1 citations · 2 across the 10 of their papers we have counts for
12 papers
Training Language Models To Be Coherent Decision-Makers
Khurram Yamin, Xavier Fernandes, Paul Koch +2
Reliable decision-making requires more than accurate prediction: a model must preserve its beliefs, apply the relevant utilities, and recognize when the information needed to justi…
Bayesian Intelligence from the Outside
Alex Smolin, Bryan Wilder
Inferring intelligence from observable behavior is a foundational challenge in artificial intelligence. We develop a theory of Bayesian intelligence for agents such as language mod…
Beliefs and Behavior in Language Models
Alex Smolin, Bryan Wilder
There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In addition to the inherent scie…
High-Stakes Decisions with Language Models: Insights from Emergency Triage
Khurram Yamin, Christopher Kelly, Bryan Wilder +1
High-stakes decisions under uncertainty, such as medical emergency triage, require more than accurate predictions. They depend on estimating the likelihood of alternative outcomes…
What Must Generalist Agents Remember?
Khurram Yamin, Namrata Deka, Maitreyi Swaroop +3
This paper develops a formal account of what generalist agents must store in memory in order to act near-optimally across multiple environments and goals. It shows that when two do…
Can Revealed Preferences Clarify LLM Alignment and Steering?
Khurram Yamin, Jingjing Tang, Eric Horvitz +1
LLMs are increasingly used to make or support high-stakes decisions under uncertainty, where alignment depends not only on factual accuracy but on how models weigh tradeoffs betwee…
When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMs
Khurram Yamin, Jingjing Tang, Santiago Cortes-Gomez +3
Large language models (LLMs) are increasingly deployed in high-stakes settings where good decisions require forming beliefs over the probability of unknown outcomes. However, it is…
Dependent Randomized Rounding for Budget Constrained Experimental Design
Khurram Yamin, Edward Kennedy, Bryan Wilder
Policymakers in resource-constrained settings require experimental designs that satisfy strict budget limits while ensuring precise estimation of treatment effects. We propose a fr…
Can LLMs Reconcile Knowledge Conflicts in Counterfactual Reasoning
Khurram Yamin, Gaurav Ghosal, Bryan Wilder
Large Language Models have been shown to contain extensive world knowledge in their parameters, enabling impressive performance on many knowledge intensive tasks. However, when dep…
Accounting for Missing Covariates in Heterogeneous Treatment Estimation
Khurram Yamin, Vibhhu Sharma, Ed Kennedy +1
Many applications of causal inference require using treatment effects estimated on a study population to make decisions in a separate target population. We consider the challenging…
Failure Modes of LLMs for Causal Reasoning on Narratives
Khurram Yamin, Shantanu Gupta, Gaurav R. Ghosal +2
The ability to robustly identify causal relationships is essential for autonomous decision-making and adaptation to novel scenarios. However, accurately inferring causal structure…
Orthogonal Causal Calibration
Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis +2
Estimates of heterogeneous treatment effects such as conditional average treatment effects (CATEs) and conditional quantile treatment effects (CQTEs) play an important role in real…