Skip to content
logo
DM
Recently Updated
07-17: 2023-07-17
07-04: 2023-07-04
06-21: In Context Learning
03-10: Draft-Dip-Nerf
02-24: Should you buy the DIP?
02-21: Machine Learning in Practice;
12-20: Language Models: Fancy Number Pattern Matching
11-30: OCR
11-29: Generalisation Revisited
11-27: Generalisation Paper

Tags

MLOps (1)
artificial_intelligence (3)
attention (2)
biologically_inspired (3)
causal_inference (1)
classic_papers (1)
commentary (1)
Theoretical Statistics: Beauty or Banality
computer_science (1)
computer_vision (3)
data_science (1)
deep_learning (2)
economics (2)
finance (3)
from_article (5)
from_paper (22)
from_podcast (1)
from_talk (1)
gradient_descent (3)
graph_neural_networks (1)
gui (2)
idea (3)
implicit_regularisation (1)
interpolation (3)
interview (1)
lit_review (4)
machine_learning (20)
mathematics (1)
matrix_completion (2)
medicine (1)
meta_analysis (1)
meta_learning (1)
money_stuff (2)
networks (2)
neural_networks (6)
neural_tangent_kernel (2)
neurips (1)
neuroscience (2)
nlp (6)
optimisation (4)
overparameterization (1)
psychology (3)
regularization (1)
reinforcement_learning (1)
research (4)
self_supervised_learning (2)
society (2)
statistics (7)
Classification vs Regression
Dynamic Graph Models
Estimating the Mean
Meta Analysis vs Preregistration
Michael Jordan Plenary Talk
Theoretical Statistics: Beauty or Banality
Two Cultures
theoretical_statistics (2)
Estimating the Mean
Theoretical Statistics: Beauty or Banality
xAI (2)
On this page
On this page

Theoretical Statistics: Beauty or Banality

While listening to this fairly interesting invited talked by Lugosi, I can't help reflect on the banality that is most of theoretical statistics, and why I have moved swiftly on from it.

When I was deeply embedded in that sub-culture

Tags

Edit this page
Last updated on 1/12/2022