activity
20102021
most citedRisks from Learned Optimization in Advanced Machine Learning Systems

24 citations · 84 across the 14 of their papers we have counts for

collaborators

16 papers

cs.AI2021

Temporal Inference with Finite Factored Sets

Scott Garrabrant

We propose a new approach to temporal inference, inspired by the Pearlian causal inference paradigm - though quite different from Pearl's approach formally. Rather than using direc…

math.CT2021

Cartesian Frames

Scott Garrabrant, Daniel A. Herrmann, Josiah Lopez-Wild

We introduce a novel framework, the theory of Cartesian frames (CF), that gives powerful tools for manipulating sets of acts. The CF framework takes as its most fundamental buildin…

cs.AI2019★ 24 cited

Risks from Learned Optimization in Advanced Machine Learning Systems

Evan Hubinger, Chris van Merwijk, Vladimir Mikulik +2

We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer - a situation we refer to as mesa-optimization, a neo…

cs.AI2019★ 4 cited

Embedded Agency

Abram Demski, Scott Garrabrant

Traditional models of rational action treat the agent as though it is cleanly separated from its environment, and can act on that environment from the outside. Such agents have a k…

cs.AI2018★ 8 cited

Categorizing Variants of Goodhart's Law

David Manheim, Scott Garrabrant

There are several distinct failure modes for overoptimization of systems on the basis of metrics. This occurs when a metric which can be used to improve a system is used to an exte…

cs.LO2017★ 7 cited

A Formal Approach to the Problem of Logical Non-Omniscience

Scott Garrabrant, Tsvi Benson-Tilsen, Andrew Critch +2

We present the logical induction criterion for computable algorithms that assign probabilities to every logical statement in a given formal language, and refine those probabilities…