Search Results for author:
Found 11 papers, 3 papers with code
Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction
We propose an approach for estimating the latent knowledge embedded inside large language models (LLMs).
Team Formation amidst Conflicts
We test and deploy our algorithms on real-world datasets and we show that our algorithms find assignments that are better than those found by natural baselines.
Understanding team collapse via probabilistic graphical models
In this work, we develop a graphical model to capture team dynamics.
Online Submodular Maximization via Online Convex Optimization
We study monotone submodular maximization under general matroid constraints in the online setting.
Finding teams that balance expert load and task coverage
The core idea in this line of work has been the strict requirement that the team of experts assigned to complete a given task should contain a superset of the skills required by the task.
Learn to Earn: Enabling Coordination within a Ride Hailing Fleet
In contrast, computer scientists primarily view it as a demand prediction problem with the goal of preemptively repositioning supply to such neighborhoods using black box coordinated multi agent deep reinforcement learning based approaches.
Algorithms for Hiring and Outsourcing in the Online Labor Market
In this paper, we provide algorithms for outsourcing and hiring workers in a general setting, where workers form a team and contribute different skills to perform a task.
Matrix completion with queries
Order&Extend is able identify and alleviate insufficient information by judiciously querying a small number of additional entries.
Targeted matrix completion
Classical approaches to matrix completion assume that the input partially-observed matrix is low rank.
Team Formation for Scheduling Educational Material in Massive Online Classes
The goal is to teach students within time frame d such that their potential for learning is maximized and find the best schedule for each group.
What do row and column marginals reveal about your dataset?
Numerous datasets ranging from group memberships within social networks to purchase histories on e-commerce sites are represented by binary matrices.
