ML Anthology

Goldblum, Micah

91 publications

ICLR 2025 Adaptive Retention & Correction: Test-Time Training for Continual Learning Haoran Chen, Micah Goldblum, Zuxuan Wu, Yu-Gang Jiang
NeurIPS 2025 Far from the Shallow: Brain-Predictive Reasoning Embedding Through Residual Disentanglement Linyang He, Tianjun Zhong, Richard Antonello, Gavin Mischler, Micah Goldblum, Nima Mesgarani
NeurIPS 2025 FineGRAIN: Evaluating Failure Modes of Text-to-Image Models with Vision Language Model Judges Kevin David Hayes, Micah Goldblum, Vikash Sehwag, Gowthami Somepalli, Ashwinee Panda, Tom Goldstein
NeurIPS 2025 Gemstones: A Model Suite for Multi-Faceted Scaling Laws Sean Michael McLeish, John Kirchenbauer, David Yu Miller, Siddharth Singh, Abhinav Bhatele, Micah Goldblum, Ashwinee Panda, Tom Goldstein
ICML 2025 Hidden No More: Attacking and Defending Private Third-Party LLM Inference Rahul Krishna Thomas, Louai Zahran, Erica Choi, Akilesh Potti, Micah Goldblum, Arka Pal
ICLRW 2025 Hidden No More: Attacking and Defending Private Third-Party LLM Inference Arka Pal, Rahul Krishna Thomas, Louai Zahran, Erica Choi, Akilesh Potti, Micah Goldblum
ICLR 2025 LiveBench: A Challenging, Contamination-Limited LLM Benchmark Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, Shubh-Agrawal, Sandeep Singh Sandha, Siddartha Venkat Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum
NeurIPS 2025 Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful Martin Marek, Sanae Lotfi, Aditya Somasundaram, Andrew Gordon Wilson, Micah Goldblum
ICLR 2025 Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking Benjamin Feuer, Micah Goldblum, Teresa Datta, Sanjana Nambiar, Raz Besaleli, Samuel Dooley, Max Cembalest, John P Dickerson
ICML 2024 Compute Better Spent: Replacing Dense Layers with Structured Matrices Shikai Qiu, Andres Potapczynski, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson
ICMLW 2024 Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam H Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum
ECCV 2024 Investigating Style Similarity in Diffusion Models Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas A. Geiping, Abhinav Shrivastava, Tom Goldstein
NeurIPS 2024 Large Language Models Must Be Taught to Know What They Don’t Know Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson
ICLR 2024 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
ICML 2024 Non-Vacuous Generalization Bounds for Large Language Models Sanae Lotfi, Marc Anton Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson
ICLR 2024 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
ICML 2024 Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning Micah Goldblum, Marc Anton Finzi, Keefer Rowan, Andrew Gordon Wilson
NeurIPSW 2024 Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models Neel Jain, Aditya Shrivastava, Chenyang Zhu, Daben Liu, Alfy Samuel, Ashwinee Panda, Anoop Kumar, Micah Goldblum, Tom Goldstein
NeurIPS 2024 Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Zixi Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Gordon Wilson
ICML 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
NeurIPS 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
ICLR 2024 Universal Guidance for Diffusion Models Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Roni Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
NeurIPS 2024 Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models Sanae Lotfi, Yilun Kuang, Brandon Amos, Micah Goldblum, Marc Finzi, Andrew Gordon Wilson
ICMLW 2024 Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models Sanae Lotfi, Yilun Kuang, Marc Anton Finzi, Brandon Amos, Micah Goldblum, Andrew Gordon Wilson
NeurIPSW 2024 vTune: Verifiable Fine-Tuning Through Backdooring Eva Zhang, Akilesh Potti, Micah Goldblum
NeurIPSW 2024 vTune: Verification of Fine-Tuning Through Backdooring Eva Zhang, Akilesh Potti, Micah Goldblum
NeurIPS 2023 A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew G Wilson, Tom Goldstein, Micah Goldblum
NeurIPSW 2023 A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bruss, Andrew Wilson, Tom Goldstein, Micah Goldblum
NeurIPS 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 G Wilson, Tom Goldstein
ICLR 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
NeurIPS 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
ICLR 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
CVPR 2023 Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein
ICLR 2023 Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang
NeurIPS 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
ICLR 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
ICLR 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
NeurIPSW 2023 Non-Vacuous Generalization Bounds for Large Language Models Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim Rudner, Micah Goldblum, Andrew Wilson
ICLR 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
NeurIPS 2023 Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition Samuel Dooley, Rhea Sukthanker, John Dickerson, Colin White, Frank Hutter, Micah Goldblum
NeurIPS 2023 Simplifying Neural Network Training Under Class Imbalance Ravid Shwartz-Ziv, Micah Goldblum, Yucen Li, C. Bayan Bruss, Andrew G Wilson
ICLR 2023 The Lie Derivative for Measuring Learned Equivariance Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson
ICLR 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
ICMLW 2023 Understanding Data Replication in Diffusion Models Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein
NeurIPS 2023 Understanding and Mitigating Copying in Diffusion Models Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, Tom Goldstein
CVPRW 2023 Universal Guidance for Diffusion Models Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
NeurIPS 2023 What Can We Learn from Unlearnable Datasets? Pedro Sandoval-Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein
NeurIPS 2023 When Do Neural Nets Outperform Boosted Trees on Tabular Data? Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C, Ganesh Ramakrishnan, Micah Goldblum, Colin White
NeurIPSW 2022 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
NeurIPSW 2022 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
NeurIPS 2022 Autoregressive Perturbations for Data Poisoning Pedro Sandoval-Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David Jacobs
ICML 2022 Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi, Pavel Izmailov, Gregory Benton, Micah Goldblum, Andrew Gordon Wilson
CVPR 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 Baraniuk, Micah Goldblum, Tom Goldstein
NeurIPSW 2022 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
NeurIPS 2022 Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew G Wilson
NeurIPSW 2022 DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam H Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein
NeurIPSW 2022 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
NeurIPS 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
ICML 2022 Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen, Jonas A. Geiping, Liam Fowl, Micah Goldblum, Tom Goldstein
ICMLW 2022 How Much Data Is Augmentation Worth? Jonas Geiping, Gowthami Somepalli, Ravid Shwartz-Ziv, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
NeurIPSW 2022 On Representation Learning Under Class Imbalance Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
NeurIPSW 2022 On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition Samuel Dooley, Rhea Sanjay Sukthanker, John P Dickerson, Colin White, Frank Hutter, Micah Goldblum
NeurIPSW 2022 On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition Samuel Dooley, Rhea Sanjay Sukthanker, John P Dickerson, Colin White, Frank Hutter, Micah Goldblum
NeurIPS 2022 PAC-Bayes Compression Bounds so Tight That They Can Explain Generalization Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew G Wilson
NeurIPSW 2022 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
ICML 2022 Plug-in Inversion: Model-Agnostic Inversion for Vision with Data Augmentations Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, Tom Goldstein
CVPRW 2022 Poisons That Are Learned Faster Are More Effective Pedro Sandoval Segura, Vasu Singla, Liam Fowl, Jonas Geiping, Micah Goldblum, David Jacobs, Tom Goldstein
ICMLW 2022 Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Prior Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
NeurIPS 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 G Wilson
ICLR 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
NeurIPSW 2022 SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training Gowthami Somepalli, Avi Schwarzschild, Micah Goldblum, C. Bayan Bruss, Tom Goldstein
NeurIPS 2022 Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein
ICLR 2022 Stochastic Training Is Not Necessary for Generalization Jonas Geiping, Micah Goldblum, Phil Pope, Michael Moeller, Tom Goldstein
ICLR 2022 The Close Relationship Between Contrastive Learning and Meta-Learning Renkun Ni, Manli Shu, Hossein Souri, Micah Goldblum, Tom Goldstein
ICLR 2022 The Uncanny Similarity of Recurrence and Depth Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein
AAAI 2022 Towards Transferable Adversarial Attacks on Vision Transformers Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang
NeurIPSW 2022 Transfer Learning with Deep Tabular Models Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
NeurIPS 2022 Where Do Models Go Wrong? Parameter-Space Saliency Maps for Explainability Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein
NeurIPSW 2021 A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, Tom Goldstein
NeurIPS 2021 Adversarial Examples Make Strong Poisons Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja, Tom Goldstein
NeurIPS 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
ICML 2021 Data Augmentation for Meta-Learning Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein
NeurIPS 2021 Encoding Robustness to Image Style via Adversarial Feature Perturbations Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein
ICML 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
ICLR 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
ICLR 2021 The Intrinsic Dimension of Images and Its Impact on Learning Phil Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein
AAAI 2020 Adversarially Robust Distillation Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein
NeurIPS 2020 Adversarially Robust Few-Shot Learning: A Meta-Learning Approach Micah Goldblum, Liam Fowl, Tom Goldstein
ICLR 2020 Truth or Backpropaganda? an Empirical Investigation of Deep Learning Theory Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein
NeurIPSW 2020 Understanding Generalization Through Visualizations W Ronny Huang, Zeyad Emam, Micah Goldblum, Liam H Fowl, J K Terry, Furong Huang, Tom Goldstein
ICML 2020 Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein