Ludwig Schmidt

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