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
84 publications
Alejandro Ribeiro
81 publications
Junzhou Huang
67 publications
Jie Liu
54 publications
Ngai-Man Cheung
39 publications
Yu Rong
29 publications
Aryan Mokhtari
27 publications
Guangyi Liu
25 publications
Lam M. Nguyen
24 publications
Jakub Marecek
22 publications
Katya Scheinberg
20 publications

Reinforcement Learning based Multi-Robot Classification via Scalable Communication Structure

In the multi-robot collaboration domain, training with Reinforcement Lea...
12/18/2020 ∙ by Guangyi Liu, et al. ∙ 26 ∙ share

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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...
07/03/2020 ∙ by Soheil Sadeghi Eshkevari, et al. ∙ 0 ∙ share

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Constrained Combinatorial Optimization with Reinforcement Learning

This paper presents a framework to tackle constrained combinatorial opti...
06/22/2020 ∙ by Ruben Solozabal, et al. ∙ 0 ∙ share

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SONIA: A Symmetric Blockwise Truncated Optimization Algorithm

This work presents a new algorithm for empirical risk minimization. The ...
06/06/2020 ∙ by Majid Jahani, et al. ∙ 0 ∙ share

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Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations

Discovering the underlying behavior of complex systems is an important t...
06/02/2020 ∙ by Zheng Shi, et al. ∙ 0 ∙ share

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Distributed Fixed Point Methods with Compressed Iterates

We propose basic and natural assumptions under which iterative optimizat...
12/20/2019 ∙ by Sélim Chraibi, et al. ∙ 0 ∙ share

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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...
10/28/2019 ∙ by Nur Sila Gulgec, et al. ∙ 26 ∙ share

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

We propose a planning and perception mechanism for a robot (agent), that...
09/20/2019 ∙ by Hossein K. Mousavi, et al. ∙ 31 ∙ share

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Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1

In this paper, we present a scalable distributed implementation of the s...
05/30/2019 ∙ by Majid Jahani, et al. ∙ 3 ∙ share

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

Although reinforcement learning (RL) can provide reliable solutions in m...
05/30/2019 ∙ by MohammadReza Nazari, et al. ∙ 1 ∙ share

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Multi-Agent Image Classification via Reinforcement Learning

We investigate a classification problem using multiple mobile agents tha...
05/13/2019 ∙ by Hossein K. Mousavi, et al. ∙ 9 ∙ share

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

We present two sampled quasi-Newton methods for deep learning: sampled L...
01/28/2019 ∙ by Albert S. Berahas, et al. ∙ 0 ∙ share

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Distributed Learning with Compressed Gradient Differences

Training very large machine learning models requires a distributed compu...
01/26/2019 ∙ by Konstantin Mishchenko, et al. ∙ 0 ∙ share

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Inexact SARAH Algorithm for Stochastic Optimization

We develop and analyze a variant of variance reducing stochastic gradien...
11/25/2018 ∙ by Lam M. Nguyen, et al. ∙ 0 ∙ share

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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...
10/26/2018 ∙ by Majid Jahani, et al. ∙ 0 ∙ share

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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...
07/14/2018 ∙ by Jie Liu, et al. ∙ 0 ∙ share

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Active Metric Learning for Supervised Classification

Clustering and classification critically rely on distance metrics that p...
03/28/2018 ∙ by Krishnan Kumaran, et al. ∙ 2 ∙ share

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Deep Reinforcement Learning for Solving the Vehicle Routing Problem

We present an end-to-end framework for solving Vehicle Routing Problem (...
02/12/2018 ∙ by MohammadReza Nazari, et al. ∙ 0 ∙ share

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SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

Stochastic gradient descent (SGD) is the optimization algorithm of choic...
02/11/2018 ∙ by Lam M. Nguyen, et al. ∙ 0 ∙ share

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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...
08/20/2017 ∙ by Afshin Oroojlooyjadid, et al. ∙ 0 ∙ share

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

The beer game is a decentralized, multi-agent, cooperative problem that ...
08/20/2017 ∙ by Afshin Oroojlooyjadid, et al. ∙ 0 ∙ share

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

This paper describes an implementation of the L-BFGS method designed to ...
07/26/2017 ∙ by Albert S. Berahas, et al. ∙ 0 ∙ share

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Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory

We develop a family of reformulations of an arbitrary consistent linear ...
06/04/2017 ∙ by Peter Richtarik, et al. ∙ 0 ∙ share

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Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

In this paper, we study and analyze the mini-batch version of StochAstic...
05/20/2017 ∙ by Lam M. Nguyen, et al. ∙ 0 ∙ share

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

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SAR...
03/01/2017 ∙ by Lam M. Nguyen, et al. ∙ 0 ∙ share

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A Multi-Batch L-BFGS Method for Machine Learning

The question of how to parallelize the stochastic gradient descent (SGD)...
05/19/2016 ∙ by Albert S. Berahas, et al. ∙ 0 ∙ share

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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...
06/08/2015 ∙ by Chenxin Ma, et al. ∙ 0 ∙ share

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

We propose mS2GD: a method incorporating a mini-batching scheme for impr...
04/16/2015 ∙ by Jakub Konečný, et al. ∙ 0 ∙ share

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

We propose a mini-batching scheme for improving the theoretical complexi...
10/17/2014 ∙ by Jakub Konečný, et al. ∙ 0 ∙ share

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Matrix Completion under Interval Uncertainty

Matrix completion under interval uncertainty can be cast as matrix compl...
08/11/2014 ∙ by Jakub Marecek, et al. ∙ 0 ∙ share

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TOP-SPIN: TOPic discovery via Sparse Principal component INterference

We propose a novel topic discovery algorithm for unlabeled images based ...
11/04/2013 ∙ by Martin Takáč, et al. ∙ 0 ∙ share

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Distributed Coordinate Descent Method for Learning with Big Data

In this paper we develop and analyze Hydra: HYbriD cooRdinAte descent me...
10/08/2013 ∙ by Peter Richtarik, et al. ∙ 0 ∙ share

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

Given a multivariate data set, sparse principal component analysis (SPCA...
12/17/2012 ∙ by Peter Richtarik, et al. ∙ 0 ∙ share

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Parallel Coordinate Descent Methods for Big Data Optimization

In this work we show that randomized (block) coordinate descent methods ...
12/04/2012 ∙ by Peter Richtarik, et al. ∙ 0 ∙ share

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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...
07/14/2011 ∙ by Peter Richtarik, et al. ∙ 0 ∙ share

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