Micah Goldblum

ORCID

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 146

Top Venues

Publications

Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Universal Guidance for Diffusion Models. ICLR (2024)
Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum
LiveBench: A Challenging, Contamination-Free LLM Benchmark. CoRR (2024)
Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks. CoRR (2024)
Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. CoRR (2024)
Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine M. Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson
Large Language Models Must Be Taught to Know What They Don't Know. CoRR (2024)
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
NEFTune: Noisy Embeddings Improve Instruction Finetuning. ICLR (2024)
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein
On the Reliability of Watermarks for Large Language Models. ICLR (2024)
Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau, Micah Goldblum, Rama Chellappa
Identifying Attack-Specific Signatures in Adversarial Examples. ICASSP (2024)
Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal, C. Bayan Bruss, Yann LeCun, Andrew Gordon Wilson
Just How Flexible are Neural Networks in Practice? CoRR (2024)
Sanae Lotfi, Yilun Kuang, Brandon Amos, Micah Goldblum, Marc Finzi, Andrew Gordon Wilson
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models. CoRR (2024)
Haoran Chen, Micah Goldblum, Zuxuan Wu, Yu-Gang Jiang
Adaptive Rentention & Correction for Continual Learning. CoRR (2024)
Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text. CoRR (2024)
Shikai Qiu, Andres Potapczynski, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson
Compute Better Spent: Replacing Dense Layers with Structured Matrices. CoRR (2024)
Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, Tom Goldstein
Measuring Style Similarity in Diffusion Models. CoRR (2024)
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models. CVPR (2023)
Duncan C. McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C., Ganesh Ramakrishnan, Micah Goldblum, Colin White
When Do Neural Nets Outperform Boosted Trees on Tabular Data? CoRR (2023)
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein
Understanding and Mitigating Copying in Diffusion Models. CoRR (2023)
Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. ICLR (2023)
Duncan C. McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C., Ganesh Ramakrishnan, Micah Goldblum, Colin White
When Do Neural Nets Outperform Boosted Trees on Tabular Data? NeurIPS (2023)
Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. CoRR (2023)
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein
On the Reliability of Watermarks for Large Language Models. CoRR (2023)
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
Transfer Learning with Deep Tabular Models. ICLR (2023)
Khalid Saifullah, Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein
Seeing in Words: Learning to Classify through Language Bottlenecks. Tiny Papers @ ICLR (2023)
Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein
What Can We Learn from Unlearnable Datasets? CoRR (2023)
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
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. CoRR (2023)
Micah Goldblum, Marc Finzi, Keefer Rowan, Andrew Gordon Wilson
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning. CoRR (2023)
Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson
Non-Vacuous Generalization Bounds for Large Language Models. CoRR (2023)
Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein
STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning. ICASSP (2023)
Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. ICLR (2023)
Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson
The Lie Derivative for Measuring Learned Equivariance. ICLR (2023)
Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. CoRR (2023)
Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
Simplifying Neural Network Training Under Class Imbalance. NeurIPS (2023)
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery. CoRR (2023)
Khalid Saifullah, Yuxin Wen, Jonas Geiping, Micah Goldblum, Tom Goldstein
Seeing in Words: Learning to Classify through Language Bottlenecks. CoRR (2023)
Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. ICLR (2023)
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Universal Guidance for Diffusion Models. CVPR Workshops (2023)
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
A Cookbook of Self-Supervised Learning. CoRR (2023)
Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson
Perspectives on the State and Future of Deep Learning - 2023. CoRR (2023)
Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. ICLR (2023)
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein
Baseline Defenses for Adversarial Attacks Against Aligned Language Models. CoRR (2023)
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery. NeurIPS (2023)
Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. NeurIPS (2023)
Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, John P. Dickerson, Micah Goldblum, Tom Goldstein
A Deep Dive into Dataset Imbalance and Bias in Face Identification. AIES (2023)
Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein
What Can We Learn from Unlearnable Datasets? NeurIPS (2023)
Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
Simplifying Neural Network Training Under Class Imbalance. CoRR (2023)
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
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. NeurIPS (2023)
Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang, Larry S. Davis
Towards Transferable Adversarial Attacks on Image and Video Transformers. IEEE Trans. Image Process. 32 (2023)
Vasu Singla, Pedro Sandoval Segura, Micah Goldblum, Jonas Geiping, Tom Goldstein
A Simple and Efficient Baseline for Data Attribution on Images. CoRR (2023)
Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. ICLR (2023)
Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. NeurIPS (2023)
Liam H. Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. ICLR (2023)
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Universal Guidance for Diffusion Models. CoRR (2023)
Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models. CoRR (2023)
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
NEFTune: Noisy Embeddings Improve Instruction Finetuning. CoRR (2023)
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein
Understanding and Mitigating Copying in Diffusion Models. NeurIPS (2023)
Samuel Dooley, Rhea Sukthanker, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition. NeurIPS (2023)
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45 (2) (2023)
Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang
Towards Transferable Adversarial Attacks on Vision Transformers. AAAI (2022)
Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson
Bayesian Model Selection, the Marginal Likelihood, and Generalization. CoRR (2022)
Jonas Geiping, Micah Goldblum, Phillip Pope, Michael Moeller, Tom Goldstein
Stochastic Training is Not Necessary for Generalization. ICLR (2022)
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. CoRR (2022)
Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CVPR (2022)
Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. CoRR (2022)
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models. CoRR (2022)
Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. NeurIPS (2022)
Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson
Bayesian Model Selection, the Marginal Likelihood, and Generalization. ICML (2022)
Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. CoRR (2022)
Liam H. Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, Tom Goldstein
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. ICLR (2022)
Yuxin Wen, Jonas Geiping, Liam Fowl, Hossein Souri, Rama Chellappa, Micah Goldblum, Tom Goldstein
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning. CoRR (2022)
Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking. NeurIPS (2022)
Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein
What do Vision Transformers Learn? A Visual Exploration. CoRR (2022)
Liam Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojtek Czaja, Micah Goldblum, Tom Goldstein
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. CoRR (2022)
Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. NeurIPS (2022)
Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David Jacobs
Autoregressive Perturbations for Data Poisoning. NeurIPS (2022)
Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein
The Close Relationship Between Contrastive Learning and Meta-Learning. ICLR (2022)
Rhea Sukthanker, Samuel Dooley, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum
On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition. CoRR (2022)
Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein
Poisons that are learned faster are more effective. CVPR Workshops (2022)
Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. CoRR (2022)
Renkun Ni, Ping-yeh Chiang, Jonas Geiping, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein
K-SAM: Sharpness-Aware Minimization at the Speed of SGD. CoRR (2022)
Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. CoRR (2022)
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
Transfer Learning with Deep Tabular Models. CoRR (2022)
Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CoRR (2022)
Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David W. Jacobs
Autoregressive Perturbations for Data Poisoning. CoRR (2022)
Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein
The Uncanny Similarity of Recurrence and Depth. ICLR (2022)
Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. CoRR (2022)
Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein
Poisons that are learned faster are more effective. CoRR (2022)
Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. NeurIPS (2022)
Nate Gruver, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson
The Lie Derivative for Measuring Learned Equivariance. CoRR (2022)
Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. CoRR (2022)
Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. CoRR (2022)
Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. ICML (2022)
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew G. Wilson
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. NeurIPS (2022)
Yuxin Wen, Jonas Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. ICML (2022)
Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. NeurIPS (2022)
Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking. CoRR (2022)
Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, Micah Goldblum, Tom Goldstein
A Deep Dive into Dataset Imbalance and Bias in Face Identification. CoRR (2022)
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. NeurIPS (2021)
Liam Fowl, Jonas Geiping, Wojtek Czaja, Micah Goldblum, Tom Goldstein
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. CoRR (2021)
Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein
The Intrinsic Dimension of Images and Its Impact on Learning. CoRR (2021)
Jonas Geiping, Micah Goldblum, Phillip E. Pope, Michael Moeller, Tom Goldstein
Stochastic Training is Not Necessary for Generalization. CoRR (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Micah Goldblum