Michael DeWeese
Professor
Studies machine learning theory, nonequilibrium statistical mechanics and systems neuroscience.
Michael DeWeese's lab at UC Berkeley works on learning theory & optimization, computational neuroscience & cognition and neuroscience & brain-computer interfaces. 2 papers since 2023. 8 PhD students. 6 alumni. Applications close .
At a glance
- papers, 2023–2026
- 1/ year
- at T1 venues
- 0%
- PhD students
- 8
- MS students
- 0
- alumni listed
- 6
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 0 papers2025 · T1 0
- 1 papers2024 · T1 0
- 1 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Learning theory & optimization1 paper
- Computational neuroscience & cognition0 papers
- Neuroscience & brain-computer interfaces0 papers
- mechanics0 papers
- neuroscience0 papers
Paper list
From the lab homepage.
20241 paper
- ICLR ‘24More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory
20231 paper
- TMLR ‘23The eigenlearning framework: A conservation law perspective on kernel ridge regression and wide neural networks
Where alumni went6 listed
Apply to UC BerkeleyFall 2027
UC Berkeley EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS (non-English-country degrees)
- Fee
- See program page
Confirmed on the program page.
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Data
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- Collected
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- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
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