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Ludwig Schmidt

73 papers · 2015–2026 · 11 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13) πŸ—ΊοΈ Taxonomy Completionist (15) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (10) πŸƒ Academic Marathon (10) 🏠 Conference Loyalist (37) πŸ”¬ Deep Specialist (16) πŸ‘₯ Mega-Team (60) πŸ† Keyword Champion (13) 🀝 Dynamic Duo (15) πŸ‘‘ Triple Crown 🧬 Topic Evolution πŸ—ƒοΈ Keyword Collector (278) ❓ The Questioner (7) ⚑ Prolific Year (8) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (72) πŸ”₯ Unstoppable (11)

Conferences

NIPS (37) ICML (12) CVPR (5) ICLR (5) EMNLP (4) ICCV (4) COLT (2) AISTATS (1) EACL (1) ECCV (1) IJCAI (1)

Top co-authors

Mitchell Wortsman (15) Gabriel Ilharco (14) Vaishaal Shankar (13) Alex Fang (10) Ali Farhadi (10) Benjamin Recht (8) Samir Yitzhak Gadre (8) Sewoong Oh (7) Yair Carmon (7) Thao Nguyen (7)

Research topics

Statistics (1)

Keywords

distribution shift (13) vision-language model (10) multimodal learning (7) vision language model (6) contrastive learning (6) domain generalization (6) zero-shot learning (6) image classification (6) benchmark evaluation (5) transfer learning (5) large language model (5) sample complexity (4) image-text pair (4) question answering (3) tabular datum (3) data augmentation (3) clip model (3) language model (3) domain adaptation (3) visual question answering (3)

Papers

Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pre-training EACL 2026
Language models scale reliably with over-training and on downstream tasks ICLR 2025
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation CVPR 2025
Data or Language Supervision: What Makes CLIP Better than DINO? EMNLP 2025
Should VLMs be Pre-trained with Image Data? ICLR 2025
A Sober Look at the Robustness of CLIPs to Spurious Features NIPS 2024
Data Filtering Networks ICLR 2024
DataComp-LM: In search of the next generation of training sets for language models NIPS 2024
MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens NIPS 2024
Large Scale Transfer Learning for Tabular Data via Language Modeling NIPS 2024
Why are Visually-Grounded Language Models Bad at Image Classification? NIPS 2024
Multilingual Diversity Improves Vision-Language Representations NIPS 2024
Resolving Discrepancies in Compute-Optimal Scaling of Language Models NIPS 2024
Getting it Right: Improving Spatial Consistency in Text-to-Image Models ECCV 2024
Better Alignment with Instruction Back-and-Forth Translation EMNLP 2024
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text NIPS 2023
Stable and low-precision training for large-scale vision-language models NIPS 2023
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images ICCV 2023
Editing models with task arithmetic ICLR 2023
Improving multimodal datasets with image captioning NIPS 2023
Does progress on ImageNet transfer to real-world datasets? NIPS 2023
VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models NIPS 2023
DataComp: In search of the next generation of multimodal datasets NIPS 2023
Objaverse-XL: A Universe of 10M+ 3D Objects NIPS 2023
GenEval: An object-focused framework for evaluating text-to-image alignment NIPS 2023
Benchmarking Distribution Shift in Tabular Data with TableShift NIPS 2023
Are aligned neural networks adversarially aligned? NIPS 2023
Neural Priming for Sample-Efficient Adaptation NIPS 2023
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness NIPS 2023
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data NIPS 2023
Reproducible Scaling Laws for Contrastive Language-Image Learning CVPR 2023
Objaverse: A Universe of Annotated 3D Objects CVPR 2023
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation CVPR 2023
Characterizing the Impacts of Semi-supervised Learning for Weak Supervision NIPS 2023
Measuring and Narrowing the Compositionality Gap in Language Models EMNLP 2023
LAION-5B: An open large-scale dataset for training next generation image-text models NIPS 2022
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time ICML 2022
Exploring The Landscape of Distributional Robustness for Question Answering Models EMNLP 2022
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP) ICML 2022
Robust Fine-Tuning of Zero-Shot Models CVPR 2022
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation NIPS 2022
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP NIPS 2022
Patching open-vocabulary models by interpolating weights NIPS 2022
Do Image Classifiers Generalize Across Time? ICCV 2021
Retiring Adult: New Datasets for Fair Machine Learning NIPS 2021
Contrasting Contrastive Self-Supervised Representation Learning Pipelines ICCV 2021
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization ICML 2021
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning NIPS 2021
Predicting With Confidence on Unseen Distributions ICCV 2021
Measuring Robustness to Natural Distribution Shifts in Image Classification NIPS 2020
The Effect of Natural Distribution Shift on Question Answering Models ICML 2020
Neural Kernels Without Tangents ICML 2020
Evaluating Machine Accuracy on ImageNet ICML 2020
A Meta-Analysis of Overfitting in Machine Learning NIPS 2019
Do ImageNet Classifiers Generalize to ImageNet? ICML 2019
Exploring the Landscape of Spatial Robustness ICML 2019
Model Similarity Mitigates Test Set Overuse NIPS 2019
Unlabeled Data Improves Adversarial Robustness NIPS 2019
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians AISTATS 2018
Towards Deep Learning Models Resistant to Adversarial Attacks ICLR 2018
Adversarially Robust Generalization Requires More Data NIPS 2018
On the Limitations of First-Order Approximation in GAN Dynamics ICML 2018
A Classification-Based Study of Covariate Shift in GAN Distributions ICML 2018
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms COLT 2018
Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities COLT 2017
Communication-Efficient Distributed Learning of Discrete Distributions NIPS 2017
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks NIPS 2017
Fast Algorithms for Segmented Regression ICML 2016
Fast recovery from a union of subspaces NIPS 2016
A Nearly-Linear Time Framework for Graph-Structured Sparsity IJCAI 2016
Practical and Optimal LSH for Angular Distance NIPS 2015
Differentially Private Learning of Structured Discrete Distributions NIPS 2015
A Nearly-Linear Time Framework for Graph-Structured Sparsity ICML 2015