Zheng Xu

Yanxiang Zhang

CoRR, 2024

Improved Communication-Privacy Trade-offs in L<sub>2</sub> Mean Estimation under Streaming Differential Privacy.

[BibT_eX]

[DOI]

CoRR, 2024

Prompt Public Large Language Models to Synthesize Data for Private On-device Applications.

[BibT_eX]

[DOI]

CoRR, 2024

Efficient Language Model Architectures for Differentially Private Federated Learning.

[BibT_eX]

[DOI]

Ananda Theertha Suresh

CoRR, 2024

Heterogeneous Low-Rank Approximation for Federated Fine-tuning of On-Device Foundation Models.

[BibT_eX]

[DOI]

CoRR, 2024

Can Public Large Language Models Help Private Cross-device Federated Learning?

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Privacy-Preserving Instructions for Aligning Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems.

[BibT_eX]

[DOI]

Hugh McMahan

Yanxiang Zhang

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: EMNLP 2024, 2024

User Inference Attacks on Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models.

[BibT_eX]

[DOI]

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Programming.

[BibT_eX]

[DOI]

Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024

2023

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy.

[BibT_eX]

[DOI]

Abhradeep Guha Thakurta

J. Artif. Intell. Res., 2023

InstructPipe: Building Visual Programming Pipelines with Human Instructions.

[BibT_eX]

[DOI]

CoRR, 2023

Private Federated Learning in Gboard.

[BibT_eX]

[DOI]

CoRR, 2023

(Amplified) Banded Matrix Factorization: A unified approach to private training.

[BibT_eX]

[DOI]

Abhradeep Guha Thakurta

CoRR, 2023

Can Public Large Language Models Help Private Cross-device Federated Learning?

[BibT_eX]

[DOI]

CoRR, 2023

An Empirical Evaluation of Federated Contextual Bandit Algorithms.

[BibT_eX]

[DOI]

Alekh Agarwal

H. Brendan McMahan

CoRR, 2023

(Amplified) Banded Matrix Factorization: A unified approach to private training.

[BibT_eX]

[DOI]

Abhradeep Guha Thakurta

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

How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Beyond Uniform Lipschitz Condition in Differentially Private Optimization.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

On the Convergence of Federated Averaging with Cyclic Client Participation.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Learning to Generate Image Embeddings with User-Level Differential Privacy.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Federated Learning of Gboard Language Models with Differential Privacy.

[BibT_eX]

[DOI]

Yanxiang Zhang

Galen Andrew

Proceedings of the The 61st Annual Meeting of the Association for Computational Linguistics: Industry Track, 2023

2022

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Advances and Open Problems in Federated Learning.

[BibT_eX]

[DOI]

Rafael G. L. D'Oliveira

Ananda Theertha Suresh

Found. Trends Mach. Learn., 2021

Efficient and Private Federated Learning with Partially Trainable Networks.

[BibT_eX]

[DOI]

CoRR, 2021

A Field Guide to Federated Optimization.

[BibT_eX]

[DOI]

CoRR, 2021

Local Adaptivity in Federated Learning: Convergence and Consistency.

[BibT_eX]

[DOI]

CoRR, 2021

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training.

[BibT_eX]

[DOI]

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

Practical and Private (Deep) Learning Without Sampling or Shuffling.

[BibT_eX]

[DOI]

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

2020

Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer.

[BibT_eX]

[DOI]

CoRR, 2020

Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training.

[BibT_eX]

[DOI]

Ali Shafahi

Karthik Abinav Sankararaman

CoRR, 2020

Adversarial training for fast arbitrary style transfer.

[BibT_eX]

[DOI]

Comput. Graph., 2020

The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent.

[BibT_eX]

[DOI]

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

Universal Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Alternating Optimization: Constrained Problems, Adversarial Networks, and Robust Models.

[BibT_eX]

[DOI]

PhD thesis, 2019

Advances and Open Problems in Federated Learning.

[BibT_eX]

[DOI]

Rafael G. L. D'Oliveira

Ananda Theertha Suresh

CoRR, 2019

Adversarial training for free!

[BibT_eX]

[DOI]

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

Learning from Multi-domain Artistic Images for Arbitrary Style Transfer.

[BibT_eX]

[DOI]

Proceedings of the 8th ACM/Eurographics Expressive Symposium, 2019

2018

Domain Generalization and Adaptation Using Low Rank Exemplar SVMs.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2018

Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer.

[BibT_eX]

[DOI]

CoRR, 2018

The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing.

[BibT_eX]

[DOI]

CoRR, 2018

Visualizing the Loss Landscape of Neural Nets.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning to Cluster for Proposal-Free Instance Segmentation.

[BibT_eX]

[DOI]

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Stabilizing Adversarial Nets with Prediction Methods.

[BibT_eX]

[DOI]

Proceedings of the 6th International Conference on Learning Representations, 2018

Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks.

[BibT_eX]

[DOI]

Yen-Chang Hsu

Jiawei Huang

Proceedings of the 6th International Conference on Learning Representations, 2018

Strong Baseline for Single Image Dehazing with Deep Features and Instance Normalization.

[BibT_eX]

[DOI]

Proceedings of the British Machine Vision Conference 2018, 2018

Training Student Networks for Acceleration with Conditional Adversarial Networks.

[BibT_eX]

[DOI]

Yen-Chang Hsu

Jiawei Huang

Proceedings of the British Machine Vision Conference 2018, 2018

Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training.

[BibT_eX]

[DOI]

Xitong Yang

Jiebo Luo

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Visualizing the Loss Landscape of Neural Nets.

[BibT_eX]

[DOI]

CoRR, 2017

Learning Loss for Knowledge Distillation with Conditional Adversarial Networks.

[BibT_eX]

[DOI]

Yen-Chang Hsu

Jiawei Huang

CoRR, 2017

Exploring Financial Relationships Using Probabilistic Topic Models (Demonstration Paper).

[BibT_eX]

[DOI]

Louiqa Raschid

Elena Zotkina

Proceedings of the 3rd International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets, 2017

Training Quantized Nets: A Deeper Understanding.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Adaptive Consensus ADMM for Distributed Optimization.

[BibT_eX]

[DOI]

Gavin Taylor

Hao Li

Xiaoming Yuan

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

Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation.

[BibT_eX]

[DOI]

Xiaoming Yuan

Christoph Studer

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Adaptive ADMM with Spectral Penalty Parameter Selection.

[BibT_eX]

[DOI]

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Scalable Classifiers with ADMM and Transpose Reduction.

[BibT_eX]

[DOI]

Gavin Taylor

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents.

[BibT_eX]

[DOI]

Douglas Burdick

Louiqa Raschid

CoRR, 2016

Non-negative Factorization of the Occurrence Tensor from Financial Contracts.

[BibT_eX]

[DOI]

CoRR, 2016

An Empirical Study of ADMM for Nonconvex Problems.

[BibT_eX]

[DOI]

Soham De

Christoph Studer

CoRR, 2016

Training Neural Networks Without Gradients: A Scalable ADMM Approach.

[BibT_eX]

[DOI]

Proceedings of the 33nd International Conference on Machine Learning, 2016

Probabilistic Financial Community Models with Latent Dirichlet Allocation for Financial Supply Chains.

[BibT_eX]

[DOI]

Louiqa Raschid

Proceedings of the Second International Workshop on Data Science for Macro-Modeling, 2016

resMBS: Constructing a Financial Supply Chain from Prospectus.

[BibT_eX]

[DOI]

Proceedings of the Second International Workshop on Data Science for Macro-Modeling, 2016

2015

Exploiting Low-rank Structure for Discriminative Sub-categorization.

[BibT_eX]

[DOI]

Proceedings of the British Machine Vision Conference 2015, 2015

2014

Exploiting Low-Rank Structure from Latent Domains for Domain Generalization.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2014, 2014

2013

Mining visualness.

[BibT_eX]

[DOI]