11 citations · 33 across the 10 of their papers we have counts for
10 papers
Formal Policy Enforcement for Agentic Systems
Nils Palumbo, Sarthak Choudhary, Jihye Choi +4
As agentic computing systems grow increasingly popular, they raise serious security concerns due to their susceptibility to various attacks. While prior research efforts aim to mit…
Prmpt: Sanitizing Sensitive Prompts for LLMs
Amrita Roy Chowdhury, David Glukhov, Divyam Anshumaan +4
The rise of large language models (LLMs) has introduced new privacy challenges, particularly during inference where sensitive information in prompts may be exposed to proprietary L…
PolicyLR: A Logic Representation For Privacy Policies
Ashish Hooda, Rishabh Khandelwal, Prasad Chalasani +2
Privacy policies are crucial in the online ecosystem, defining how services handle user data and adhere to regulations such as GDPR and CCPA. However, their complexity and frequent…
MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance
Jihye Choi, Nils Palumbo, Prasad Chalasani +4
In the era of Large Language Models (LLMs), given their remarkable text understanding and generation abilities, there is an unprecedented opportunity to develop new, LLM-based meth…
Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation
Xi Wu, Yang Guo, Jiefeng Chen +3
We consider representation learning (hypothesis class ) where training and test distributions can be different. Recent studies provide hi…
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
Wei Zhang, Thomas Kobber Panum, Somesh Jha +2
We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences. Existing work suffers from either limited mode…
Concise Explanations of Neural Networks using Adversarial Training
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury +2
We show new connections between adversarial learning and explainability for deep neural networks (DNNs). One form of explanation of the output of a neural network model in terms of…
Counterfactual-based Incrementality Measurement in a Digital Ad-Buying Platform
Prasad Chalasani, Ari Buchalter, Jaynth Thiagarajan +1
The problem of measuring the true incremental effectiveness of a digital advertising campaign is of increasing importance to marketers. With a large and increasing percentage of di…
An on-line algorithm for improving performance in navigation
Avrim Blum, Prasad Chalasani
Recent papers have shown optimally-competitive on-line strategies for a robot traveling from a point to a point in certain unknown geometric environments. We consider the q…
On the minimum latency problem
Avrim Blum, Prasad Chalasani, Don Coppersmith +3
We are given a set of points and a symmetric distance matrix giving the distance between and . We wish to construct a tour that minimizes $…