Tom Goldstein

According to our database, Tom Goldstein authored at least 239 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion.

Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum

CoRR, 2024

What do we learn from inverting CLIP models?

Hamid Kazemi, Atoosa Malemir Chegini, Jonas Geiping, Soheil Feizi, Tom Goldstein

CoRR, 2024

Coercing LLMs to do and reveal (almost) anything.

Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, Tom Goldstein

CoRR, 2024

ODIN: Disentangled Reward Mitigates Hacking in RLHF.

Lichang Chen, Chen Zhu, Davit Soselia, Jiuhai Chen, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro

CoRR, 2024

Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models.

Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang

CoRR, 2024

Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text.

Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

CoRR, 2024

Benchmarking the Robustness of Image Watermarks.

Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang

CoRR, 2024

2023

Towards Transferable Adversarial Attacks on Image and Video Transformers.

Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang, Larry S. Davis

IEEE Trans. Image Process., 2023

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses.

Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein

IEEE Trans. Pattern Anal. Mach. Intell., 2023

Universal Pyramid Adversarial Training for Improved ViT Performance.

Ping-yeh Chiang, Yipin Zhou, Omid Poursaeed, Satya Narayan Shukla, Ashish Shah, Tom Goldstein, Ser-Nam Lim

CoRR, 2023

Perspectives on the State and Future of Deep Learning - 2023.

Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson

CoRR, 2023

Object Recognition as Next Token Prediction.

Kaiyu Yue, Bor-Chun Chen, Jonas Geiping, Hengduo Li, Tom Goldstein, Ser-Nam Lim

CoRR, 2023

A Simple and Efficient Baseline for Data Attribution on Images.

Vasu Singla, Pedro Sandoval Segura, Micah Goldblum, Jonas Geiping, Tom Goldstein

CoRR, 2023

NEFTune: Noisy Embeddings Improve Instruction Finetuning.

Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

CoRR, 2023

Baseline Defenses for Adversarial Attacks Against Aligned Language Models.

Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein

CoRR, 2023

Bring Your Own Data! Self-Supervised Evaluation for Large Language Models.

Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

CoRR, 2023

On the Reliability of Watermarks for Large Language Models.

John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein

CoRR, 2023

InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models.

Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou

CoRR, 2023

Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust.

Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein

CoRR, 2023

A Cookbook of Self-Supervised Learning.

Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum

CoRR, 2023

JPEG Compressed Images Can Bypass Protections Against AI Editing.

Pedro Sandoval Segura, Jonas Geiping, Tom Goldstein

CoRR, 2023

Neural Auctions Compromise Bidder Information.

Alex Stein, Avi Schwarzschild, Michael J. Curry, Tom Goldstein, John P. Dickerson

CoRR, 2023

Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images.

Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein

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

Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery.

Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein

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

Understanding and Mitigating Copying in Diffusion Models.

Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein

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

On the Exploitability of Instruction Tuning.

Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, Tom Goldstein

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

What Can We Learn from Unlearnable Datasets?

Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein

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

Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks.

Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein

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

A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning.

Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum

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

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.

Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein

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

GOAT: A Global Transformer on Large-scale Graphs.

Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2023

A Watermark for Large Language Models.

John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2023

Cramming: Training a Language Model on a single GPU in one day.

Jonas Geiping, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2023

Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness.

Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries.

Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Seeing in Words: Learning to Classify through Language Bottlenecks.

Khalid Saifullah, Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Transfer Learning with Deep Tabular Models.

Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Provable Robustness against Wasserstein Distribution Shifts via Input Randomization.

Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi

Proceedings of the Eleventh International Conference on Learning Representations, 2023

How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization.

Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models.

Liam H. Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation.

Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent.

Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein

Proceedings of the Eleventh International Conference on Learning Representations, 2023

STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning.

Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein

Proceedings of the IEEE International Conference on Acoustics, 2023

Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models.

Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein

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

Universal Guidance for Diffusion Models.

Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein

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

Unifying the Harmonic Analysis of Adversarial Attacks and Robustness.

Shishira R. Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava

Proceedings of the 34th British Machine Vision Conference 2023, 2023

A Deep Dive into Dataset Imbalance and Bias in Face Identification.

Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, John P. Dickerson, Micah Goldblum, Tom Goldstein

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022

Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems.

Haochuan Song, Tom Goldstein, Xiaohu You, Chuan Zhang, Olav Tirkkonen, Christoph Studer

IEEE Trans. Wirel. Commun., 2022

What do Vision Transformers Learn? A Visual Exploration.

Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein

CoRR, 2022

K-SAM: Sharpness-Aware Minimization at the Speed of SGD.

Renkun Ni, Ping-yeh Chiang, Jonas Geiping, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein

CoRR, 2022

Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning.

Yuxin Wen, Jonas Geiping, Liam Fowl, Hossein Souri, Rama Chellappa, Micah Goldblum, Tom Goldstein

CoRR, 2022

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.

Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein

CoRR, 2022

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf

CoRR, 2022

A Deep Dive into Dataset Imbalance and Bias in Face Identification.

Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, Micah Goldblum, Tom Goldstein

CoRR, 2022

End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking.

Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

CoRR, 2022

Certifying Model Accuracy under Distribution Shifts.

Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi

CoRR, 2022

Execute Order 66: Targeted Data Poisoning for Reinforcement Learning.

Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor

CoRR, 2022

Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch.

Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models.

Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Autoregressive Perturbations for Data Poisoning.

Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David Jacobs

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability.

Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robustness Disparities in Face Detection.

Samuel Dooley, George Z. Wei, Tom Goldstein, John Dickerson

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking.

Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification.

Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2022

Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations.

Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2022

Certified Neural Network Watermarks with Randomized Smoothing.

Arpit Bansal, Ping-Yeh Chiang, Michael J. Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P. Dickerson, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2022

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

Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

The Uncanny Similarity of Recurrence and Depth.

Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

The Close Relationship Between Contrastive Learning and Meta-Learning.

Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

Stochastic Training is Not Necessary for Generalization.

Jonas Geiping, Micah Goldblum, Phillip Pope, Michael Moeller, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models.

Liam H. Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

Proceedings of the Tenth International Conference on Learning Representations, 2022

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective.

Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Poisons that are learned faster are more effective.

Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Robust Optimization as Data Augmentation for Large-scale Graphs.

Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Learning Revenue-Maximizing Auctions With Differentiable Matching.

Michael J. Curry, Uro Lyi, Tom Goldstein, John P. Dickerson

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Towards Transferable Adversarial Attacks on Vision Transformers.

Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang

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

2021

Active Learning at the ImageNet Scale.

Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang, Wojciech Czaja, Richard Leapman, Micah Goldblum, Tom Goldstein

CoRR, 2021

A Frequency Perspective of Adversarial Robustness.

Shishira R. Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava

CoRR, 2021

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

CoRR, 2021

Comparing Human and Machine Bias in Face Recognition.

Samuel Dooley, Ryan Downing, George Z. Wei, Nathan Shankar, Bradon Thymes, Gudrun Thorkelsdottir, Tiye Kurtz-Miott, Rachel Mattson, Olufemi Obiwumi, Valeriia Cherepanova, Micah Goldblum, John P. Dickerson, Tom Goldstein

CoRR, 2021

Robustness Disparities in Commercial Face Detection.

Samuel Dooley, Tom Goldstein, John P. Dickerson

CoRR, 2021

Datasets for Studying Generalization from Easy to Hard Examples.

Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

CoRR, 2021

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data.

Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein

CoRR, 2021

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.

Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein

CoRR, 2021

THAT: Two Head Adversarial Training for Improving Robustness at Scale.

Zuxuan Wu, Tom Goldstein, Larry S. Davis, Ser-Nam Lim

CoRR, 2021

Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving.

Shivam Akhauri, Laura Y. Zheng, Tom Goldstein, Ming C. Lin

CoRR, 2021

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release.

Liam Fowl, Ping-yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, Tom Goldstein

CoRR, 2021

DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations.

Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein

CoRR, 2021

What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors.

Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein

CoRR, 2021

Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images.

Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin

CoRR, 2021

Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems.

Avi Schwarzschild, Arjun Gupta, Micah Goldblum, Tom Goldstein

CoRR, 2021

Center Smoothing for Certifiably Robust Vector-Valued Functions.

Aounon Kumar, Tom Goldstein

CoRR, 2021

Technical Challenges for Training Fair Neural Networks.

Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P. Dickerson, Tom Goldstein

CoRR, 2021

MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients.

Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Long-Short Transformer: Efficient Transformers for Language and Vision.

Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro

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

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

Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein

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

Encoding Robustness to Image Style via Adversarial Feature Perturbations.

Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein

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

Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering.

Yu Shen, Laura Y. Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin

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

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks.

Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein

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

Center Smoothing: Certified Robustness for Networks with Structured Outputs.

Aounon Kumar, Tom Goldstein

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

Adversarial Examples Make Strong Poisons.

Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein

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

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization.

Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein

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

Adversarial Differentiable Data Augmentation for Autonomous Systems.

Manli Shu, Yu Shen, Ming C. Lin, Tom Goldstein

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks.

Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein

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

Data Augmentation for Meta-Learning.

Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein

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

The Intrinsic Dimension of Images and Its Impact on Learning.

Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein

Proceedings of the 9th International Conference on Learning Representations, 2021

WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic.

Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein

Proceedings of the 9th International Conference on Learning Representations, 2021

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching.

Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein

Proceedings of the 9th International Conference on Learning Representations, 2021

LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition.

Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein

Proceedings of the 9th International Conference on Learning Representations, 2021

Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff.

Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta

Proceedings of the IEEE International Conference on Acoustics, 2021

Adversarial attacks on machine learning systems for high-frequency trading.

Micah Goldblum, Avi Schwarzschild, Ankit B. Patel, Tom Goldstein

Proceedings of the ICAIF'21: 2nd ACM International Conference on AI in Finance, Virtual Event, November 3, 2021

Hybrid Jammer Mitigation for All-Digital mmWave Massive MU-MIMO.

Gian Marti, Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer

Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks.

Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO With Processing-in-Memory.

Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer

IEEE Trans. Circuits Syst. II Express Briefs, 2020

Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication.

Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer

IEEE J. Sel. Areas Commun., 2020

Analyzing the Machine Learning Conference Review Process.

David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, Tom Goldstein

CoRR, 2020

Tight Second-Order Certificates for Randomized Smoothing.

Alexander Levine, Aounon Kumar, Thomas A. Goldstein, Soheil Feizi

CoRR, 2020

FLAG: Adversarial Data Augmentation for Graph Neural Networks.

Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

CoRR, 2020

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

Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein

CoRR, 2020

Random Network Distillation as a Diversity Metric for Both Image and Text Generation.

Liam Fowl, Micah Goldblum, Arjun Gupta, Amr Sharaf, Tom Goldstein

CoRR, 2020

ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning.

Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J. Curry, Samuel Dooley, Ping-yeh Chiang, Tom Goldstein, John P. Dickerson

CoRR, 2020

An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process.

David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, Tom Goldstein

CoRR, 2020

Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization.

Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein

CoRR, 2020

WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic.

Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-yeh Chiang, Christoph Studer, Tom Goldstein

CoRR, 2020

Detection as Regression: Certified Object Detection by Median Smoothing.

Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John P. Dickerson, Tom Goldstein

CoRR, 2020

Adaptive Learning Rates with Maximum Variation Averaging.

Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein

CoRR, 2020

Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training.

Zheng Xu, Ali Shafahi, Tom Goldstein

CoRR, 2020

Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers.

Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein

CoRR, 2020

Adversarial Attacks on Machine Learning Systems for High-Frequency Trading.

Micah Goldblum, Avi Schwarzschild, Naftali Cohen, Tucker Balch, Ankit B. Patel, Tom Goldstein

CoRR, 2020

Certifying Confidence via Randomized Smoothing.

Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein

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

MetaPoison: Practical General-purpose Clean-label Data Poisoning.

W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein

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

Adversarially Robust Few-Shot Learning: A Meta-Learning Approach.

Micah Goldblum, Liam Fowl, Tom Goldstein

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

Certifying Strategyproof Auction Networks.

Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John Dickerson

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

Detection as Regression: Certified Object Detection with Median Smoothing.

Ping-yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein

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

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

Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein

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

Adversarial Attacks on Copyright Detection Systems.

Parsa Saadatpanah, Ali Shafahi, Tom Goldstein

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

Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness.

Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi

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

Certified Data Removal from Machine Learning Models.

Chuan Guo, Tom Goldstein, Awni Y. Hannun, Laurens van der Maaten

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

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks.

Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein

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

FreeLB: Enhanced Adversarial Training for Natural Language Understanding.

Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu

Proceedings of the 8th International Conference on Learning Representations, 2020

Network Deconvolution.

Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke, Cornelia Fermüller, Yiannis Aloimonos

Proceedings of the 8th International Conference on Learning Representations, 2020

Adversarially robust transfer learning.

Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David W. Jacobs, Tom Goldstein

Proceedings of the 8th International Conference on Learning Representations, 2020

Truth or backpropaganda? An empirical investigation of deep learning theory.

Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein

Proceedings of the 8th International Conference on Learning Representations, 2020

Breaking Certified Defenses: Semantic Adversarial Examples with Spoofed robustness Certificates.

Amin Ghiasi, Ali Shafahi, Tom Goldstein

Proceedings of the 8th International Conference on Learning Representations, 2020

Certified Defenses for Adversarial Patches.

Ping-yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein

Proceedings of the 8th International Conference on Learning Representations, 2020

Understanding Generalization Through Visualizations.

W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

Witchcraft: Efficient PGD Attacks with Random Step Size.

Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Soft-Output Finite Alphabet Equalization for mmWave Massive MIMO.

Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Headless Horseman: Adversarial Attacks on Transfer Learning Models.

Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors.

Zuxuan Wu, Ser-Nam Lim, Larry S. Davis, Tom Goldstein

Proceedings of the Computer Vision - ECCV 2020, 2020

Deep k-NN Defense Against Clean-Label Data Poisoning Attacks.

Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson

Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

MSE-Optimal Neural Network Initialization via Layer Fusion.

Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer

Proceedings of the 54th Annual Conference on Information Sciences and Systems, 2020

Making L-BFGS Work with Industrial-Strength Nets.

Abhay Kumar Yadav, Tom Goldstein, David W. Jacobs

Proceedings of the 31st British Machine Vision Conference 2020, 2020

Hardware-Friendly Two-Stage Spatial Equalization for All-Digital mmWave Massive MU-MIMO.

Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer

Proceedings of the 54th Asilomar Conference on Signals, Systems, and Computers, 2020

Universal Adversarial Training.

Ali Shafahi, Mahyar Najibi, Zheng Xu, John P. Dickerson, Larry S. Davis, Tom Goldstein

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

Adversarially Robust Distillation.

Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein

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

2019

Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?

Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein

CoRR, 2019

Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets.

Yogesh Balaji, Tom Goldstein, Judy Hoffman

CoRR, 2019

Robust Few-Shot Learning with Adversarially Queried Meta-Learners.

Micah Goldblum, Liam Fowl, Tom Goldstein

CoRR, 2019

Strong Baseline Defenses Against Clean-Label Poisoning Attacks.

Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson

CoRR, 2019

FreeLB: Enhanced Adversarial Training for Language Understanding.

Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu

CoRR, 2019

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets.

Chen Zhu, W. Ronny Huang, Ali Shafahi, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein

CoRR, 2019

Understanding the (un)interpretability of natural image distributions using generative models.

Ryen Krusinga, Sohil Shah, Matthias Zwicker, Tom Goldstein, David W. Jacobs

CoRR, 2019

Improving Channel Charting with Representation -Constrained Autoencoders.

Pengzhi Huang, Oscar Castañeda, Emre Gönültas, Saïd Medjkouh, Olav Tirkkonen, Tom Goldstein, Christoph Studer

Proceedings of the 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2019

Adversarial training for free!

Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein

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

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets.

Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein

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

Are adversarial examples inevitable?

Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein

Proceedings of the 7th International Conference on Learning Representations, 2019

ACE: Adapting to Changing Environments for Semantic Segmentation.

Zuxuan Wu, Xin Wang, Joseph Gonzalez, Tom Goldstein, Larry Davis

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Batch-wise Logit-Similarity: Generalizing Logit-Squeezing and Label-Smoothing.

Ali Shafahi, Amin Ghiasi, Mahyar Najibi, Furong Huang, John P. Dickerson, Tom Goldstein

Proceedings of the 30th British Machine Vision Conference 2019, 2019

Siamese Neural Networks for Wireless Positioning and Channel Charting.

Eric Lei, Oscar Castañeda, Olav Tirkkonen, Tom Goldstein, Christoph Studer

Proceedings of the 57th Annual Allerton Conference on Communication, 2019

Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems.

Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

PhaseMax: Convex Phase Retrieval via Basis Pursuit.

Tom Goldstein, Christoph Studer

IEEE Trans. Inf. Theory, 2018

VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems.

Oscar Castañeda, Tom Goldstein, Christoph Studer

IEEE Trans. Circuits Syst. I Regul. Pap., 2018

Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability.

Soumyadip Sengupta, Hao Zhou, Walter Forkel, Ronen Basri, Tom Goldstein, David W. Jacobs

J. Math. Imaging Vis., 2018

Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation.

Sohil Shah, Pallabi Ghosh, Larry S. Davis, Tom Goldstein

CoRR, 2018

Channel Charting: Locating Users Within the Radio Environment Using Channel State Information.

Christoph Studer, Said Medjkouh, Emre Gönültas, Tom Goldstein, Olav Tirkkonen

IEEE Access, 2018

Challenges for Machine Learning on Distributed Platforms (Invited Talk).

Tom Goldstein

Proceedings of the 32nd International Symposium on Distributed Computing, 2018

Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks.

Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein

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

Visualizing the Loss Landscape of Neural Nets.

Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein

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

VLSI Design of a 3-bit Constant-Modulus Precoder for Massive MU-MIMO.

Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer

Proceedings of the IEEE International Symposium on Circuits and Systems, 2018

Linear Spectral Estimators and an Application to Phase Retrieval.

Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer

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

Stabilizing Adversarial Nets with Prediction Methods.

Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein

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

Unsupervised Charting of Wireless Channels.

Said Medjkouh, Emre Gönültas, Tom Goldstein, Olav Tirkkonen, Christoph Studer

Proceedings of the IEEE Global Communications Conference, 2018

DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation.

Zuxuan Wu, Xintong Han, Yen-Liang Lin, Mustafa Gökhan Uzunbas, Tom Goldstein, Ser-Nam Lim, Larry S. Davis

Proceedings of the Computer Vision - ECCV 2018, 2018

PhaseLin: Linear phase retrieval.

Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer

Proceedings of the 52nd Annual Conference on Information Sciences and Systems, 2018

2017

Quantized Precoding for Massive MU-MIMO.

Sven Jacobsson, Giuseppe Durisi, Mikael Coldrey, Tom Goldstein, Christoph Studer

IEEE Trans. Commun., 2017

Compressive Video Sensing: Algorithms, architectures, and applications.

Richard G. Baraniuk, Thomas A. Goldstein, Aswin C. Sankaranarayanan, Christoph Studer, Ashok Veeraraghavan, Michael B. Wakin

IEEE Signal Process. Mag., 2017

Decentralized Baseband Processing for Massive MU-MIMO Systems.

Kaipeng Li, Rishi Sharan, Yujun Chen, Tom Goldstein, Joseph R. Cavallaro, Christoph Studer

IEEE J. Emerg. Sel. Topics Circuits Syst., 2017

1-bit Massive MU-MIMO Precoding in VLSI.

Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Mikael Coldrey, Tom Goldstein, Christoph Studer

IEEE J. Emerg. Sel. Topics Circuits Syst., 2017

Visualizing the Loss Landscape of Neural Nets.

Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein

CoRR, 2017

PhasePack User Guide.

Rohan Chandra, Ziyuan Zhong, Justin Hontz, Val McCulloch, Christoph Studer, Tom Goldstein

CoRR, 2017

Training Quantized Nets: A Deeper Understanding.

Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein

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

FPGA design of low-complexity joint channel estimation and data detection for large SIMO wireless systems.

Oscar Castañeda, Tom Goldstein, Christoph Studer

Proceedings of the IEEE International Symposium on Circuits and Systems, 2017

Convex Phase Retrieval without Lifting via PhaseMax.

Tom Goldstein, Christoph Studer

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

Adaptive Consensus ADMM for Distributed Optimization.

Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein

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

Son of Zorn's lemma: Targeted style transfer using instance-aware semantic segmentation.

Carlos Domingo Castillo, Soham De, Xintong Han, Bharat Singh, Abhay Kumar Yadav, Tom Goldstein

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

POKEMON: A non-linear beamforming algorithm for 1-bit massive MIMO.

Oscar Castañeda, Tom Goldstein, Christoph Studer

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

A New Rank Constraint on Multi-view Fundamental Matrices, and Its Application to Camera Location Recovery.

Soumyadip Sengupta, Tal Amir, Meirav Galun, Tom Goldstein, David W. Jacobs, Amit Singer, Ronen Basri

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

Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation.

Zheng Xu, Mário A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein

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

Automated Inference with Adaptive Batches.

Soham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein

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

Adaptive ADMM with Spectral Penalty Parameter Selection.

Zheng Xu, Mário A. T. Figueiredo, Tom Goldstein

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

PhasePack: A phase retrieval library.

Rohan Chandra, Ziyuan Zhong, Justin Hontz, Val McCulloch, Christoph Studer, Tom Goldstein

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

Scalable Classifiers with ADMM and Transpose Reduction.

Gavin Taylor, Zheng Xu, Tom Goldstein

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

2016

Data Detection in Large Multi-Antenna Wireless Systems via Approximate Semidefinite Relaxation.

Oscar Castañeda, Tom Goldstein, Christoph Studer

IEEE Trans. Circuits Syst. I Regul. Pap., 2016

Biconvex Relaxation for Semidefinite Programming in Computer Vision.

Sohil Shah, Abhay Kumar Yadav, David W. Jacobs, Christoph Studer, Tom Goldstein

CoRR, 2016

Big Batch SGD: Automated Inference using Adaptive Batch Sizes.

Soham De, Abhay Kumar Yadav, David W. Jacobs, Tom Goldstein

CoRR, 2016

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

Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein

CoRR, 2016

An Empirical Study of ADMM for Nonconvex Problems.

Zheng Xu, Soham De, Mário A. T. Figueiredo, Christoph Studer, Tom Goldstein

CoRR, 2016

Deterministic Column Sampling for Low-Rank Matrix Approximation: Nyström vs. Incomplete Cholesky Decomposition.

Raajen Patel, Tom Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk

Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

FPGA design of approximate semidefinite relaxation for data detection in large MIMO wireless systems.

Oscar Castañeda, Tom Goldstein, Christoph Studer

Proceedings of the IEEE International Symposium on Circuits and Systems, 2016

Training Neural Networks Without Gradients: A Scalable ADMM Approach.

Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein

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

Dealbreaker: A Nonlinear Latent Variable Model for Educational Data.

Andrew S. Lan, Tom Goldstein, Richard G. Baraniuk, Christoph Studer

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

Efficient Distributed SGD with Variance Reduction.

Soham De, Tom Goldstein

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Decentralized beamforming for massive MU-MIMO on a GPU cluster.

Kaipeng Li, Riski Skaran, Yujun Chen, Joseph R. Cavallaro, Tom Goldstein, Christoph Studer

Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

Biconvex Relaxation for Semidefinite Programming in Computer Vision.

Sohil Shah, Abhay Kumar Yadav, Carlos Domingo Castillo, David W. Jacobs, Christoph Studer, Tom Goldstein

Proceedings of the Computer Vision - ECCV 2016, 2016

ShapeFit and ShapeKick for Robust, Scalable Structure from Motion.

Thomas A. Goldstein, Paul Hand, Choongbum Lee, Vladislav Voroninski, Stefano Soatto

Proceedings of the Computer Vision - ECCV 2016, 2016

Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity.

Sohil Shah, Tom Goldstein, Christoph Studer

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction.

Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Decentralized data detection for massive MU-MIMO on a Xeon Phi cluster.

Kaipeng Li, Yujun Chen, Rishi Sharan, Tom Goldstein, Joseph R. Cavallaro, Christoph Studer

Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

Nonlinear 1-bit precoding for massive MU-MIMO with higher-order modulation.

Sven Jacobsson, Giuseppe Durisi, Mikael Coldrey, Tom Goldstein, Christoph Studer

Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

2015

The STOne Transform: Multi-Resolution Image Enhancement and Compressive Video.

Tom Goldstein, Lina Xu, Kevin F. Kelly, Richard G. Baraniuk

IEEE Trans. Image Process., 2015

oASIS: Adaptive Column Sampling for Kernel Matrix Approximation.

Raajen Patel, Thomas A. Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk

CoRR, 2015

FASTA: A Generalized Implementation of Forward-Backward Splitting.

Tom Goldstein, Christoph Studer, Richard G. Baraniuk

CoRR, 2015

Self-Expressive Decompositions for Matrix Approximation and Clustering.

Eva L. Dyer, Tom Goldstein, Raajen Patel, Konrad P. Körding, Richard G. Baraniuk

CoRR, 2015

Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction.

Soham De, Gavin Taylor, Tom Goldstein

CoRR, 2015

Variance Reduction for Distributed Stochastic Gradient Descent.

Soham De, Gavin Taylor, Tom Goldstein

CoRR, 2015

Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing.

Tom Goldstein, Min Li, Xiaoming Yuan

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Layer-Specific Adaptive Learning Rates for Deep Networks.

Bharat Singh, Soham De, Yangmuzi Zhang, Thomas A. Goldstein, Gavin Taylor

Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015

Exploiting Low-rank Structure for Discriminative Sub-categorization.

Zheng Xu, Xue Li, Kuiyuan Yang, Thomas A. Goldstein

Proceedings of the British Machine Vision Conference 2015, 2015

2014

Fast Alternating Direction Optimization Methods.

Tom Goldstein, Brendan O'Donoghue, Simon Setzer, Richard G. Baraniuk

SIAM J. Imaging Sci., 2014

Democratic Representations.

Christoph Studer, Tom Goldstein, Wotao Yin, Richard G. Baraniuk

CoRR, 2014

A Field Guide to Forward-Backward Splitting with a FASTA Implementation.

Tom Goldstein, Christoph Studer, Richard G. Baraniuk

CoRR, 2014

Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit.

Ali Ayremlou, Thomas A. Goldstein, Ashok Veeraraghavan, Richard G. Baraniuk

CoRR, 2014

2013

The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video.

Tom Goldstein, Lina Xu, Kevin F. Kelly, Richard G. Baraniuk

CoRR, 2013

Adaptive step size selection for optimization via the ski rental problem.

Amirali Aghazadeh, Ali Ayremlou, Daniel D. Calderon, Tom Goldstein, Raajen Patel, Divyanshu Vats, Richard G. Baraniuk

Proceedings of the IEEE International Conference on Acoustics, 2013

2010

Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction.

Tom Goldstein, Xavier Bresson, Stanley J. Osher

J. Sci. Comput., 2010

2009

The Split Bregman Method for L1-Regularized Problems.

Tom Goldstein, Stanley J. Osher

SIAM J. Imaging Sci., 2009

2004

Perceptual speech quality assessment in acoustic and binaural applications.

Tom Goldstein, Antony W. Rix

Proceedings of the 2004 IEEE International Conference on Acoustics, 2004