Ludwig Schmidt

46

Papers

301

Total Citations

Papers (46)

Data Filtering Networks

On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks

NeurIPS 2017arXiv

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation

Fast recovery from a union of subspaces

Predicting With Confidence on Unseen Distributions

Do Image Classifiers Generalize Across Time?

Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images

Reproducible Scaling Laws for Contrastive Language-Image Learning

Differentially Private Learning of Structured Discrete Distributions

Communication-Efficient Distributed Learning of Discrete Distributions

Robust Fine-Tuning of Zero-Shot Models

Practical and Optimal LSH for Angular Distance

Objaverse: A Universe of Annotated 3D Objects

CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation

Contrasting Contrastive Self-Supervised Representation Learning Pipelines

Improving multimodal datasets with image captioning

Does progress on ImageNet transfer to real-world datasets?

VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models

DataComp: In search of the next generation of multimodal datasets

Objaverse-XL: A Universe of 10M+ 3D Objects

GenEval: An object-focused framework for evaluating text-to-image alignment

Benchmarking Distribution Shift in Tabular Data with TableShift

Are aligned neural networks adversarially aligned?

Neural Priming for Sample-Efficient Adaptation

On the Connection between Pre-training Data Diversity and Fine-tuning Robustness

Effective Robustness against Natural Distribution Shifts for Models with Different Training Data

A Nearly-Linear Time Framework for Graph-Structured Sparsity

Fast Algorithms for Segmented Regression

On the Limitations of First-Order Approximation in GAN Dynamics

A Classification-Based Study of Covariate Shift in GAN Distributions

Exploring the Landscape of Spatial Robustness

Do ImageNet Classifiers Generalize to ImageNet?

Adversarially Robust Generalization Requires More Data

Unlabeled Data Improves Adversarial Robustness

Model Similarity Mitigates Test Set Overuse

A Meta-Analysis of Overfitting in Machine Learning

Measuring Robustness to Natural Distribution Shifts in Image Classification

Retiring Adult: New Datasets for Fair Machine Learning

Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation

Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP

LAION-5B: An open large-scale dataset for training next generation image-text models

Patching open-vocabulary models by interpolating weights

Characterizing the Impacts of Semi-supervised Learning for Weak Supervision

Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text

Stable and low-precision training for large-scale vision-language models