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ORCIDMark Schmidt 0001
- > Home > Persons > Mark Schmidt 0001
Publications
- 2024
[i59]Frederik Kunstner, Robin Yadav, Alan Milligan, Mark Schmidt, Alberto Bietti:
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models. CoRR abs/2402.19449 (2024)- 2023
- [c63]Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington, Mark Schmidt:
Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be. ICLR 2023 - [c58]Frederik Kunstner, Victor Sanches Portella, Mark Schmidt, Nicholas J. A. Harvey:
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. NeurIPS 2023 - [i51]Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington, Mark Schmidt:
Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Be. CoRR abs/2304.13960 (2023) - [i49]Frederik Kunstner, Victor S. Portella, Mark Schmidt, Nick Harvey:
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking. CoRR abs/2306.02527 (2023) - 2022
- [c54]Frederik Kunstner, Raunak Kumar, Mark Schmidt:
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent (Extended Abstract). IJCAI 2022: 5294-5298 - 2021
- [c53]Frederik Kunstner, Raunak Kumar, Mark Schmidt:
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. AISTATS 2021: 3295-3303 - 2020
- [i41]Sharan Vaswani, Frederik Kunstner, Issam H. Laradji, Si Yi Meng, Mark Schmidt, Simon Lacoste-Julien:
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search). CoRR abs/2006.06835 (2020) - [i38]Frederik Kunstner, Raunak Kumar, Mark Schmidt:
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent. CoRR abs/2011.01170 (2020) - 2018
- [c34]Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan:
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. NeurIPS 2018: 6248-6258 - [i23]Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan:
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. CoRR abs/1811.04504 (2018)
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