From the 1 of 5 linked papers with an AI index.
5 papers
Prediction-Only Distillation in Linear and Logistic Regression
Hien Dang, Pratik Patil, Alessandro Rinaldo
Self-distillation (SD) is typically studied when the student is retrained on the teacher's original training inputs. In many practical deployments, however, the labeled training da…
Optimal Self-Distillation for Rectified Flow via Linear Probing
Saptarshi Roy, Debepsita Mukherjee, Pratik Patil
The paper investigates how to improve rectified flow generative models by optimally mixing teacher-generated velocity fields with true velocities, deriving a closed‑form mixing coe…
Evaluating Stochasticity in Deep Research Agents
Haotian Zhai, Elias Stengel-Eskin, Pratik Patil +1
Deep Research Agents (DRAs) are promising agentic systems that gather and synthesize information to support research across domains such as financial decision-making, medical analy…
Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning
Hien Dang, Pratik Patil, Alessandro Rinaldo
Self-distillation (SD) is the process of retraining a student on a mixture of ground-truth labels and the teacher's own predictions using the same architecture and training data. A…
Precise Model Benchmarking with Only a Few Observations
Riccardo Fogliato, Pratik Patil, Nil-Jana Akpinar +1
How can we precisely estimate a large language model's (LLM) accuracy on questions belonging to a specific topic within a larger question-answering dataset? The standard direct est…