263 citations · 370 across the 9 of their papers we have counts for
7 papers · 1 filter
Type-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Data
Chu-Cheng Lin, Daiyi Peng, Yifeng Lu +2
Reliably composing Large Language Models (LLMs) for complex, multi-step workflows remains a significant challenge. The dominant paradigm -- optimizing discrete prompts in a pipelin…
ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions
Yule Li, Yifeng Lu, Zhen Wang +3
In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks. Although GNNs inherently encode specific inductive biases (e.g., ac…
Large Language Models as Optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu +4
Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applicati…
Brainformers: Trading Simplicity for Efficiency
Yanqi Zhou, Nan Du, Yanping Huang +12
Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-for…
PyGlove: Efficiently Exchanging ML Ideas as Code
Daiyi Peng, Xuanyi Dong, Esteban Real +2
The increasing complexity and scale of machine learning (ML) has led to the need for more efficient collaboration among multiple teams. For example, when a research team invents a…
PyGlove: Symbolic Programming for Automated Machine Learning
Daiyi Peng, Xuanyi Dong, Esteban Real +7
Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML soft…