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07/21/2023
Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent
Each round in Differential Private Stochastic Gradient Descent (DPSGD) t...
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06/11/2023
Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach
Representation learning for time series has been an important research a...
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06/01/2023
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Time series forecasting using historical data has been an interesting an...
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04/12/2023
Label-Free Concept Bottleneck Models
Concept bottleneck models (CBM) are a popular way of creating more inter...
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02/11/2023
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Deep neural networks (DNN) have shown great capacity of modeling a dynam...
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12/12/2022
Generalizing DP-SGD with Shuffling and Batching Clipping
Classical differential private DP-SGD implements individual clipping wit...
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06/21/2022
Finding Optimal Policy for Queueing Models: New Parameterization
Queueing systems appear in many important real-life applications includi...
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06/13/2022
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
Stochastic gradient descent (SGD) algorithm is the method of choice in m...
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02/08/2022
On the Convergence of Gradient Extrapolation Methods for Unbalanced Optimal Transport
We study the Unbalanced Optimal Transport (UOT) between two measures of ...
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02/07/2022
Evaluating Robustness of Cooperative MARL: A Model-based Approach
In recent years, a proliferation of methods were developed for cooperati...
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02/07/2022
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
In this paper, we propose Nesterov Accelerated Shuffling Gradient (NASG)...
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02/07/2022
Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution
Deep neural networks (DNNs) have shown great success in many machine lea...
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12/10/2021
Interpretable Clustering via Multi-Polytope Machines
Clustering is a popular unsupervised learning tool often used to discove...
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11/11/2021
On the Equivalence between Neural Network and Support Vector Machine
Recent research shows that the dynamics of an infinitely wide neural net...
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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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02/17/2021
Differential Private Hogwild! over Distributed Local Data Sets
We consider the Hogwild! setting where clients use local SGD iterations ...
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11/24/2020
Shuffling Gradient-Based Methods with Momentum
We combine two advanced ideas widely used in optimization for machine le...
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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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10/27/2020
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where...
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08/20/2020
An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization
In this note we propose a new variant of the hybrid variance-reduced pro...
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07/17/2020
Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise
The feasibility of federated learning is highly constrained by the serve...
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06/27/2020
Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems
We develop a novel variance-reduced algorithm to solve a stochastic nonc...
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03/24/2020
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness
Motivated by broad applications in reinforcement learning and machine le...
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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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02/17/2020
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
We develop two new stochastic Gauss-Newton algorithms for solving a clas...
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10/03/2019
BUZz: BUffer Zones for defending adversarial examples in image classification
We propose a novel defense against all existing gradient based adversari...
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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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12/18/2018
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing state-of-the-art machine learning mo...
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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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10/10/2018
Tight Dimension Independent Lower Bound on Optimal Expected Convergence Rate for Diminishing Step Sizes in SGD
We study convergence of Stochastic Gradient Descent (SGD) for strongly c...
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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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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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01/18/2018
When Does Stochastic Gradient Algorithm Work Well?
In this paper, we consider a general stochastic optimization problem whi...
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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