Micah Goldblum

According to our database, Micah Goldblum authored at least 94 papers between 2019 and 2024.

Collaborative distances:

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

2024

Measuring Style Similarity in Diffusion Models.

Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, Tom Goldstein

CoRR, 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

TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks.

Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White

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

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

Non-Vacuous Generalization Bounds for Large Language Models.

Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson

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

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

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

The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning.

Micah Goldblum, Marc Finzi, Keefer Rowan, Andrew Gordon Wilson

CoRR, 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

Simplifying Neural Network Training Under Class Imbalance.

Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson

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

When Do Neural Nets Outperform Boosted Trees on Tabular Data?

Duncan C. McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C., Ganesh Ramakrishnan, Micah Goldblum, Colin White

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

Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition.

Samuel Dooley, Rhea Sukthanker, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum

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

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

The Lie Derivative for Measuring Learned Equivariance.

Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson

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

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

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

On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition.

Rhea Sukthanker, Samuel Dooley, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum

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 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

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers.

Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson

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

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors.

Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson

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

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization.

Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson

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

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

Bayesian Model Selection, the Marginal Likelihood, and Generalization.

Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson

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

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

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

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

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

Identification of Attack-Specific Signatures in Adversarial Examples.

Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau, Micah Goldblum, Rama Chellappa

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

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

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

Avi Schwarzschild, Arjun Gupta, Micah Goldblum, Tom Goldstein

CoRR, 2021

Technical Challenges for Training Fair Neural Networks.

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

CoRR, 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

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

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

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

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

2020

Adversarial Robustness and Robust Meta-Learning for Neural Networks.

Micah Goldblum

PhD thesis, 2020

Analyzing the Machine Learning Conference Review Process.

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

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

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

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

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

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

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

Adversarially Robust Distillation.

Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein

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

2019

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

Micah Goldblum, Liam Fowl, Tom Goldstein

CoRR, 2019