3 citations · 12 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
An Efficient Plugin Method for Metric Optimization of Black-Box Models
Siddartha Devic, Nurendra Choudhary, Anirudh Srinivasan +3
Many machine learning algorithms and classifiers are available only via API queries as a ``black-box'' -- that is, the downstream user has no ability to change, re-train, or fine-t…
Logits are All We Need to Adapt Closed Models
Gaurush Hiranandani, Haolun Wu, Subhojyoti Mukherjee +1
Many commercial Large Language Models (LLMs) are often closed-source, limiting developers to prompt tuning for aligning content generation with specific applications. While these m…
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…
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks
Ajay Jaiswal, Nurendra Choudhary, Ravinarayana Adkathimar +6
Graph Neural Networks (GNNs) have attracted immense attention in the past decade due to their numerous real-world applications built around graph-structured data. On the other hand…
Optimizing Black-box Metrics with Iterative Example Weighting
Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan +2
We consider learning to optimize a classification metric defined by a black-box function of the confusion matrix. Such black-box learning settings are ubiquitous, for example, when…
Rich-Item Recommendations for Rich-Users: Exploiting Dynamic and Static Side Information
Amar Budhiraja, Gaurush Hiranandani, Darshak Chhatbar +5
In this paper, we study the problem of recommendation system where the users and items to be recommended are rich data structures with multiple entity types and with multiple sourc…