4 citations · 14 across the 12 of their papers we have counts for
12 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…
A Sinkhorn-type Algorithm for Constrained Optimal Transport
Xun Tang, Holakou Rahmanian, Michael Shavlovsky +3
Entropic optimal transport (OT) and the Sinkhorn algorithm have made it practical for machine learning practitioners to perform the fundamental task of calculating transport distan…
Accelerating Sinkhorn Algorithm with Sparse Newton Iterations
Xun Tang, Michael Shavlovsky, Holakou Rahmanian +4
Computing the optimal transport distance between statistical distributions is a fundamental task in machine learning. One remarkable recent advancement is entropic regularization a…
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…
Extended Formulations via Decision Diagrams
Yuta Kurokawa, Ryotaro Mitsuboshi, Haruki Hamasaki +3
We propose a general algorithm of constructing an extended formulation for any given set of linear constraints with integer coefficients. Our algorithm consists of two phases: firs…
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…
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…
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…
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…