyi Liu, et al. ∙

∙ 02/05/2022

Distributed Learning With Sparsified Gradient Differences

A very large number of communications are typically required to solve di...

Yicheng Chen, et al. ∙

∙ 11/26/2021

Random-reshuffled SARAH does not need a full gradient computations

The StochAstic Recursive grAdient algoritHm (SARAH) algorithm is a varia...

Aleksandr Beznosikov, et al. ∙

∙ 09/11/2021

Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information

We present a novel adaptive optimization algorithm for large-scale machi...

Majid Jahani, et al. ∙

∙ 07/06/2021

Improving Text-to-Image Synthesis Using Contrastive Learning

The goal of text-to-image synthesis is to generate a visually realistic ...

Hui Ye, et al. ∙

∙ 02/19/2021

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods

We present an adaptive stochastic variance reduced method with an implic...

Zheng Shi, et al. ∙

∙ 12/18/2020

Distributed Map Classification using Local Observations

We consider the problem of classifying a map using a team of communicati...

Guangyi Liu, et al. ∙

∙ 07/03/2020

DynNet: Physics-based neural architecture design for linear and nonlinear structural response modeling and prediction

Data-driven models for predicting dynamic responses of linear and nonlin...

Soheil Sadeghi Eshkevari, et al. ∙

∙ 06/22/2020

Constrained Combinatorial Optimization with Reinforcement Learning

This paper presents a framework to tackle constrained combinatorial opti...

Ruben Solozabal, et al. ∙

∙ 06/06/2020

SONIA: A Symmetric Blockwise Truncated Optimization Algorithm

This work presents a new algorithm for empirical risk minimization. The ...

Majid Jahani, et al. ∙

∙ 06/02/2020

Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations

Discovering the underlying behavior of complex systems is an important t...

Zheng Shi, et al. ∙

∙ 12/20/2019

Distributed Fixed Point Methods with Compressed Iterates

We propose basic and natural assumptions under which iterative optimizat...

Sélim Chraibi, et al. ∙

∙ 10/28/2019

FD-Net with Auxiliary Time Steps: Fast Prediction of PDEs using Hessian-Free Trust-Region Methods

Discovering the underlying physical behavior of complex systems is a cru...

Nur Sila Gulgec, et al. ∙

∙ 09/20/2019

A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning

We propose a planning and perception mechanism for a robot (agent), that...

Hossein K. Mousavi, et al. ∙

∙ 05/30/2019

Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1

In this paper, we present a scalable distributed implementation of the s...

Majid Jahani, et al. ∙

∙ 05/30/2019

Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework

Although reinforcement learning (RL) can provide reliable solutions in m...

MohammadReza Nazari, et al. ∙

∙ 05/13/2019

Multi-Agent Image Classification via Reinforcement Learning

We investigate a classification problem using multiple mobile agents tha...

Hossein K. Mousavi, et al. ∙

∙ 01/28/2019

Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample

We present two sampled quasi-Newton methods for deep learning: sampled L...

Albert S. Berahas, et al. ∙

∙ 01/26/2019

Distributed Learning with Compressed Gradient Differences

Training very large machine learning models requires a distributed compu...

Konstantin Mishchenko, et al. ∙

∙ 11/25/2018

Inexact SARAH Algorithm for Stochastic Optimization

We develop and analyze a variant of variance reducing stochastic gradien...

Lam M. Nguyen, et al. ∙

∙ 10/26/2018

Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy

In this paper, we propose a Distributed Accumulated Newton Conjugate gra...

Majid Jahani, et al. ∙

∙ 07/14/2018

On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches

This paper proposes a framework of L-BFGS based on the (approximate) sec...

Jie Liu, et al. ∙

∙ 03/28/2018

Active Metric Learning for Supervised Classification

Clustering and classification critically rely on distance metrics that p...

Krishnan Kumaran, et al. ∙

∙ 02/12/2018

Deep Reinforcement Learning for Solving the Vehicle Routing Problem

We present an end-to-end framework for solving Vehicle Routing Problem (...

MohammadReza Nazari, et al. ∙

∙ 02/11/2018

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

Stochastic gradient descent (SGD) is the optimization algorithm of choic...

Lam M. Nguyen, et al. ∙

∙ 08/20/2017

A Deep Q-Network for the Beer Game: A Reinforcement Learning algorithm to Solve Inventory Optimization Problems

The beer game is a widely used in-class game that is played in supply ch...

Afshin Oroojlooyjadid, et al. ∙

∙ 08/20/2017

A Deep Q-Network for the Beer Game with Partial Information

The beer game is a decentralized, multi-agent, cooperative problem that ...

Afshin Oroojlooyjadid, et al. ∙

∙ 07/26/2017

A Robust Multi-Batch L-BFGS Method for Machine Learning

This paper describes an implementation of the L-BFGS method designed to ...

Albert S. Berahas, et al. ∙

∙ 06/04/2017

Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory

We develop a family of reformulations of an arbitrary consistent linear ...

Peter Richtarik, et al. ∙

∙ 05/20/2017

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

In this paper, we study and analyze the mini-batch version of StochAstic...

Lam M. Nguyen, et al. ∙

∙ 03/01/2017

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SAR...

Lam M. Nguyen, et al. ∙

∙ 05/19/2016

A Multi-Batch L-BFGS Method for Machine Learning

The question of how to parallelize the stochastic gradient descent (SGD)...

Albert S. Berahas, et al. ∙

∙ 06/08/2015

Linear Convergence of the Randomized Feasible Descent Method Under the Weak Strong Convexity Assumption

In this paper we generalize the framework of the feasible descent method...

Chenxin Ma, et al. ∙

∙ 04/16/2015

Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

We propose mS2GD: a method incorporating a mini-batching scheme for impr...

Jakub Konečný, et al. ∙

∙ 10/17/2014

mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

We propose a mini-batching scheme for improving the theoretical complexi...

Jakub Konečný, et al. ∙

∙ 08/11/2014

Matrix Completion under Interval Uncertainty

Matrix completion under interval uncertainty can be cast as matrix compl...

Jakub Marecek, et al. ∙

∙ 11/04/2013

TOP-SPIN: TOPic discovery via Sparse Principal component INterference

We propose a novel topic discovery algorithm for unlabeled images based ...

Martin Takáč, et al. ∙

∙ 10/08/2013

Distributed Coordinate Descent Method for Learning with Big Data

In this paper we develop and analyze Hydra: HYbriD cooRdinAte descent me...

Peter Richtarik, et al. ∙

∙ 12/17/2012

Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes

Given a multivariate data set, sparse principal component analysis (SPCA...

Peter Richtarik, et al. ∙

∙ 12/04/2012

Parallel Coordinate Descent Methods for Big Data Optimization

In this work we show that randomized (block) coordinate descent methods ...

Peter Richtarik, et al. ∙

∙ 07/14/2011

Iteration Complexity of Randomized Block-Coordinate Descent Methods for Minimizing a Composite Function

In this paper we develop a randomized block-coordinate descent method fo...

Peter Richtarik, et al. ∙