Risk Assessment and Statistical Significance in the Age of Foundation Models

no code implementations • 11 Oct 2023 • Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti, Kristjan Greenewald, Brian Belgodere, Mikhail Yurochkin, Jiri Navratil, Igor Melnyk, Jerret Ross

Using this framework, we formally develop a risk-aware approach for foundation model selection given guardrails quantified by specified metrics.

Econometrics Model Selection +1

Auditing and Generating Synthetic Data with Controllable Trust Trade-offs

no code implementations • 21 Apr 2023 • Brian Belgodere, Pierre Dognin, Adam Ivankay, Igor Melnyk, Youssef Mroueh, Aleksandra Mojsilovic, Jiri Navratil, Apoorva Nitsure, Inkit Padhi, Mattia Rigotti, Jerret Ross, Yair Schiff, Radhika Vedpathak, Richard A. Young

We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.

Model Selection Privacy Preserving

A Scalable Space-efficient In-database Interpretability Framework for Embedding-based Semantic SQL Queries

no code implementations • 23 Feb 2023 • Prabhakar Kudva, Rajesh Bordawekar, Apoorva Nitsure

AI-Powered database (AI-DB) is a novel relational database system that uses a self-supervised neural network, database embedding, to enable semantic SQL queries on relational tables.

Unlocking New York City Crime Insights using Relational Database Embeddings

no code implementations • 19 May 2020 • Apoorva Nitsure, Rajesh Bordawekar, Jose Neves

This paper demonstrates the use of the AI-Powered Database (AI-DB) in identifying non-obvious patterns in crime data that could serve as an aid to predictive policing measures.

Feature Engineering