16 citations · 37 across the 13 of their papers we have counts for
6 papers · 1 filter
Federated Learning with Neural Graphical Models
Urszula Chajewska, Harsh Shrivastava
Federated Learning (FL) addresses the need to create models based on proprietary data in such a way that multiple clients retain exclusive control over their data, while all benefi…
Knowledge Propagation over Conditional Independence Graphs
Urszula Chajewska, Harsh Shrivastava
Conditional Independence (CI) graph is a special type of a Probabilistic Graphical Model (PGM) where the feature connections are modeled using an undirected graph and the edge weig…
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models
Shima Imani, Ali Beyram, Harsh Shrivastava
In this paper, we introduce DiversiGATE, a unified framework that consolidates diverse methodologies for LLM verification. The proposed framework comprises two main components: Div…
MathPrompter: Mathematical Reasoning using Large Language Models
Shima Imani, Liang Du, Harsh Shrivastava
Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math probl…
Are uGLAD? Time will tell!
Shima Imani, Harsh Shrivastava
We frequently encounter multiple series that are temporally correlated in our surroundings, such as EEG data to examine alterations in brain activity or sensors to monitor body mov…
Neural Graph Revealers
Harsh Shrivastava, Urszula Chajewska
Sparse graph recovery methods work well where the data follows their assumptions but often they are not designed for doing downstream probabilistic queries. This limits their adopt…