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08/23/2022
Cardinality-Regularized Hawkes-Granger Model
We propose a new sparse Granger-causal learning framework for temporal e...
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03/05/2021
Federated Learning with Randomized Douglas-Rachford Splitting Methods
In this paper, we develop two new algorithms, called, FedDR and asyncFed...
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11/06/2020
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
Several recent publications report advances in training optimal decision...
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03/01/2020
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
We propose a novel hybrid stochastic policy gradient estimator by combin...
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02/19/2020
A Unified Convergence Analysis for Shuffling-Type Gradient Methods
In this paper, we provide a unified convergence analysis for a class of ...
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07/08/2019
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
In this paper, we introduce a new approach to develop stochastic optimiz...
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05/15/2019
Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization
We introduce a hybrid stochastic estimator to design stochastic gradient...
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02/15/2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
In this paper, we propose a new stochastic algorithmic framework to solv...
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01/22/2019
Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH
The total complexity (measured as the total number of gradient computati...
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01/22/2019
DTN: A Learning Rate Scheme with Convergence Rate of O(1/t) for SGD
We propose a novel diminishing learning rate scheme, coined Decreasing-T...
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10/09/2018
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
We study Stochastic Gradient Descent (SGD) with diminishing step sizes f...
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01/18/2018