6 citations · 8 across the 6 of their papers we have counts for
8 papers
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing
Xiaolong Xu, Hao Jiang, Bo Jiang +5
Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis rather than…
OptiCom : A Unified Framework for State-Conditioned Composition in LLM-Driven Optimization
Chenxing Wei, Sichen Liu, Lizhao Liu +6
Large language models (LLMs) are increasingly deployed to solve complex scientific and practical problems via iterative optimization. However, dynamically coordinating diverse sear…
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
Chenyu Huang, Jianghao Lin, Zhengyang Tang +4
We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated by recent advances in LLM-based e…
Efficient Token-Level Policy Optimization for Masked Diffusion Models via Velocity Alignment
Chenxing Wei, Jiazhen Kang, Hong Wang +8
Reinforcement Learning with Verifiable Rewards (RLVR) successfully aligns autoregressive models in rigorous domains like mathematical reasoning and code generation. However, applyi…
Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation
Chenxing Wei, Hong Wang, Ying He +4
Test-time policy adaptation for multi-turn interactions (T2PAM) is essential for aligning Large Language Models (LLMs) with dynamic user needs during inference time. However, exist…
Generative Expansion of Small Datasets: An Expansive Graph Approach
Vahid Jebraeeli, Bo Jiang, Hamid Krim +1
Limited data availability in machine learning significantly impacts performance and generalization. Traditional augmentation methods enhance moderately sufficient datasets. GANs st…
Koopcon: A new approach towards smarter and less complex learning
Vahid Jebraeeli, Bo Jiang, Derya Cansever +1
In the era of big data, the sheer volume and complexity of datasets pose significant challenges in machine learning, particularly in image processing tasks. This paper introduces a…
Volterra Neural Networks (VNNs)
Siddharth Roheda, Hamid Krim
The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning. In an attempt to reduce the com…