← Authors

Micah Goldblum

63 papers · 2020–2025 · 8 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (8) πŸƒ Academic Marathon (5) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (13) 🏠 Conference Loyalist (24) πŸ† Grand Slam πŸ‘‘ Triple Crown 🀝 Dynamic Duo (44) 🧬 Topic Evolution πŸ† Keyword Champion (2) ⚑ Prolific Year (18) πŸ—ƒοΈ Keyword Collector (161) ❓ The Questioner (9) πŸ”₯ Unstoppable (6) πŸ’Ž Century Club (63)

Conferences

NIPS (24) ICLR (21) ICML (11) AAAI (2) CVPR (2) EACL (1) ECCV (1) NAACL (1)

Top co-authors

Tom Goldstein (44) Jonas Geiping (21) Andrew Gordon Wilson (10) Avi Schwarzschild (9) Gowthami Somepalli (8) Liam Fowl (7) Arpit Bansal (7) Yuxin Wen (6) Valeriia Cherepanova (6) Andrew G Wilson (6)

Research topics

Privacy (1)

Keywords

neural network (9) data poisoning (4) data augmentation (4) large language model (4) few-shot learning (3) diffusion model (3) adversarial robustness (3) adversarial training (3) in-context learning (3) transfer learning (3) image classification (3) generalization bound (2) generative model (2) uncertainty estimation (2) vision transformer (2) backdoor attack (2) bayesian inference (2) domain generalization (2) model compression (2) neural architecture search (2)

Papers

LiveBench: A Challenging, Contamination-Limited LLM Benchmark ICLR 2025
Adaptive Retention & Correction: Test-Time Training for Continual Learning ICLR 2025
Hidden No More: Attacking and Defending Private Third-Party LLM Inference ICML 2025
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking ICLR 2025
LLM-Generated Passphrases That Are Secure and Easy to Remember NAACL 2025
Large Language Models Must Be Taught to Know What They Don’t Know NIPS 2024
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text ICML 2024
Non-Vacuous Generalization Bounds for Large Language Models ICML 2024
Calibration-Tuning: Teaching Large Language Models to Know What They Don’t Know EACL 2024
Investigating Style Similarity in Diffusion Models ECCV 2024
Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning ICML 2024
Compute Better Spent: Replacing Dense Layers with Structured Matrices ICML 2024
Universal Guidance for Diffusion Models ICLR 2024
NEFTune: Noisy Embeddings Improve Instruction Finetuning ICLR 2024
On the Reliability of Watermarks for Large Language Models ICLR 2024
Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices NIPS 2024
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models NIPS 2024
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks NIPS 2024
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks NIPS 2023
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models ICLR 2023
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization ICLR 2023
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation ICLR 2023
Transfer Learning with Deep Tabular Models ICLR 2023
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness ICLR 2023
The Lie Derivative for Measuring Learned Equivariance ICLR 2023
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries ICLR 2023
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent ICLR 2023
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise NIPS 2023
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning NIPS 2023
Understanding and Mitigating Copying in Diffusion Models NIPS 2023
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery NIPS 2023
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition NIPS 2023
What Can We Learn from Unlearnable Datasets? NIPS 2023
When Do Neural Nets Outperform Boosted Trees on Tabular Data? NIPS 2023
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models CVPR 2023
Simplifying Neural Network Training Under Class Imbalance NIPS 2023
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers NIPS 2022
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability NIPS 2022
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch NIPS 2022
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking NIPS 2022
Autoregressive Perturbations for Data Poisoning NIPS 2022
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors NIPS 2022
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization NIPS 2022
Towards Transferable Adversarial Attacks on Vision Transformers AAAI 2022
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent From the Decision Boundary Perspective CVPR 2022
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models ICLR 2022
Stochastic Training is Not Necessary for Generalization ICLR 2022
The Close Relationship Between Contrastive Learning and Meta-Learning ICLR 2022
The Uncanny Similarity of Recurrence and Depth ICLR 2022
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations ICML 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization ICML 2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification ICML 2022
Encoding Robustness to Image Style via Adversarial Feature Perturbations NIPS 2021
The Intrinsic Dimension of Images and Its Impact on Learning ICLR 2021
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition ICLR 2021
Data Augmentation for Meta-Learning ICML 2021
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks NIPS 2021
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks ICML 2021
Adversarial Examples Make Strong Poisons NIPS 2021
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks ICML 2020
Truth or backpropaganda? An empirical investigation of deep learning theory ICLR 2020
Adversarially Robust Distillation AAAI 2020
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach NIPS 2020