Mark Schmidt

Publications

BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks. Issam H. Laradji, Amrutha Ramesh, Mark Schmidt. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence. julie nutini, Issam H. Laradji, Mark Schmidt. At International Conference on Machine Learning (ICML), 2023.

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence. julie nutini, Issam H. Laradji, Mark Schmidt. At Journal of Machine Learning Research (JMLR), 2022.

Proposal-based Instance Segmentation with Point Supervision. Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt. At International Conference on Image Processing (ICIP), 2020.

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Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. Si Yi Meng, Sharan Vaswani, Issam H. Laradji, Mark Schmidt, Simon Lcoste-Julien. At International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

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Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lcoste-Julien. At Conference on Neural Information Processing Systems (NeurIPS), 2019.

Where are the Masks: Instance Segmentation with Image-level Supervision. Issam H. Laradji, David Vazquez, Mark Schmidt. At Britsh Machine Vision Conference (BMVC), 2019.

Efficient Deep Gaussian Process Models for Variable-Sized Inputs. Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim. At International Joint Conference on Neural Networks (IJCNN), 2019.

Where are the Blobs: Counting by Localization with Point Supervision. Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt. At European Conference on Computer Vision (ECCV), 2018.

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