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Reinforcement Learning based Multi-Robot Classification via Scalable Communication Structure
In the multi-robot collaboration domain, training with Reinforcement Lea...
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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...
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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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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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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 Quasi-Newton 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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Multi-Agent Image Classification via Reinforcement Learning
We investigate a classification problem using multiple mobile agents tha...
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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...
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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 Large-scale 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 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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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 end-to-end 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 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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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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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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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 mini-batch 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 Multi-Batch L-BFGS 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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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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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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Matrix Completion under Interval Uncertainty
Matrix completion under interval uncertainty can be cast as matrix compl...
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TOP-SPIN: 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 Block-Coordinate Descent Methods for Minimizing a Composite Function
In this paper we develop a randomized block-coordinate descent method fo...
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