13 papers
Quantum-Informed Portfolio Selection: An End-to-End Pipeline Validated on Trapped-Ion Hardware with Real Market Data
Romina Yalovetzky, Martin J. A. Schuetz, Zichang He +11
The paper presents a hybrid quantum‑classical pipeline (qReduMIS) that uses QAOA measurements to guide reductions for solving portfolio diversification formulated as a Maximum Inde…
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray +3
We present MetaTT, a Tensor Train (TT) adapter framework for fine-tuning of pre-trained transformers. MetaTT enables flexible and parameter-efficient model adaptation by using a si…
Entropy Distribution as a Fingerprint for Hallucinations in Generative Models
Mattia J. Villani, Pranav Deshpande, Akshay Seshadri +2
Large Language Models (LLMs) often generate factually incorrect outputs, commonly termed hallucinations, that undermine trust and limit deployment in high-stakes settings. Existing…
Anytime Training with Schedule-Free Spectral Optimization
Anuj Apte, Pranav Deshpande, Niraj Kumar +2
Standard neural network training relies on learning-rate schedules tied to a fixed horizon, leading to strong path dependence and costly re-tuning as data availability changes. Sch…
A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values
Tyler Chen, Akshay Seshadri, Mattia J. Villani +7
Shapley values have emerged as a critical tool for explaining which features impact the decisions made by machine learning models. However, computing exact Shapley values is diffic…
Provably faster randomized and quantum algorithms for -means clustering via uniform sampling
Tyler Chen, Archan Ray, Akshay Seshadri +6
The -means algorithm (Lloyd's algorithm) is a widely used method for clustering unlabeled data. A key bottleneck of the -means algorithm is that each iteration requires time…