1 citations · 2 across the 6 of their papers we have counts for
8 papers
VERSE: Verified Self-Evolving Optimizer for Agent Harnesses
Zekai Wang, Yingqiang Ge, Zekun Wang +6
Harness evolution improves an LLM agent's prompts, tools, and workflow, while the optimizer's own tools and procedures often remain fixed. We study whether an optimizer can improve…
SoftWater: Class-Aware Rate Allocation for Softmax Quantization
Joao V. Cavalcanti, Ashia C. Wilson
Post-training quantization pipelines routinely leave the softmax output layer in high precision. Yet in small LLMs with modern vocabularies, the head holds 15--30\% of all paramete…
Adaptive Kernel Selection for Stein Variational Gradient Descent
Moritz Melcher, Simon Weissmann, Ashia C. Wilson +1
A central challenge in Bayesian inference is efficiently approximating posterior distributions. Stein Variational Gradient Descent (SVGD) is a popular variational inference method…
Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs
Gerardo A. Flores, Alyssa H. Smith, Julia A. Fukuyama +1
Machine learning-based decision support systems are increasingly deployed in clinical settings, where probabilistic scoring functions are used to inform and prioritize patient mana…
Semivalue-based data valuation is arbitrary and gameable
Hannah Diehl, Ashia C. Wilson
The game-theoretic notion of the semivalue offers a popular framework for credit attribution and data valuation in machine learning. Semivalues have been proposed for a variety of…
Adaptive Acceleration Without Strong Convexity Priors Or Restarts
Joao V. Cavalcanti, Laurent Lessard, Ashia C. Wilson
A longstanding challenge in optimization is achieving optimal performance when the strong convexity parameter m is unknown. In this paper, we propose NAG-free, a simple extension o…
Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models
Vinith M. Suriyakumar, Rohan Alur, Ayush Sekhari +2
Text-to-image diffusion models rely on massive, web-scale datasets. Training them from scratch is computationally expensive, and as a result, developers often prefer to make increm…
Adaptive Backtracking Line Search
Joao V. Cavalcanti, Laurent Lessard, Ashia C. Wilson
Backtracking line search is foundational in numerical optimization. The basic idea is to adjust the step-size of an algorithm by a constant factor until some chosen criterion (e.g.…