Max Vladymyrov

According to our database, Max Vladymyrov authored at least 24 papers between 2012 and 2024.

Collaborative distances:

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

2024

Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning.

Max Vladymyrov, Andrey Zhmoginov, Mark Sandler

Trans. Mach. Learn. Res., 2024

Learning and Unlearning of Fabricated Knowledge in Language Models.

Chen Sun, Nolan Andrew Miller, Andrey Zhmoginov, Max Vladymyrov, Mark Sandler

CoRR, 2024

Narrowing the Focus: Learned Optimizers for Pretrained Models.

Gus Kristiansen, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Anirudh Goyal, Jihwan Lee, Max Vladymyrov

CoRR, 2024

Linear Transformers are Versatile In-Context Learners.

Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge

CoRR, 2024

2023

Uncovering mesa-optimization algorithms in Transformers.

Johannes von Oswald, Eyvind Niklasson, Maximilian Schlegel, Seijin Kobayashi, Nicolas Zucchet, Nino Scherrer, Nolan Miller, Mark Sandler, Blaise Agüera y Arcas, Max Vladymyrov, Razvan Pascanu, João Sacramento

CoRR, 2023

Continual Few-Shot Learning Using HyperTransformers.

Max Vladymyrov, Andrey Zhmoginov, Mark Sandler

CoRR, 2023

Training trajectories, mini-batch losses and the curious role of the learning rate.

Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Nolan Miller

CoRR, 2023

Transformers Learn In-Context by Gradient Descent.

Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov

Proceedings of the International Conference on Machine Learning, 2023

Decentralized Learning with Multi-Headed Distillation.

Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov

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

2022

Underspecification Presents Challenges for Credibility in Modern Machine Learning.

Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley

J. Mach. Learn. Res., 2022

HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning.

Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov

Proceedings of the International Conference on Machine Learning, 2022

GradMax: Growing Neural Networks using Gradient Information.

Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov

Proceedings of the Tenth International Conference on Learning Representations, 2022

Fine-tuning Image Transformers using Learnable Memory.

Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson

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

2021

Meta-Learning Bidirectional Update Rules.

Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas

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

2019

No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms.

Max Vladymyrov

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

2017

Fast, accurate spectral clustering using locally linear landmarks.

Max Vladymyrov, Miguel Á. Carreira-Perpiñán

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2016

The Variational Nystrom method for large-scale spectral problems.

Max Vladymyrov, Miguel Á. Carreira-Perpiñán

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

2015

A fast, universal algorithm to learn parametric nonlinear embeddings.

Miguel Á. Carreira-Perpiñán, Max Vladymyrov

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

2014

Large-Scale Methods for Nonlinear Manifold Learning.

Maksym Vladymyrov

PhD thesis, 2014

Linear-time training of nonlinear low-dimensional embeddings.

Max Vladymyrov, Miguel Á. Carreira-Perpiñán

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Locally Linear Landmarks for Large-Scale Manifold Learning.

Max Vladymyrov, Miguel Á. Carreira-Perpiñán

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Entropic Affinities: Properties and Efficient Numerical Computation.

Max Vladymyrov, Miguel Á. Carreira-Perpiñán

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Partial-Hessian Strategies for Fast Learning of Nonlinear Embeddings

Max Vladymyrov, Miguel Á. Carreira-Perpiñán

CoRR, 2012

Fast Training of Nonlinear Embedding Algorithms.

Max Vladymyrov, Miguel Á. Carreira-Perpiñán

Proceedings of the 29th International Conference on Machine Learning, 2012