Max Vladymyrov

35 papers A* 10A 1B 1Journal 20Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Contextually Guided Transformers via Low-Rank Adaptation. CoRR Andrey Zhmoginov, Jihwan Lee, Max Vladymyrov, Mark Sandler
2025	A*	`conf`	How new data permeates LLM knowledge and how to dilute it. ICLR Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler
2025	J	`jnl`	How new data permeates LLM knowledge and how to dilute it. CoRR Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler
2025	J	`jnl`	Long Context In-Context Compression by Getting to the Gist of Gisting. CoRR Aleksandar Petrov, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Max Vladymyrov
2024	J	`jnl`	Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning. Trans. Mach. Learn. Res. Max Vladymyrov, Andrey Zhmoginov, Mark Sandler
2024	J	`jnl`	Learning and Unlearning of Fabricated Knowledge in Language Models. CoRR Chen Sun, Nolan Andrew Miller, Andrey Zhmoginov, Max Vladymyrov, Mark Sandler
2024	A*	`conf`	Linear Transformers are Versatile In-Context Learners. NeurIPS Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge
2024	J	`jnl`	Linear Transformers are Versatile In-Context Learners. CoRR Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge
2024	J	`jnl`	Narrowing the Focus: Learned Optimizers for Pretrained Models. CoRR Gus Kristiansen, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Anirudh Goyal, Jihwan Lee, Max Vladymyrov
2023	J	`jnl`	Continual Few-Shot Learning Using HyperTransformers. CoRR Max Vladymyrov, Andrey Zhmoginov, Mark Sandler
2023	A*	`conf`	Decentralized Learning with Multi-Headed Distillation. CVPR Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov
2023	J	`jnl`	Training trajectories, mini-batch losses and the curious role of the learning rate. CoRR Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Nolan Miller
2023	A*	`conf`	Transformers Learn In-Context by Gradient Descent. ICML Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov
2023	J	`jnl`	Uncovering mesa-optimization algorithms in Transformers. CoRR 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
2022	J	`jnl`	Decentralized Learning with Multi-Headed Distillation. CoRR Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov
2022	A*	`conf`	Fine-tuning Image Transformers using Learnable Memory. CVPR Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson
2022	J	`jnl`	Fine-tuning Image Transformers using Learnable Memory. CoRR Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson
2022	A*	`conf`	GradMax: Growing Neural Networks using Gradient Information. ICLR Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov
2022	J	`jnl`	GradMax: Growing Neural Networks using Gradient Information. CoRR Utku Evci, Max Vladymyrov, Thomas Unterthiner, Bart van Merriënboer, Fabian Pedregosa
2022	J	`jnl`	HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. CoRR Andrey Zhmoginov, Mark Sandler, Max Vladymyrov
2022	J	`jnl`	Transformers learn in-context by gradient descent. CoRR Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov
2022	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 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
2021	A*	`conf`	Meta-Learning Bidirectional Update Rules. ICML Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas
2021	J	`jnl`	Meta-Learning Bidirectional Update Rules. CoRR Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Agüera y Arcas
2020	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR 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
2019	A*	`conf`	No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms. NeurIPS Max Vladymyrov
2019	J	`jnl`	No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms. CoRR Max Vladymyrov
2017	B	`conf`	Fast, accurate spectral clustering using locally linear landmarks. IJCNN Max Vladymyrov, Miguel Á. Carreira-Perpiñán
2016	A*	`conf`	The Variational Nystrom method for large-scale spectral problems. ICML Max Vladymyrov, Miguel Á. Carreira-Perpiñán
2015	—	`conf`	A fast, universal algorithm to learn parametric nonlinear embeddings. NIPS Miguel Á. Carreira-Perpiñán, Max Vladymyrov
2014	A	`conf`	Linear-time training of nonlinear low-dimensional embeddings. AISTATS Max Vladymyrov, Miguel Á. Carreira-Perpiñán
2013	—	`conf`	Entropic Affinities: Properties and Efficient Numerical Computation. ICML (3) Max Vladymyrov, Miguel Á. Carreira-Perpiñán
2013	—	`conf`	Locally Linear Landmarks for Large-Scale Manifold Learning. ECML/PKDD (3) Max Vladymyrov, Miguel Á. Carreira-Perpiñán
2012	A*	`conf`	Fast Training of Nonlinear Embedding Algorithms. ICML Max Vladymyrov, Miguel Á. Carreira-Perpiñán
2012	J	`jnl`	Partial-Hessian Strategies for Fast Learning of Nonlinear Embeddings CoRR Max Vladymyrov, Miguel Á. Carreira-Perpiñán