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20162025
most citedToward Understanding Privileged Features Distillation in Learning-to-Rank

4 citations · 14 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.LG2024

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…

cs.LG2023

Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow

Yinuo Ren, Tesi Xiao, Tanmay Gangwani +4

Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications. We introduce a novel interacting particle method for MOO…

cs.LG2022★ 4 cited

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…

cs.LG2018

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…

cs.LG2017★ 2 cited

Online Dynamic Programming

Holakou Rahmanian, Manfred K. Warmuth, S. V. N. Vishwanathan

We propose a general method for combinatorial online learning problems whose offline optimization problem can be solved efficiently via a dynamic programming algorithm defined by a…

cs.LG2017★ 4 cited

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…

cs.LG2016★ 3 cited

Online Learning of Combinatorial Objects via Extended Formulation

Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan

The standard techniques for online learning of combinatorial objects perform multiplicative updates followed by projections into the convex hull of all the objects. However, this m…