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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...
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04/04/2023
Regularization of the policy updates for stabilizing Mean Field Games
This work studies non-cooperative Multi-Agent Reinforcement Learning (MA...
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03/15/2023
MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids
Integrating variable renewable energy into the grid has posed challenges...
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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 ...
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12/07/2022
Partial Disentanglement with Partially-Federated GANs (PaDPaF)
Federated learning has become a popular machine learning paradigm with m...
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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...
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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...
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07/17/2022
SP2: A Second Order Stochastic Polyak Method
Recently the "SP" (Stochastic Polyak step size) method has emerged as a ...
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07/15/2022
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging
Collaboration among multiple data-owning entities (e.g., hospitals) can ...
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06/09/2022
Learning to generalize Dispatching rules on the Job Shop Scheduling
This paper introduces a Reinforcement Learning approach to better genera...
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06/06/2022
Learning to Control under Time-Varying Environment
This paper investigates the problem of regret minimization in linear tim...
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06/01/2022
Stochastic Gradient Methods with Preconditioned Updates
This work considers non-convex finite sum minimization. There are a numb...
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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...
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02/05/2022
Distributed Learning With Sparsified Gradient Differences
A very large number of communications are typically required to solve di...
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11/26/2021
Random-reshuffled SARAH does not need a full gradient computations
The StochAstic Recursive grAdient algoritHm (SARAH) algorithm is a varia...
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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...
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07/06/2021
Improving Text-to-Image Synthesis Using Contrastive Learning
The goal of text-to-image synthesis is to generate a visually realistic ...
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02/19/2021
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods
We present an adaptive stochastic variance reduced method with an implic...
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12/18/2020
Distributed Map Classification using Local Observations
We consider the problem of classifying a map using a team of communicati...
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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...
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06/22/2020
Constrained Combinatorial Optimization with Reinforcement Learning
This paper presents a framework to tackle constrained combinatorial opti...
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06/06/2020
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
This work presents a new algorithm for empirical risk minimization. The ...
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06/02/2020
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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12/20/2019
Distributed Fixed Point Methods with Compressed Iterates
We propose basic and natural assumptions under which iterative optimizat...
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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...
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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...
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05/30/2019
Scaling Up Quasi-Newton Algorithms: Communication Efficient Distributed SR1
In this paper, we present a scalable distributed implementation of the s...
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05/30/2019
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework
Although reinforcement learning (RL) can provide reliable solutions in m...
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05/13/2019
Multi-Agent Image Classification via Reinforcement Learning
We investigate a classification problem using multiple mobile agents tha...
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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...
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01/26/2019
Distributed Learning with Compressed Gradient Differences
Training very large machine learning models requires a distributed compu...
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11/25/2018
Inexact SARAH Algorithm for Stochastic Optimization
We develop and analyze a variant of variance reducing stochastic gradien...
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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...
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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...
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03/28/2018
Active Metric Learning for Supervised Classification
Clustering and classification critically rely on distance metrics that p...
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02/12/2018
Deep Reinforcement Learning for Solving the Vehicle Routing Problem
We present an end-to-end framework for solving Vehicle Routing Problem (...
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02/11/2018
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Stochastic gradient descent (SGD) is the optimization algorithm of choic...
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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...
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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 ...
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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 ...
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06/04/2017
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory
We develop a family of reformulations of an arbitrary consistent linear ...
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05/20/2017
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
In this paper, we study and analyze the mini-batch version of StochAstic...
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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...
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05/19/2016
A Multi-Batch L-BFGS Method for Machine Learning
The question of how to parallelize the stochastic gradient descent (SGD)...
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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...
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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...
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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...
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08/11/2014
Matrix Completion under Interval Uncertainty
Matrix completion under interval uncertainty can be cast as matrix compl...
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11/04/2013
TOP-SPIN: TOPic discovery via Sparse Principal component INterference
We propose a novel topic discovery algorithm for unlabeled images based ...
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10/08/2013
