2 citations · 2 across the 4 of their papers we have counts for
4 papers
FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain
Rohan Deb, Kiran Thekumparampil, Kousha Kalantari +3
Supervised fine-tuning (SFT) is a standard approach to adapting large language models (LLMs) to new domains. In this work, we improve the statistical efficiency of SFT by selecting…
Comparing Few to Rank Many: Active Human Preference Learning using Randomized Frank-Wolfe
Kiran Koshy Thekumparampil, Gaurush Hiranandani, Kousha Kalantari +2
We study learning of human preferences from a limited comparison feedback. This task is ubiquitous in machine learning. Its applications such as reinforcement learning from human f…
Language-Model Prior Overcomes Cold-Start Items
Shiyu Wang, Hao Ding, Yupeng Gu +3
The growth of recommender systems (RecSys) is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming. The content in these sys…
Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances
Anusha Lalitha, Kousha Kalantari, Yifei Ma +2
We study the problem of best-arm identification (BAI) in the fixed-budget setting with heterogeneous reward variances. We propose two variance-adaptive BAI algorithms for this sett…