Micah Goldblum
- Papers
- 32
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Authored papers
32Vista4D: Video Reshooting with 4D Point Clouds
arXiv 2026
Closing the Train-Test Gap in World Models for Gradient-Based Planning
arXiv 2025
Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence
arXiv 2025
Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
arXiv 2025
Gemstones: A Model Suite for Multi-Faceted Scaling Laws
arXiv 2025
LiveBench: A Challenging, Contamination-Limited LLM Benchmark
arXiv 2024
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text
arXiv 2024
Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models
arXiv 2024
Measuring Style Similarity in Diffusion Models
arXiv 2024
Large Language Models Must Be Taught to Know What They Don't Know
arXiv 2024
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
arXiv 2024
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
arXiv 2024
Compute Better Spent: Replacing Dense Layers with Structured Matrices
arXiv 2024
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking
arXiv 2024
NEFTune: Noisy Embeddings Improve Instruction Finetuning
arXiv 2023
On the Reliability of Watermarks for Large Language Models
arXiv 2023
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
NeurIPS 2023 11
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
hard-prompts-made-easy-gradient-based
When Do Neural Nets Outperform Boosted Trees on Tabular Data?
when-do-neural-nets-outperform-boosted-trees
Understanding and Mitigating Copying in Diffusion Models
understanding-and-mitigating-copying-in
Non-Vacuous Generalization Bounds for Large Language Models
arXiv 2023
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models
arXiv 2023
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
arXiv 2023
Universal Guidance for Diffusion Models
arXiv 2023
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
cold-diffusion-inverting-arbitrary-image
What do Vision Transformers Learn? A Visual Exploration
arXiv 2022
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
arXiv 2022
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
NeurIPS 2023 11
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
plug-in-inversion-model-agnostic-inversion
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
saint-improved-neural-networks-for-tabular-1
Datasets for Studying Generalization from Easy to Hard Examples
arXiv 2021
Stochastic Training is Not Necessary for Generalization
stochastic-training-is-not-necessary-for-1
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