Convergent algorithms for collaborative filtering. - Jon M. Kleinberg; Mark Sandler 002 (EC, 2003)

On Learning Mixtures of Heavy-Tailed Distributions. - Anirban Dasgupta 001; John E. Hopcroft; Jon M. Kleinberg; Mark Sandler 002 (FOCS, 2005)

On the use of linear programming for unsupervised text classification. - Mark Sandler 002 (KDD, 2005)

Privacy via pseudorandom sketches. - Nina Mishra; Mark Sandler 002 (PODS, 2006)

Algorithms for Mixture Models. - Mark Sandler 002 (2006)

Hierarchical mixture models: a probabilistic analysis. - Mark Sandler 002 (KDD, 2007)

Using mixture models for collaborative filtering. - Jon M. Kleinberg; Mark Sandler 002 (J. Comput. Syst. Sci., 2008)

Network Failure Detection and Graph Connectivity. - Jon M. Kleinberg; Mark Sandler 002; Aleksandrs Slivkins (SIAM J. Comput., 2008)

Theory research at Google. - Gagan Aggarwal; Nir Ailon; Florin Constantin; Eyal Even-Dar; Jon Feldman; Gereon Frahling; Monika Rauch Henzinger; S. Muthukrishnan 001; Noam Nisan; Martin Pál; Mark Sandler 002; Anastasios Sidiropoulos (SIGACT News, 2008)

Monitoring algorithms for negative feedback systems. - Mark Sandler 002; S. Muthukrishnan 001 (WWW, 2010)

Modeling the Parallel Execution of Black-Box Services. - Gideon Mann; Mark Sandler 002; Darja Krushevskaja; Sudipto Guha; Eyal Even-Dar (HotCloud, 2011)

Telling Two Distributions Apart: a Tight Characterization - Eyal Even-Dar; Mark Sandler 002 (CoRR, 2011)

Frugal Streaming for Estimating Quantiles. - Qiang Ma 006; S. Muthukrishnan 001; Mark Sandler 002 (Space-Efficient Data Structures, Streams, and Algorithms, 2013)

Understanding latency variations of black box services. - Darja Krushevskaja; Mark Sandler 002 (WWW, 2013)

Frugal Streaming for Estimating Quantiles: One (or two) memory suffices. - Qiang Ma 006; S. Muthukrishnan 001; Mark Sandler 002 (CoRR, 2014)

Inverting face embeddings with convolutional neural networks. - Andrey Zhmoginov; Mark Sandler 002 (CoRR, 2016)

The Power of Sparsity in Convolutional Neural Networks. - Soravit Changpinyo; Mark Sandler 002; Andrey Zhmoginov (CoRR, 2017)

CycleGAN, a Master of Steganography. - Casey Chu; Andrey Zhmoginov; Mark Sandler 002 (CoRR, 2017)

MobileNetV2: Inverted Residuals and Linear Bottlenecks. - Mark Sandler 002; Andrew G. Howard; Menglong Zhu; Andrey Zhmoginov; Liang-Chieh Chen (CVPR, 2018)

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications. - Tien-Ju Yang; Andrew G. Howard; Bo Chen 019; Xiao Zhang; Alec Go; Mark Sandler 002; Vivienne Sze; Hartwig Adam (ECCV (10), 2018)

Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. - Mark Sandler 002; Andrew G. Howard; Menglong Zhu; Andrey Zhmoginov; Liang-Chieh Chen (CoRR, 2018)

MnasNet: Platform-Aware Neural Architecture Search for Mobile. - Mingxing Tan; Bo Chen 019; Ruoming Pang; Vijay Vasudevan; Mark Sandler 002; Andrew Howard 002; Quoc V. Le (CVPR, 2019)

Searching for MobileNetV3. - Andrew Howard 002; Ruoming Pang; Hartwig Adam; Quoc V. Le; Mark Sandler 002; Bo Chen 019; Weijun Wang; Liang-Chieh Chen; Mingxing Tan; Grace Chu; Vijay Vasudevan; Yukun Zhu (ICCV, 2019)

Non-Discriminative Data or Weak Model? On the Relative Importance of Data and Model Resolution. - Mark Sandler 002; Jonathan Baccash; Andrey Zhmoginov; Andrew Howard 002 (ICCV Workshops, 2019)

K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning. - Pramod Kaushik Mudrakarta; Mark Sandler 002; Andrey Zhmoginov; Andrew G. Howard (ICLR (Poster), 2019)

Structured Multi-Hashing for Model Compression. - Elad Eban; Yair Movshovitz-Attias; Hao Wu; Mark Sandler 002; Andrew Poon; Yerlan Idelbayev; Miguel Á. Carreira-Perpiñán (CVPR, 2020)

SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. - Keren Ye; Adriana Kovashka; Mark Sandler 002; Menglong Zhu; Andrew G. Howard; Marco Fornoni (ECCV Workshops (1), 2020)

Information-Bottleneck Approach to Salient Region Discovery. - Andrey Zhmoginov; Ian Fischer; Mark Sandler 002 (ECML/PKDD (3), 2020)

Image segmentation via Cellular Automata. - Mark Sandler 002; Andrey Zhmoginov; Liangcheng Luo; Alexander Mordvintsev; Ettore Randazzo; Blaise Agüera y Arcas (CoRR, 2020)

Large-Scale Generative Data-Free Distillation. - Liangchen Luo; Mark Sandler 002; Zi Lin; Andrey Zhmoginov; Andrew Howard 002 (CoRR, 2020)

Meta-Learning Bidirectional Update Rules. - Mark Sandler 002; Max Vladymyrov; Andrey Zhmoginov; Nolan Miller; Tom Madams; Andrew Jackson 004; Blaise Agüera y Arcas (ICML, 2021)

Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks. - Andrey Zhmoginov; Dina Bashkirova; Mark Sandler 002 (CoRR, 2021)

Fine-tuning Image Transformers using Learnable Memory. - Mark Sandler 002; Andrey Zhmoginov; Max Vladymyrov; Andrew Jackson 004 (CVPR, 2022)

HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. - Andrey Zhmoginov; Mark Sandler 002; Maksym Vladymyrov (ICML, 2022)

Decentralized Learning with Multi-Headed Distillation. - Andrey Zhmoginov; Mark Sandler 002; Nolan Miller; Gus Kristiansen; Max Vladymyrov (CVPR, 2023)

Training trajectories, mini-batch losses and the curious role of the learning rate. - Mark Sandler 002; Andrey Zhmoginov; Max Vladymyrov; Nolan Miller (CoRR, 2023)

Continual Few-Shot Learning Using HyperTransformers. - Max Vladymyrov; Andrey Zhmoginov; Mark Sandler 002 (CoRR, 2023)

Uncovering mesa-optimization algorithms in Transformers. - Johannes von Oswald; Eyvind Niklasson; Maximilian Schlegel; Seijin Kobayashi; Nicolas Zucchet; Nino Scherrer; Nolan Miller; Mark Sandler 002; Blaise Agüera y Arcas; Max Vladymyrov; Razvan Pascanu; João Sacramento (CoRR, 2023)

Linear Transformers are Versatile In-Context Learners. - Max Vladymyrov; Johannes von Oswald; Mark Sandler 002; Rong Ge (NeurIPS, 2024)

Narrowing the Focus: Learned Optimizers for Pretrained Models. - Gus Kristiansen; Mark Sandler 002; Andrey Zhmoginov; Nolan Miller; Anirudh Goyal; Jihwan Lee; Max Vladymyrov (CoRR, 2024)

Learning and Unlearning of Fabricated Knowledge in Language Models. - Chen Sun; Nolan Andrew Miller; Andrey Zhmoginov; Max Vladymyrov; Mark Sandler 002 (CoRR, 2024)

Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning. - Max Vladymyrov; Andrey Zhmoginov; Mark Sandler 002 (Trans. Mach. Learn. Res., 2024)

How new data permeates LLM knowledge and how to dilute it. - Chen Sun; Renat Aksitov; Andrey Zhmoginov; Nolan Andrew Miller; Max Vladymyrov; Ulrich Rueckert; Been Kim; Mark Sandler 002 (ICLR, 2025)

Long Context In-Context Compression by Getting to the Gist of Gisting. - Aleksandar Petrov; Mark Sandler 002; Andrey Zhmoginov; Nolan Miller; Max Vladymyrov (CoRR, 2025)

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones. - Andrey Zhmoginov; Jihwan Lee; Mark Sandler 002 (CoRR, 2025)

Contextually Guided Transformers via Low-Rank Adaptation. - Andrey Zhmoginov; Jihwan Lee; Max Vladymyrov; Mark Sandler 002 (CoRR, 2025)