5 citations · 5 across the 9 of their papers we have counts for
12 papers
MLToolBench: Learning Tool-Augmented Agents for Machine Learning Development
Xin Yu, Lizhu Zhang, Jiamu Bai +7
Machine learning engineering (MLE) agents have made substantial progress, but learning through ML experimentation remains costly in time and computation. Synthetic environments red…
Difficulty-Aware Semantic-ID Optimization for Generative Recommendation
Xin Yu, Stephen Li, Sina Aghaei +6
Semantic-ID-based generative recommendation casts retrieval and ranking as autoregressive generation over hierarchical item identifiers. A common recipe is SFT followed by GRPO, ye…
Towards General Preference Alignment: Diffusion Models at Nash Equilibrium
Jiaming Hu, Jiamu Bai, Haoyu Wang +2
Reinforcement learning from human feedback (RLHF) has been popular for aligning text-to-image (T2I) diffusion models with human preferences. As a mainstream branch of RLHF, Direct…
Accurate and Scalable Matrix Mechanisms via Divide and Conquer
Guanlin He, Yingtai Xiao, Jiamu Bai +4
Matrix mechanisms are often used to provide unbiased differentially private query answers when publishing statistics or creating synthetic data. Recent work has developed matrix me…
Composition for Pufferfish Privacy
Jiamu Bai, Guanlin He, Xin Gu +2
When creating public data products out of confidential datasets, inferential/posterior-based privacy definitions, such as Pufferfish, provide compelling privacy semantics for data…
Towards Better Optimization For Listwise Preference in Diffusion Models
Jiamu Bai, Xin Yu, Meilong Xu +6
Reinforcement learning from human feedback (RLHF) has proven effectiveness for aligning text-to-image (T2I) diffusion models with human preferences. Although Direct Preference Opti…
Correlating Cross-Iteration Noise for DP-SGD using Model Curvature
Xin Gu, Yingtai Xiao, Guanlin He +3
Differentially private stochastic gradient descent (DP-SGD) offers the promise of training deep learning models while mitigating many privacy risks. However, there is currently a l…
Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers
Xingyue Huang, Rishabh, Gregor Franke +43
Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RL…
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
Yan Gao, Massimo Roberto Scamarcia, Javier Fernandez-Marques +18
Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data, raisin…
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Jiamu Bai, Daoyuan Chen, Bingchen Qian +2
Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs). While promising, it raises significant challenges due to t…
Among Us: Adversarially Robust Collaborative Perception by Consensus
Yiming Li, Qi Fang, Jiamu Bai +3
Multiple robots could perceive a scene (e.g., detect objects) collaboratively better than individuals, although easily suffer from adversarial attacks when using deep learning. Thi…
Artificial Intelligence for Scientific Research: Authentic Research Education Framework
Sergey V Samsonau, Aziza Kurbonova, Lu Jiang +7
We report a framework that enables the wide adoption of authentic research educational methodology at various schools by addressing common barriers. The guiding principles we prese…