
AISARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods
We present an adaptive stochastic variance reduced method with an implic...
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Distributed Map Classification using Local Observations
We consider the problem of classifying a map using a team of communicati...
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DynNet: Physicsbased neural architecture design for linear and nonlinear structural response modeling and prediction
Datadriven models for predicting dynamic responses of linear and nonlin...
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Constrained Combinatorial Optimization with Reinforcement Learning
This paper presents a framework to tackle constrained combinatorial opti...
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SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
This work presents a new algorithm for empirical risk minimization. The ...
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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...
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Distributed Fixed Point Methods with Compressed Iterates
We propose basic and natural assumptions under which iterative optimizat...
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FDNet with Auxiliary Time Steps: Fast Prediction of PDEs using HessianFree TrustRegion Methods
Discovering the underlying physical behavior of complex systems is a cru...
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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...
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Scaling Up QuasiNewton Algorithms: Communication Efficient Distributed SR1
In this paper, we present a scalable distributed implementation of the s...
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Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework
Although reinforcement learning (RL) can provide reliable solutions in m...
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MultiAgent Image Classification via Reinforcement Learning
We investigate a classification problem using multiple mobile agents tha...
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QuasiNewton Methods for Deep Learning: Forget the Past, Just Sample
We present two sampled quasiNewton methods for deep learning: sampled L...
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Distributed Learning with Compressed Gradient Differences
Training very large machine learning models requires a distributed compu...
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Inexact SARAH Algorithm for Stochastic Optimization
We develop and analyze a variant of variance reducing stochastic gradien...
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Efficient Distributed Hessian Free Algorithm for Largescale Empirical Risk Minimization via Accumulating Sample Strategy
In this paper, we propose a Distributed Accumulated Newton Conjugate gra...
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On the Acceleration of LBFGS with SecondOrder Information and Stochastic Batches
This paper proposes a framework of LBFGS based on the (approximate) sec...
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Active Metric Learning for Supervised Classification
Clustering and classification critically rely on distance metrics that p...
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Deep Reinforcement Learning for Solving the Vehicle Routing Problem
We present an endtoend framework for solving Vehicle Routing Problem (...
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SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Stochastic gradient descent (SGD) is the optimization algorithm of choic...
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A Deep QNetwork for the Beer Game: A Reinforcement Learning algorithm to Solve Inventory Optimization Problems
The beer game is a widely used inclass game that is played in supply ch...
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A Deep QNetwork for the Beer Game with Partial Information
The beer game is a decentralized, multiagent, cooperative problem that ...
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A Robust MultiBatch LBFGS Method for Machine Learning
This paper describes an implementation of the LBFGS method designed to ...
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Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory
We develop a family of reformulations of an arbitrary consistent linear ...
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Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
In this paper, we study and analyze the minibatch version of StochAstic...
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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...
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A MultiBatch LBFGS Method for Machine Learning
The question of how to parallelize the stochastic gradient descent (SGD)...
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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...
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MiniBatch SemiStochastic Gradient Descent in the Proximal Setting
We propose mS2GD: a method incorporating a minibatching scheme for impr...
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mS2GD: MiniBatch SemiStochastic Gradient Descent in the Proximal Setting
We propose a minibatching scheme for improving the theoretical complexi...
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Matrix Completion under Interval Uncertainty
Matrix completion under interval uncertainty can be cast as matrix compl...
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TOPSPIN: TOPic discovery via Sparse Principal component INterference
We propose a novel topic discovery algorithm for unlabeled images based ...
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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...
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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...
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Parallel Coordinate Descent Methods for Big Data Optimization
In this work we show that randomized (block) coordinate descent methods ...
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Iteration Complexity of Randomized BlockCoordinate Descent Methods for Minimizing a Composite Function
In this paper we develop a randomized blockcoordinate descent method fo...
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Martin Takáč
verfied profile
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 20072010.