Improving automatic writer identification. - Laurens van der Maaten; Eric O. Postma (BNAIC, 2005)

Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation. - Stijn Vanderlooy; Laurens van der Maaten; Ida G. Sprinkhuizen-Kuyper (MLDM, 2007)

Learning a Parametric Embedding by Preserving Local Structure. - Laurens van der Maaten (AISTATS, 2009)

Capturing appearance variation in active appearance models. - Laurens van der Maaten; Emile A. Hendriks (CVPR Workshops, 2010)

Deep Supervised t-Distributed Embedding. - Martin Renqiang Min; Laurens van der Maaten; Zineng Yuan; Anthony J. Bonner; Zhaolei Zhang (ICML, 2010)

On Herding and the Perceptron Cycling Theorem. - Andrew Gelfand; Yutian Chen 001; Laurens van der Maaten; Max Welling (NIPS, 2010)

Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development. - Diane Hu; Laurens van der Maaten; Youngmin Cho; Lawrence K. Saul; Sorin Lerner (NIPS, 2010)

Learning Discriminative Fisher Kernels. - Laurens van der Maaten (ICML, 2011)

Discussion of "Spectral Dimensionality Reduction via Maximum Entropy". - Laurens van der Maaten (AISTATS, 2011)

Hidden-Unit Conditional Random Fields. - Laurens van der Maaten; Max Welling; Lawrence K. Saul (AISTATS, 2011)

A Behavioral Investigation of Dimensionality Reduction. - Joshua M. Lewis; Laurens van der Maaten; Virginia R. de Sa (CogSci, 2012)

Improving segment based stereo matching using SURF key points. - Gorkem Saygili; Laurens van der Maaten; Emile A. Hendriks (ICIP, 2012)

Audio-visual emotion challenge 2012: a simple approach. - Laurens van der Maaten (ICMI, 2012)

Accelerating Cost Aggregation for Real-Time Stereo Matching. - Jianbin Fang; Ana Lucia Varbanescu; Jie Shen 003; Henk J. Sips; Gorkem Saygili; Laurens van der Maaten (ICPADS, 2012)

Stochastic triplet embedding. - Laurens van der Maaten; Kilian Q. Weinberger (MLSP, 2012)

Action unit classification using active appearance models and conditional random fields. - Laurens van der Maaten; Emile A. Hendriks (Cogn. Process., 2012)

Analyzing floristic inventories with multiple maps. - Laurens van der Maaten; Sebastian Schmidtlein; Miguel D. Mahecha (Ecol. Informatics, 2012)

Visualizing non-metric similarities in multiple maps. - Laurens van der Maaten; Geoffrey E. Hinton (Mach. Learn., 2012)

Structure Preserving Object Tracking. - Lu Zhang 036; Laurens van der Maaten (CVPR, 2013)

Learning with Marginalized Corrupted Features. - Laurens van der Maaten; Minmin Chen; Stephen Tyree; Kilian Q. Weinberger (ICML (1), 2013)

Barnes-Hut-SNE - Laurens van der Maaten (ICLR, 2013)

Divvy: fast and intuitive exploratory data analysis. - Joshua M. Lewis; Virginia R. de Sa; Laurens van der Maaten (J. Mach. Learn. Res., 2013)

Speeding Up Tracking by Ignoring Features. - Lu Zhang 036; Hamdi Dibeklioglu; Laurens van der Maaten (CVPR, 2014)

On Fast Trackers that are Robust to Partial Occlusions. - Lu Zhang 036; Hamdi Dibeklioglu; Laurens van der Maaten (CVPR Workshops, 2014)

Graph-Based Kinship Recognition. - Yuanhao Guo; Hamdi Dibeklioglu; Laurens van der Maaten (ICPR, 2014)

Stereo Similarity Metric Fusion Using Stereo Confidence. - Gorkem Saygili; Laurens van der Maaten; Emile A. Hendriks (ICPR, 2014)

Hybrid Kinect Depth Map Refinement for Transparent Objects. - Gorkem Saygili; Laurens van der Maaten; Emile A. Hendriks (ICPR, 2014)

Improving Object Tracking by Adapting Detectors. - Lu Zhang 036; Laurens van der Maaten (ICPR, 2014)

Marginalizing Corrupted Features. - Laurens van der Maaten; Minmin Chen; Stephen Tyree; Kilian Q. Weinberger (CoRR, 2014)

Accelerating t-SNE using tree-based algorithms. - Laurens van der Maaten (J. Mach. Learn. Res., 2014)

Preserving Structure in Model-Free Tracking. - Lu Zhang 036; Laurens van der Maaten (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Time Series Classification using the Hidden-Unit Logistic Model. - Wenjie Pei; Hamdi Dibeklioglu; David M. J. Tax; Laurens van der Maaten (CoRR, 2015)

Adaptive stereo similarity fusion using confidence measures. - Gorkem Saygili; Laurens van der Maaten; Emile A. Hendriks (Comput. Vis. Image Underst., 2015)

Introduction to the special issue on visual analytics using multidimensional projections. - Michaël Aupetit 001; Laurens van der Maaten (Neurocomputing, 2015)

Automatic Thread-Level Canvas Analysis: A machine-learning approach to analyzing the canvas of paintings. - Laurens van der Maaten; Robert G. Erdmann (IEEE Signal Process. Mag., 2015)

Revisiting Visual Question Answering Baselines. - Allan Jabri; Armand Joulin; Laurens van der Maaten (ECCV (8), 2016)

Learning Visual Features from Large Weakly Supervised Data. - Armand Joulin; Laurens van der Maaten; Allan Jabri; Nicolas Vasilache (ECCV (7), 2016)

Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance. - Kevin van Hecke; Guido de Croon; Laurens van der Maaten; Daniel Hennes; Dario Izzo (CoRR, 2016)

Modeling Time Series Similarity with Siamese Recurrent Networks. - Wenjie Pei; David M. J. Tax; Laurens van der Maaten (CoRR, 2016)

Feature-Level Domain Adaptation. - Wouter M. Kouw; Laurens J. P. van der Maaten; Jesse H. Krijthe; Marco Loog (J. Mach. Learn. Res., 2016)

Densely Connected Convolutional Networks. - Gao Huang 001; Zhuang Liu 003; Laurens van der Maaten; Kilian Q. Weinberger (CVPR, 2017)

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning. - Justin Johnson 001; Bharath Hariharan; Laurens van der Maaten; Li Fei-Fei 001; C. Lawrence Zitnick; Ross B. Girshick (CVPR, 2017)

Inferring and Executing Programs for Visual Reasoning. - Justin Johnson 001; Bharath Hariharan; Laurens van der Maaten; Judy Hoffman; Li Fei-Fei 001; C. Lawrence Zitnick; Ross B. Girshick (ICCV, 2017)

Learning Visual N-Grams from Web Data. - Ang Li; Allan Jabri; Armand Joulin; Laurens van der Maaten (ICCV, 2017)

Submanifold Sparse Convolutional Networks. - Benjamin Graham; Laurens van der Maaten (CoRR, 2017)

Multi-Scale Dense Convolutional Networks for Efficient Prediction. - Gao Huang 001; Danlu Chen; Tianhong Li; Felix Wu; Laurens van der Maaten; Kilian Q. Weinberger (CoRR, 2017)

Memory-Efficient Implementation of DenseNets. - Geoff Pleiss; Danlu Chen; Gao Huang 001; Tongcheng Li; Laurens van der Maaten; Kilian Q. Weinberger (CoRR, 2017)

Approximated and User Steerable tSNE for Progressive Visual Analytics. - Nicola Pezzotti; Boudewijn P. F. Lelieveldt; Laurens van der Maaten; Thomas Höllt; Elmar Eisemann; Anna Vilanova (IEEE Trans. Vis. Comput. Graph., 2017)

3D Semantic Segmentation With Submanifold Sparse Convolutional Networks. - Benjamin Graham; Martin Engelcke; Laurens van der Maaten (CVPR, 2018)

CondenseNet: An Efficient DenseNet Using Learned Group Convolutions. - Gao Huang 001; Shichen Liu; Laurens van der Maaten; Kilian Q. Weinberger (CVPR, 2018)

Learning by Asking Questions. - Ishan Misra; Ross B. Girshick; Rob Fergus; Martial Hebert; Abhinav Gupta 001; Laurens van der Maaten (CVPR, 2018)

Separating Self-Expression and Visual Content in Hashtag Supervision. - Andreas Veit; Maximilian Nickel; Serge J. Belongie; Laurens van der Maaten (CVPR, 2018)

Exploring the Limits of Weakly Supervised Pretraining. - Dhruv Mahajan 001; Ross B. Girshick; Vignesh Ramanathan; Kaiming He; Manohar Paluri; Yixuan Li 001; Ashwin Bharambe; Laurens van der Maaten (ECCV (2), 2018)

PAWE: Polysemy Aware Word Embeddings. - Taygun Kekeç; Laurens van der Maaten; David M. J. Tax (ICISDM, 2018)

Countering Adversarial Images using Input Transformations. - Chuan Guo 001; Mayank Rana; Moustapha Cissé; Laurens van der Maaten (ICLR (Poster), 2018)

Multi-Scale Dense Networks for Resource Efficient Image Classification. - Gao Huang 001; Danlu Chen; Tianhong Li; Felix Wu; Laurens van der Maaten; Kilian Q. Weinberger (ICLR, 2018)

Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model. - Wenjie Pei; Hamdi Dibeklioglu; David M. J. Tax; Laurens van der Maaten (IEEE Trans. Neural Networks Learn. Syst., 2018)

Does Object Recognition Work for Everyone? - Terrance DeVries; Ishan Misra; Changhan Wang; Laurens van der Maaten (CVPR Workshops, 2019)

Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search. - Abhimanyu Dubey; Laurens van der Maaten; Zeki Yalniz; Yixuan Li 001; Dhruv Mahajan 001 (CVPR, 2019)

Feature Denoising for Improving Adversarial Robustness. - Cihang Xie; Yuxin Wu; Laurens van der Maaten; Alan L. Yuille; Kaiming He (CVPR, 2019)

Evaluating Text-to-Image Matching using Binary Image Selection (BISON). - Hexiang Hu; Ishan Misra; Laurens van der Maaten (ICCV Workshops, 2019)

Anytime Stereo Image Depth Estimation on Mobile Devices. - Yan Wang 051; Zihang Lai; Gao Huang 001; Brian H. Wang; Laurens van der Maaten; Mark E. Campbell; Kilian Q. Weinberger (ICRA, 2019)

PHYRE: A New Benchmark for Physical Reasoning. - Anton Bakhtin; Laurens van der Maaten; Justin Johnson 001; Laura Gustafson; Ross B. Girshick (NeurIPS, 2019)

Binary Image Selection (BISON): Interpretable Evaluation of Visual Grounding. - Hexiang Hu; Ishan Misra; Laurens van der Maaten (CoRR, 2019)

Privacy-Preserving Contextual Bandits. - Awni Y. Hannun; Brian Knott; Shubho Sengupta; Laurens van der Maaten (CoRR, 2019)

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning. - Yan Wang 051; Wei-Lun Chao; Kilian Q. Weinberger; Laurens van der Maaten (CoRR, 2019)

Measuring Dataset Granularity. - Yin Cui; Zeqi Gu; Dhruv Mahajan 001; Laurens van der Maaten; Serge J. Belongie; Ser-Nam Lim (CoRR, 2019)

Self-Supervised Learning of Pretext-Invariant Representations. - Ishan Misra; Laurens van der Maaten (CVPR, 2020)

Certified Data Removal from Machine Learning Models. - Chuan Guo 001; Tom Goldstein; Awni Y. Hannun; Laurens van der Maaten (ICML, 2020)

Secure multiparty computations in floating-point arithmetic. - Chuan Guo 001; Awni Y. Hannun; Brian Knott; Laurens van der Maaten; Mark Tygert; Ruiyu Zhu (CoRR, 2020)

Forward Prediction for Physical Reasoning. - Rohit Girdhar; Laura Gustafson; Aaron Adcock; Laurens van der Maaten (CoRR, 2020)

The Trade-Offs of Private Prediction. - Laurens van der Maaten; Awni Y. Hannun (CoRR, 2020)

Data Appraisal Without Data Sharing. - Mimee Xu; Laurens van der Maaten; Awni Y. Hannun (CoRR, 2020)

Making Paper Reviewing Robust to Bid Manipulation Attacks. - Ruihan Wu; Chuan Guo 001; Felix Wu; Rahul Kidambi; Laurens van der Maaten; Kilian Q. Weinberger (ICML, 2021)

CrypTen: Secure Multi-Party Computation Meets Machine Learning. - Brian Knott; Shobha Venkataraman; Awni Y. Hannun; Shubho Sengupta; Mark Ibrahim; Laurens van der Maaten (NeurIPS, 2021)

Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems. - Ruihan Wu; Chuan Guo 001; Awni Y. Hannun; Laurens van der Maaten (NeurIPS, 2021)

Measuring data leakage in machine-learning models with Fisher information. - Awni Y. Hannun; Chuan Guo 001; Laurens van der Maaten (UAI, 2021)

Physical Reasoning Using Dynamics-Aware Models. - Eltayeb Ahmed; Anton Bakhtin; Laurens van der Maaten; Rohit Girdhar (CoRR, 2021)

Data Appraisal Without Data Sharing. - Xinlei Xu; Awni Y. Hannun; Laurens van der Maaten (AISTATS, 2022)

Scaling up Instance Segmentation using Approximately Localized Phrases. - Karan Desai; Ishan Misra; Justin Johnson 001; Laurens van der Maaten (BMVC, 2022)

EIFFeL: Ensuring Integrity for Federated Learning. - Amrita Roy Chowdhury 001; Chuan Guo 001; Somesh Jha; Laurens van der Maaten (CCS, 2022)

Omnivore: A Single Model for Many Visual Modalities. - Rohit Girdhar; Mannat Singh; Nikhila Ravi; Laurens van der Maaten; Armand Joulin; Ishan Misra (CVPR, 2022)

Revisiting Weakly Supervised Pre-Training of Visual Perception Models. - Mannat Singh; Laura Gustafson; Aaron Adcock; Vinicius de Freitas Reis; Bugra Gedik; Raj Prateek Kosaraju; Dhruv Mahajan 001; Ross B. Girshick; Piotr Dollár; Laurens van der Maaten (CVPR, 2022)

Bounding Training Data Reconstruction in Private (Deep) Learning. - Chuan Guo 001; Brian Karrer; Kamalika Chaudhuri; Laurens van der Maaten (ICML, 2022)

Measuring Data Leakage in Machine-Learning Models with Fisher Information (Extended Abstract). - Awni Y. Hannun; Chuan Guo 001; Laurens van der Maaten (IJCAI, 2022)

Submix: Practical Private Prediction for Large-Scale Language Models. - Antonio Ginart; Laurens van der Maaten; James Zou 001; Chuan Guo 001 (CoRR, 2022)

A Systematic Study of Bias Amplification. - Melissa Hall; Laurens van der Maaten; Laura Gustafson; Aaron Adcock (CoRR, 2022)

Convolutional Networks with Dense Connectivity. - Gao Huang 001; Zhuang Liu 003; Geoff Pleiss; Laurens van der Maaten; Kilian Q. Weinberger (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition. - Vikram V. Ramaswamy; Sing Yu Lin; Dora Zhao; Aaron Adcock; Laurens van der Maaten; Deepti Ghadiyaram; Olga Russakovsky (NeurIPS, 2023)

Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset. - Vikram V. Ramaswamy; Sing Yu Lin; Dora Zhao; Aaron B. Adcock; Laurens van der Maaten; Deepti Ghadiyaram; Olga Russakovsky (CoRR, 2023)

Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. - Kamalika Chaudhuri; Chuan Guo 001; Laurens van der Maaten; Saeed Mahloujifar; Mark Tygert (Trans. Mach. Learn. Res., 2024)

Law of the Weakest Link: Cross Capabilities of Large Language Models. - Ming Zhong 005; Aston Zhang; Xuewei Wang; Rui Hou; Wenhan Xiong; Chenguang Zhu 001; Zhengxing Chen; Liang Tan; Chloe Bi; Mike Lewis; Sravya Popuri; Sharan Narang; et al. (ICLR, 2025)

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning. - Justin Johnson 001; Bharath Hariharan; Laurens van der Maaten; Fei-Fei Li 001; C. Lawrence Zitnick; Ross B. Girshick (Zenodo, 2025)

RoFL: Robust Fingerprinting of Language Models. - Yun-Yun Tsai; Chuan Guo 001; Junfeng Yang; Laurens van der Maaten (CoRR, 2025)

NOMAD Projection. - Brandon Duderstadt; Zach Nussbaum; Laurens van der Maaten (CoRR, 2025)