Lam M. Nguyen

CoRR, 2024

One Step Closer to Unbiased Aleatoric Uncertainty Estimation.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series.

[BibT_eX]

[DOI]

CoRR, 2023

Correlated Attention in Transformers for Multivariate Time Series.

[BibT_eX]

[DOI]

Quang Minh Nguyen

Subhro Das

CoRR, 2023

Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent.

[BibT_eX]

[DOI]

CoRR, 2023

Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach.

[BibT_eX]

[DOI]

CoRR, 2023

An End-to-End Time Series Model for Simultaneous Imputation and Forecast.

[BibT_eX]

[DOI]

CoRR, 2023

On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Analyzing Generalization of Neural Networks through Loss Path Kernels.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Label-free Concept Bottleneck Models.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Attacking c-MARL More Effectively: A Data Driven Approach.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Data Mining, 2023

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Data Mining, 2023

Scalable and Secure Federated XGBoost.

[BibT_eX]

[DOI]

Quang Minh Nguyen

Nhan Khanh Le

Dharmashankar Subramanian

Proceedings of the IEEE International Conference on Acoustics, 2023

Optimal Control via Linearizable Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2023

2022

A hybrid stochastic optimization framework for composite nonconvex optimization.

[BibT_eX]

[DOI]

Math. Program., 2022

AI-Based Real-Time Site-Wide Optimization for Process Manufacturing.

[BibT_eX]

[DOI]

Raju Pavuluri

Xiang Ma

Crystal Lui

Giovane Cesar Da Silva

INFORMS J. Appl. Anal., 2022

Generalizing DP-SGD with Shuffling and Batching Clipping.

[BibT_eX]

[DOI]

CoRR, 2022

Finding Optimal Policy for Queueing Models: New Parameterization.

[BibT_eX]

[DOI]

Katya Scheinberg

CoRR, 2022

On the Convergence of Gradient Extrapolation Methods for Unbalanced Optimal Transport.

[BibT_eX]

[DOI]

CoRR, 2022

Evaluating Robustness of Cooperative MARL: A Model-based Approach.

[BibT_eX]

[DOI]

CoRR, 2022

Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution.

[BibT_eX]

[DOI]

Marten van Dijk

CoRR, 2022

Finite-sum smooth optimization with SARAH.

[BibT_eX]

[DOI]

Comput. Optim. Appl., 2022

Besting the Black-Box: Barrier Zones for Adversarial Example Defense.

[BibT_eX]

[DOI]

IEEE Access, 2022

StepDIRECT - A Derivative-Free Optimization Method for Stepwise Functions.

[BibT_eX]

[DOI]

Dzung T. Phan

Hongsheng Liu

Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

Nesterov Accelerated Shuffling Gradient Method for Convex Optimization.

[BibT_eX]

[DOI]

Katya Scheinberg

Proceedings of the International Conference on Machine Learning, 2022

Interpretable Clustering via Multi-Polytope Machines.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Inexact SARAH algorithm for stochastic optimization.

[BibT_eX]

[DOI]

Ramón Fernandez Astudillo

Katya Scheinberg

Martin Takác

Optim. Methods Softw., 2021

A Unified Convergence Analysis for Shuffling-Type Gradient Methods.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Federated Learning with Randomized Douglas-Rachford Splitting Methods.

[BibT_eX]

[DOI]

CoRR, 2021

Differential Private Hogwild! over Distributed Local Data Sets.

[BibT_eX]

[DOI]

CoRR, 2021

FedDR - Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Ensembling Graph Predictions for AMR Parsing.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Equivalence between Neural Network and Support Vector Machine.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

SMG: A Shuffling Gradient-Based Method with Momentum.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Regression Optimization for System-level Production Control.

[BibT_eX]

[DOI]

Proceedings of the 2021 American Control Conference, 2021

Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2020

Shuffling Gradient-Based Methods with Momentum.

[BibT_eX]

[DOI]

CoRR, 2020

Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise.

[BibT_eX]

[DOI]

CoRR, 2020

Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness.

[BibT_eX]

[DOI]

CoRR, 2020

A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function.

[BibT_eX]

[DOI]

Deyi Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization.

[BibT_eX]

[DOI]

Nhan H. Pham

Proceedings of the 37th International Conference on Machine Learning, 2020

Pruning Deep Neural Networks with $\ell_{0}$-constrained Optimization.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

New Convergence Aspects of Stochastic Gradient Algorithms.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

BUZz: BUffer Zones for defending adversarial examples in image classification.

[BibT_eX]

[DOI]

CoRR, 2019

A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization.

[BibT_eX]

[DOI]

CoRR, 2019

Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH.

[BibT_eX]

[DOI]

CoRR, 2019

Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD.

[BibT_eX]

[DOI]

Phuong Ha Nguyen

Marten van Dijk

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

A queueing system with on-demand servers: local stability of fluid limits.

[BibT_eX]

[DOI]

Alexander L. Stolyar

Queueing Syst. Theory Appl., 2018

PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach.

[BibT_eX]

[DOI]

CoRR, 2018

Tight Dimension Independent Lower Bound on Optimal Expected Convergence Rate for Diminishing Step Sizes in SGD.

[BibT_eX]

[DOI]

Phuong Ha Nguyen

Marten van Dijk

CoRR, 2018

When Does Stochastic Gradient Algorithm Work Well?

[BibT_eX]

[DOI]

CoRR, 2018

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

ChieF: A Change Pattern based Interpretable Failure Analyzer.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization.

[BibT_eX]

[DOI]

CoRR, 2017

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

A Service System with Randomly Behaving On-demand Agents.

[BibT_eX]

[DOI]