Martin Takáč

research

∙ 04/08/2023

WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition

Named Entity Recognition (NER) is a fundamental NLP tasks with a wide ra...

0 Dávid Šuba, et al. ∙

research

∙ 04/04/2023

Regularization of the policy updates for stabilizing Mean Field Games

This work studies non-cooperative Multi-Agent Reinforcement Learning (MA...

0 Talal Algumaei, et al. ∙

research

∙ 03/15/2023

MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids

Integrating variable renewable energy into the grid has posed challenges...

0 Nicolas Cuadrado, et al. ∙

research

∙ 01/02/2023

In Quest of Ground Truth: Learning Confident Models and Estimating Uncertainty in the Presence of Annotator Noise

The performance of the Deep Learning (DL) models depends on the quality ...

0 Asma Ahmed Hashmi, et al. ∙

research

∙ 12/07/2022

Partial Disentanglement with Partially-Federated GANs (PaDPaF)

Federated learning has become a popular machine learning paradigm with m...

0 Abdulla Jasem Almansoori, et al. ∙

research

∙ 11/02/2022

Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points

This work shows that applying Gradient Descent (GD) with a fixed step si...

0 Rachael Tappenden, et al. ∙

research

∙ 10/18/2022

FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences

In the recent paper FLECS (Agafonov et al, FLECS: A Federated Learning S...

0 Artem Agafonov, et al. ∙

research

∙ 07/17/2022

SP2: A Second Order Stochastic Polyak Method

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

5 Shuang Li, et al. ∙

research

∙ 07/15/2022

Suppressing Poisoning Attacks on Federated Learning for Medical Imaging

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

0 Naif Alkhunaizi, et al. ∙

research

∙ 06/09/2022

Learning to generalize Dispatching rules on the Job Shop Scheduling

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

0 Zangir Iklassov, et al. ∙

research

∙ 06/06/2022

Learning to Control under Time-Varying Environment

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

0 Yuzhen Han, et al. ∙

research

∙ 06/01/2022

Stochastic Gradient Methods with Preconditioned Updates

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

0 Abdurakhmon Sadiev, et al. ∙

research

∙ 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...

0 Guangyi Liu, et al. ∙

research

∙ 02/05/2022

Distributed Learning With Sparsified Gradient Differences

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

0 Yicheng Chen, et al. ∙

research

∙ 11/26/2021

Random-reshuffled SARAH does not need a full gradient computations

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

0 Aleksandr Beznosikov, et al. ∙

research

∙ 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...

12 Majid Jahani, et al. ∙

research

∙ 07/06/2021

Improving Text-to-Image Synthesis Using Contrastive Learning

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

0 Hui Ye, et al. ∙

research

∙ 02/19/2021

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods

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

12 Zheng Shi, et al. ∙

research

∙ 12/18/2020

Distributed Map Classification using Local Observations

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

26 Guangyi Liu, et al. ∙

research

∙ 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...

0 Soheil Sadeghi Eshkevari, et al. ∙

research

∙ 06/22/2020

Constrained Combinatorial Optimization with Reinforcement Learning

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

0 Ruben Solozabal, et al. ∙

research

∙ 06/06/2020

SONIA: A Symmetric Blockwise Truncated Optimization Algorithm

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

0 Majid Jahani, et al. ∙

research

∙ 06/02/2020

Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations

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

0 Zheng Shi, et al. ∙

research

∙ 12/20/2019

Distributed Fixed Point Methods with Compressed Iterates

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

0 Sélim Chraibi, et al. ∙

research

∙ 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...

26 Nur Sila Gulgec, et al. ∙

research

∙ 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...

31 Hossein K. Mousavi, et al. ∙

research

∙ 05/30/2019

Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1

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

3 Majid Jahani, et al. ∙

research

∙ 05/30/2019

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

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

1 MohammadReza Nazari, et al. ∙

research

∙ 05/13/2019

Multi-Agent Image Classification via Reinforcement Learning

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

9 Hossein K. Mousavi, et al. ∙

research

∙ 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...

0 Albert S. Berahas, et al. ∙

research

∙ 01/26/2019

Distributed Learning with Compressed Gradient Differences

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

0 Konstantin Mishchenko, et al. ∙

research

∙ 11/25/2018

Inexact SARAH Algorithm for Stochastic Optimization

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

0 Lam M. Nguyen, et al. ∙

research

∙ 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...

0 Majid Jahani, et al. ∙

research

∙ 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...

0 Jie Liu, et al. ∙

research

∙ 03/28/2018

Active Metric Learning for Supervised Classification

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

2 Krishnan Kumaran, et al. ∙

research

∙ 02/12/2018

Deep Reinforcement Learning for Solving the Vehicle Routing Problem

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

0 MohammadReza Nazari, et al. ∙

research

∙ 02/11/2018

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

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

0 Lam M. Nguyen, et al. ∙

research

∙ 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...

0 Afshin Oroojlooyjadid, et al. ∙

research

∙ 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 ...

0 Afshin Oroojlooyjadid, et al. ∙

research

∙ 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 ...

0 Albert S. Berahas, et al. ∙

research

∙ 06/04/2017

Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory

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

0 Peter Richtarik, et al. ∙

research

∙ 05/20/2017

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

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

0 Lam M. Nguyen, et al. ∙

research

∙ 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...

0 Lam M. Nguyen, et al. ∙

research

∙ 05/19/2016

A Multi-Batch L-BFGS Method for Machine Learning

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

0 Albert S. Berahas, et al. ∙

research

∙ 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...

0 Chenxin Ma, et al. ∙

research

∙ 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...

0 Jakub Konečný, et al. ∙

research

∙ 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...

0 Jakub Konečný, et al. ∙

research

∙ 08/11/2014

Matrix Completion under Interval Uncertainty

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

0 Jakub Marecek, et al. ∙

research

∙ 11/04/2013

TOP-SPIN: TOPic discovery via Sparse Principal component INterference

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

0 Martin Takáč, et al. ∙

research

∙ 10/08/2013

Distributed Coordinate Descent Method for Learning with Big Data

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

0 Peter Richtarik, et al. ∙

Martin Takáč

Featured Co-authors