| A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization | 0 | 0.34 | 2022 |
| Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information | 0 | 0.34 | 2022 |
| Improving Text-to-Image Synthesis Using Contrastive Learning. | 0 | 0.34 | 2021 |
| Sonia: A Symmetric Blockwise Truncated Optimization Algorithm | 0 | 0.34 | 2021 |
| Roles For Event Representations In Sensorimotor Experience, Memory Formation, And Language Processing | 0 | 0.34 | 2021 |
| Fast And Safe: Accelerated Gradient Methods With Optimality Certificates And Underestimate Sequences | 0 | 0.34 | 2021 |
| Active Metric Learning For Supervised Classification | 0 | 0.34 | 2021 |
| Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment | 0 | 0.34 | 2020 |
| A robust multi-batch L-BFGS method for machine learning* | 2 | 0.37 | 2020 |
| A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning | 0 | 0.34 | 2020 |
| Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations | 0 | 0.34 | 2020 |
| Multi-Agent Image Classification Via Reinforcement Learning | 0 | 0.34 | 2019 |
| Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample. | 0 | 0.34 | 2019 |
| Entropy-Penalized Semidefinite Programming. | 0 | 0.34 | 2019 |
| Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework. | 0 | 0.34 | 2019 |
| Distributed Learning with Compressed Gradient Differences. | 2 | 0.36 | 2019 |
| SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. | 5 | 0.43 | 2018 |
| Inexact Sarah Algorithm For Stochastic Optimization | 3 | 0.43 | 2018 |
| Reinforcement Learning for Solving the Vehicle Routing Problem. | 8 | 0.49 | 2018 |
| On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches. | 1 | 0.35 | 2018 |
| Deep Reinforcement Learning for Solving the Vehicle Routing Problem. | 0 | 0.34 | 2018 |
| Active Metric Learning for Supervised Classification. | 0 | 0.34 | 2018 |
| New Convergence Aspects of Stochastic Gradient Algorithms. | 1 | 0.35 | 2018 |
| Distributed Inexact Damped Newton Method: Data Partitioning and Work-Balancing. | 0 | 0.34 | 2017 |
| SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. | 20 | 0.64 | 2017 |
| Matrix completion under interval uncertainty. | 4 | 0.41 | 2017 |
| Distributed Hessian-Free Optimization for Deep Neural Network. | 1 | 0.35 | 2017 |
| Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. | 6 | 0.46 | 2017 |
| A Low-Rank Coordinate-Descent Algorithm for Semidefinite Programming Relaxations of Optimal Power Flow | 4 | 0.39 | 2017 |
| An Accelerated Communication-Efficient Primal-Dual Optimization Framework For Structured Machine Learning | 1 | 0.37 | 2017 |
| STOCHASTIC REFORMULATIONS OF LINEAR SYSTEMS: ALGORITHMS AND CONVERGENCE THEORY | 7 | 0.47 | 2017 |
| Underestimate Sequences via Quadratic Averaging. | 0 | 0.34 | 2017 |
| Stock-out Prediction in Multi-echelon Networks. | 0 | 0.34 | 2017 |
| CoCoA: A General Framework for Communication-Efficient Distributed Optimization. | 5 | 0.52 | 2017 |
| Hybrid Methods in Solving Alternating-Current Optimal Power Flows. | 3 | 0.37 | 2017 |
| A Deep Q-Network for the Beer Game with Partial Information. | 0 | 0.34 | 2017 |
| A Multi-Batch L-BFGS Method for Machine Learning. | 12 | 0.56 | 2016 |
| Primal-Dual Rates and Certificates. | 2 | 0.46 | 2016 |
| Applying deep learning to the newsvendor problem | 1 | 0.35 | 2016 |
| Large Scale Distributed Hessian-Free Optimization for Deep Neural Network. | 3 | 0.37 | 2016 |
| A Multi-Batch L-BFGS Method for Machine Learning. | 0 | 0.34 | 2016 |
| Parallel coordinate descent methods for big data optimization. | 99 | 3.42 | 2016 |
| Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing. | 1 | 0.34 | 2016 |
| Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption. | 5 | 0.42 | 2016 |
| Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption. | 0 | 0.34 | 2016 |
| Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. | 34 | 1.13 | 2016 |
| Adding vs. Averaging in Distributed Primal-Dual Optimization. | 26 | 1.06 | 2015 |
| Distributed Optimization with Arbitrary Local Solvers | 32 | 1.47 | 2015 |
| SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. | 21 | 0.76 | 2015 |
| Dual Free SDCA for Empirical Risk Minimization with Adaptive Probabilities. | 2 | 0.43 | 2015 |
