62 citations · 238 across the 21 of their papers we have counts for
4 papers · 1 filter
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions
Ripon K. Saha, Akira Ura, Sonal Mahajan +6
Automatic machine learning, or AutoML, holds the promise of truly democratizing the use of machine learning (ML), by substantially automating the work of data scientists. However,…
Programming and Training Rate-Independent Chemical Reaction Networks
Marko Vasic, Cameron Chalk, Austin Luchsinger +2
Embedding computation in biochemical environments incompatible with traditional electronics is expected to have wide-ranging impact in synthetic biology, medicine, nanofabrication…
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)
Muhammad Usman, Wenxi Wang, Kaiyuan Wang +3
This paper introduces the MCML approach for empirically studying the learnability of relational properties that can be expressed in the well-known software design language Alloy. A…
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
Marko Vasic, Andrija Petrovic, Kaiyuan Wang +3
Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their ad…