Ballot Structure from the Preference-Dependency Graph
Which questions should a ballot ask together? The votes can tell us.
Minimality from uncertainty
How much of an action set should be foreclosed before you can learn?
Agency as the candidate objective
Deriving a social utility function from four toy worlds
The Geometry of Governance
Voting as a compression problem, and the right to be heard
A Decision-Theoretic Take on Social Choice
Using L2 to solve the preference dilution problem
The Bayesian Computation Wishlist
The fundamental operations that define the practice of Bayesian inference.
Committed to Fidelity
A Unified Model for Multi-Fidelity Bandits
Why are future rewards worth less than present rewards?
A Bayesian Derivation of Discounting
Goal Hijacking
How Intrinsic Motivation Can Derail Goal-Directed Behavior
The MAP solution is not the best solution?
MAP produces solutions that are not typical
Projection into the typical set: PITS
A new approach to solving inverse problems
Diffusion posterior sampling
A review of recent work
Typical set of arbitrary distributions
Constructing the typical set for arbitrary distributions
Typical set of arbitrary distributions
Proving that the image of the typical set is the typical set of the image
The behaviour of neural flows
Neural nets can struggle to learn very simple flows.
Uncertainty and graphs
From uncertainty over constraints to uncertainty over graphs
Requests for research
Some ideas from my masters.
The advantages of backward reasoning.
A simple exploration of what can be gained by reasoning backwards from your goal.
Real time bandits
Rewarding a twitter bot is more complicated than I imagined.
Principles of neural design
Note from reading the book.
Visualising dataset alignment
Dataset alignment represented as tensor networks
Conserved complexity
A conservation law for algorithms?
Saddles, Splitting, and Reparameterization
Dynamically Growing Neural Networks