Ludwig Schmidt

According to our database, Ludwig Schmidt authored at least 93 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Do CLIPs Always Generalize Better than ImageNet Models?

Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang

CoRR, 2024

Language models scale reliably with over-training and on downstream tasks.

Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, Suchin Gururangan, Mitchell Wortsman, Rulin Shao, Jean Mercat, Alex Fang, Jeffrey Li, Sedrick Keh, Rui Xin, Marianna Nezhurina, Igor Vasiljevic, Jenia Jitsev, Alexandros G. Dimakis, Gabriel Ilharco, Shuran Song, Thomas Kollar, Yair Carmon, Achal Dave, Reinhard Heckel, Niklas Muennighoff, Ludwig Schmidt

CoRR, 2024

2023

lo-fi: distributed fine-tuning without communication.

Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael G. Rabbat, Ari S. Morcos

Trans. Mach. Learn. Res., 2023

Data Filtering Networks.

Alex Fang, Albin Madappally Jose, Amit Jain, Ludwig Schmidt, Alexander Toshev, Vaishaal Shankar

CoRR, 2023

VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use.

Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, Ludwig Schmidt

CoRR, 2023

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Yitzhak Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, Ludwig Schmidt

CoRR, 2023

Are aligned neural networks adversarially aligned?

Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramèr, Ludwig Schmidt

CoRR, 2023

The Role of Pre-training Data in Transfer Learning.

Rahim Entezari, Mitchell Wortsman, Olga Saukh, Moein Shariatnia, Hanie Sedghi, Ludwig Schmidt

CoRR, 2023

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

Wanrong Zhu, Jack Hessel, Anas Awadalla, Samir Yitzhak Gadre, Jesse Dodge, Alex Fang, Youngjae Yu, Ludwig Schmidt, William Yang Wang, Yejin Choi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Priming for Sample-Efficient Adaptation.

Matthew Wallingford, Vivek Ramanujan, Alex Fang, Aditya Kusupati, Roozbeh Mottaghi, Aniruddha Kembhavi, Ludwig Schmidt, Ali Farhadi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Vivek Ramanujan, Thao Nguyen, Sewoong Oh, Ali Farhadi, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Improving multimodal datasets with image captioning.

Thao Nguyen, Samir Yitzhak Gadre, Gabriel Ilharco, Sewoong Oh, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Characterizing the Impacts of Semi-supervised Learning for Weak Supervision.

Jeffrey Li, Jieyu Zhang, Ludwig Schmidt, Alexander J. Ratner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Dhruba Ghosh, Hannaneh Hajishirzi, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Benchmarking Distribution Shift in Tabular Data with TableShift.

Josh Gardner, Zoran Popovic, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DataComp: In search of the next generation of multimodal datasets.

Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah M. Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander J. Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Does progress on ImageNet transfer to real-world datasets?

Alex Fang, Simon Kornblith, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, Ali Farhadi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Are aligned neural networks adversarially aligned?

Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei Koh, Daphne Ippolito, Florian Tramèr, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Editing models with task arithmetic.

Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Nitzan Bitton Guetta, Yonatan Bitton, Jack Hessel, Ludwig Schmidt, Yuval Elovici, Gabriel Stanovsky, Roy Schwartz

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Measuring and Narrowing the Compositionality Gap in Language Models.

Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, Mike Lewis

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

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

Samir Yitzhak Gadre, Mitchell Wortsman, Gabriel Ilharco, Ludwig Schmidt, Shuran Song

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Objaverse: A Universe of Annotated 3D Objects.

Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, Ali Farhadi

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Reproducible Scaling Laws for Contrastive Language-Image Learning.

Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, Jenia Jitsev

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections.

Mike A. Merrill, Esteban Safranchik, Arinbjörn Kolbeinsson, Piyusha Gade, Ernesto Ramirez, Ludwig Schmidt, Luca Foshchini, Tim Althoff

Proceedings of the Conference on Health, Inference, and Learning, 2023

2022

Editing Models with Task Arithmetic.

Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi

CoRR, 2022

CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration.

Samir Yitzhak Gadre, Mitchell Wortsman, Gabriel Ilharco, Ludwig Schmidt, Shuran Song

CoRR, 2022

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

Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, Jenia Jitsev

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Patching open-vocabulary models by interpolating weights.

Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation.

Josh Gardner, Zoran Popovic, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt

Proceedings of the International Conference on Machine Learning, 2022

Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP).

Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt

Proceedings of the International Conference on Machine Learning, 2022

Adversarial Scrutiny of Evidentiary Statistical Software.

Rediet Abebe, Moritz Hardt, Angela Jin, John Miller, Ludwig Schmidt, Rebecca Wexler

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Exploring The Landscape of Distributional Robustness for Question Answering Models.

Anas Awadalla, Mitchell Wortsman, Gabriel Ilharco, Sewon Min, Ian Magnusson, Hannaneh Hajishirzi, Ludwig Schmidt

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Robust fine-tuning of zero-shot models.

Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Robust fine-tuning of zero-shot models.

Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

CoRR, 2021

Contrasting Contrastive Self-Supervised Representation Learning Models.

Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi

CoRR, 2021

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

Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning.

Thomas Liao, Rohan Taori, Deborah Raji, Ludwig Schmidt

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Retiring Adult: New Datasets for Fair Machine Learning.

Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization.

John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt

Proceedings of the 38th International Conference on Machine Learning, 2021

Do Image Classifiers Generalize Across Time?

Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Contrasting Contrastive Self-Supervised Representation Learning Pipelines.

Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Predicting with Confidence on Unseen Distributions.

Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, Ludwig Schmidt

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Measuring Robustness to Natural Distribution Shifts in Image Classification.

Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Evaluating Machine Accuracy on ImageNet.

Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt

Proceedings of the 37th International Conference on Machine Learning, 2020

Neural Kernels Without Tangents.

Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Jonathan Ragan-Kelley, Ludwig Schmidt, Benjamin Recht

Proceedings of the 37th International Conference on Machine Learning, 2020

The Effect of Natural Distribution Shift on Question Answering Models.

John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

A systematic framework for natural perturbations from videos.

Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

CoRR, 2019

A Meta-Analysis of Overfitting in Machine Learning.

Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Model Similarity Mitigates Test Set Overuse.

Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Unlabeled Data Improves Adversarial Robustness.

Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Do ImageNet Classifiers Generalize to ImageNet?

Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar

Proceedings of the 36th International Conference on Machine Learning, 2019

Exploring the Landscape of Spatial Robustness.

Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry

Proceedings of the 36th International Conference on Machine Learning, 2019

Model Reconstruction from Model Explanations.

Smitha Milli, Ludwig Schmidt, Anca D. Dragan, Moritz Hardt

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018

Algorithms above the noise floor.

Ludwig Schmidt

PhD thesis, 2018

Do CIFAR-10 Classifiers Generalize to CIFAR-10?

Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar

CoRR, 2018

Adversarially Robust Generalization Requires More Data.

Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Classification-Based Study of Covariate Shift in GAN Distributions.

Shibani Santurkar, Ludwig Schmidt, Aleksander Madry

Proceedings of the 35th International Conference on Machine Learning, 2018

On the Limitations of First-Order Approximation in GAN Dynamics.

Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt

Proceedings of the 35th International Conference on Machine Learning, 2018

Towards Deep Learning Models Resistant to Adversarial Attacks.

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu

Proceedings of the 6th International Conference on Learning Representations, 2018

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms.

Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

Proceedings of the Conference On Learning Theory, 2018

A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians.

Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Graph-Sparse Logistic Regression.

Alexander LeNail, Ludwig Schmidt, Johnathan Li, Tobias Ehrenberger, Karen Sachs, Stefanie Jegelka, Ernest Fraenkel

CoRR, 2017

A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations.

Logan Engstrom, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry

CoRR, 2017

A Classification-Based Perspective on GAN Distributions.

Shibani Santurkar, Ludwig Schmidt, Aleksander Madry

CoRR, 2017

Towards Understanding the Dynamics of Generative Adversarial Networks.

Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt

CoRR, 2017

Better Approximations for Tree Sparsity in Nearly-Linear Time.

Arturs Backurs, Piotr Indyk, Ludwig Schmidt

Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017

Sample-Optimal Density Estimation in Nearly-Linear Time.

Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017

Communication-Efficient Distributed Learning of Discrete Distributions.

Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

Arturs Backurs, Piotr Indyk, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities.

Jerry Li, Ludwig Schmidt

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Fast recovery from a union of subspaces.

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Fast Algorithms for Segmented Regression.

Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Approximation Algorithms for Model-Based Compressive Sensing.

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

IEEE Trans. Inf. Theory, 2015

Fast Algorithms for Structured Sparsity.

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

Bull. EATCS, 2015

A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k.

Jerry Zheng Li, Ludwig Schmidt

CoRR, 2015

Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms.

Jayadev Acharya, Ilias Diakonikolas, Chinmay Hegde, Jerry Zheng Li, Ludwig Schmidt

Proceedings of the 34th ACM Symposium on Principles of Database Systems, 2015

Differentially Private Learning of Structured Discrete Distributions.

Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Practical and Optimal LSH for Angular Distance.

Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya P. Razenshteyn, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Nearly-Linear Time Framework for Graph-Structured Sparsity.

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

Proceedings of the 32nd International Conference on Machine Learning, 2015

Seismic feature extraction using steiner tree methods.

Ludwig Schmidt, Chinmay Hegde, Piotr Indyk, Ligang Lu, Xingang Chi, Detlef Hohl

Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014

Recent Developments in the Sparse Fourier Transform: A compressed Fourier transform for big data.

Anna C. Gilbert, Piotr Indyk, Mark A. Iwen, Ludwig Schmidt

IEEE Signal Process. Mag., 2014

Approximation-Tolerant Model-Based Compressive Sensing.

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 2014

A fast approximation algorithm for tree-sparse recovery.

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Large-scale speaker identification.

Ludwig Schmidt, Matthew Sharifi, Ignacio López-Moreno

Proceedings of the IEEE International Conference on Acoustics, 2014

Automatic fault localization using the generalized Earth Mover's distance.

Ludwig Schmidt, Chinmay Hegde, Piotr Indyk, Jonathan Kane, Ligang Lu, Detlef Hohl

Proceedings of the IEEE International Conference on Acoustics, 2014

Nearly Linear-Time Model-Based Compressive Sensing.

Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

Proceedings of the Automata, Languages, and Programming - 41st International Colloquium, 2014