2 citations · 4 across the 11 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…
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…
Predicting Language Models' Success at Zero-Shot Probabilistic Prediction
Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Patiño +7
Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics (e.g., to serve as risk models or…
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…
Novelty Detection for Election Fraud: A Case Study with Agent-Based Simulation Data
Khurram Yamin, Nima Jadali, Dima Nazzal +1
In this paper, we propose a robust election simulation model and independently developed election anomaly detection algorithm that demonstrates the simulation's utility. The simula…
Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes
Khurram Yamin, Haoyun Wang, Benoit Montreuil +1
In this paper, we attempt to detect an inflection or change-point resulting from the Covid-19 pandemic on supply chain data received from a large furniture company. To accomplish t…