8 citations · 17 across the 8 of their papers we have counts for
6 papers · 1 filter
Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation
Ayan Sengupta, Vaibhav Seth, Arinjay Pathak +5
Large Language Models (LLMs) are highly resource-intensive to fine-tune due to their enormous size. While low-rank adaptation is a prominent parameter-efficient fine-tuning approac…
On Learning Action Costs from Input Plans
Marianela Morales, Alberto Pozanco, Giuseppe Canonaco +3
Most of the work on learning action models focus on learning the actions' dynamics from input plans. This allows us to specify the valid plans of a planning task. However, very lit…
TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners
Tomas de la Rosa, Sriram Gopalakrishnan, Alberto Pozanco +2
Travel planning is a complex task that involves generating a sequence of actions related to visiting places subject to constraints and maximizing some user satisfaction criteria. T…
On the arithmetic complexity of computing Gröbner bases of comaximal determinantal ideals
Sriram Gopalakrishnan
Let be an matrix of homogeneous linear forms over a field . If the ideal generated by minors of size is Cohen-Macaulay, then the…
Optimized Gröbner basis algorithms for maximal determinantal ideals and critical point computations
Sriram Gopalakrishnan, Vincent Neiger, Mohab Safey El Din
Given polynomials and , all in for some field , we consider the problem of computing the critical points of the restriction of …
Synthetic Data Applications in Finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta +17
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applicatio…