4 citations · 8 across the 3 of their papers we have counts for
6 papers
Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation
Neeraj Gangwar, Anshuka Rangi, Rishabh Deshmukh +3
Parameter-efficient fine-tuning methods have emerged as a promising solution for adapting pre-trained models to various downstream tasks. While these methods perform well in single…
An efficient algorithm for entropic optimal transport under martingale-type constraints
Xun Tang, Michael Shavlovsky, Holakou Rahmanian +2
This work introduces novel computational methods for entropic optimal transport (OT) problems under martingale-type conditions. The considered problems include the discrete marting…
COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework
Yinuo Ren, Tesi Xiao, Michael Shavlovsky +2
In LLM alignment and many other ML applications, one often faces the Multi-Objective Fine-Tuning (MOFT) problem, i.e., fine-tuning an existing model with datasets labeled w.r.t. di…
Toward Understanding Privileged Features Distillation in Learning-to-Rank
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian +2
In learning-to-rank problems, a privileged feature is one that is available during model training, but not available at test time. Such features naturally arise in merchandised rec…
Online Non-Additive Path Learning under Full and Partial Information
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri +2
We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction. We pre…
Deep Embedding Forest: Forest-based Serving with Deep Embedding Features
Jie Zhu, Ying Shan, JC Mao +3
Deep Neural Networks (DNN) have demonstrated superior ability to extract high level embedding vectors from low level features. Despite the success, the serving time is still the bo…