21 citations · 35 across the 10 of their papers we have counts for
10 papers
GiVA: Gradient-Informed Bases for Vector-Based Adaptation
Neeraj Gangwar, Rishabh Deshmukh, Michael Shavlovsky +4
As model sizes continue to grow, parameter-efficient fine-tuning has emerged as a powerful alternative to full fine-tuning. While LoRA is widely adopted among these methods, recent…
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…
Orbit: A Framework for Designing and Evaluating Multi-objective Rankers
Chenyang Yang, Tesi Xiao, Michael Shavlovsky +2
Machine learning in production needs to balance multiple objectives: This is particularly evident in ranking or recommendation models, where conflicting objectives such as user eng…
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…
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
Charlie Hou, Kiran Koshy Thekumparampil, Michael Shavlovsky +3
On tabular data, a significant body of literature has shown that current deep learning (DL) models perform at best similarly to Gradient Boosted Decision Trees (GBDTs), while signi…
Incentives for Truthful Evaluations
Luca de Alfaro, Marco Faella, Vassilis Polychronopoulos +1
We consider crowdsourcing problems where the users are asked to provide evaluations for items; the user evaluations are then used directly, or aggregated into a consensus value. La…
Incentives for Truthful Peer Grading
Luca de Alfaro, Michael Shavlovsky, Vassilis Polychronopoulos
Peer grading systems work well only if users have incentives to grade truthfully. An example of non-truthful grading, that we observed in classrooms, consists in students assigning…
CrowdGrader: Crowdsourcing the Evaluation of Homework Assignments
Luca de Alfaro, Michael Shavlovsky
Crowdsourcing offers a practical method for ranking and scoring large amounts of items. To investigate the algorithms and incentives that can be used in crowdsourcing quality evalu…