3 citations · 12 across the 16 of their papers we have counts for
18 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…
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…
Context-Aware Clustering using Large Language Models
Sindhu Tipirneni, Ravinarayana Adkathimar, Nurendra Choudhary +5
Despite the remarkable success of Large Language Models (LLMs) in text understanding and generation, their potential for text clustering tasks remains underexplored. We observed th…
ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding
Zixuan Liu, Gaurush Hiranandani, Kun Qian +5
Developing text mining approaches to mine aspects from customer reviews has been well-studied due to its importance in understanding customer needs and product attributes. In contr…
Metric Elicitation; Moving from Theory to Practice
Safinah Ali, Sohini Upadhyay, Gaurush Hiranandani +2
Metric Elicitation (ME) is a framework for eliciting classification metrics that better align with implicit user preferences based on the task and context. The existing ME strategy…
Classification Performance Metric Elicitation and its Applications
Gaurush Hiranandani
Given a learning problem with real-world tradeoffs, which cost function should the model be trained to optimize? This is the metric selection problem in machine learning. Despite i…
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…
Quadratic Metric Elicitation for Fairness and Beyond
Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan +1
Metric elicitation is a recent framework for eliciting classification performance metrics that best reflect implicit user preferences based on the task and context. However, availa…
Fair Performance Metric Elicitation
Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi Koyejo
What is a fair performance metric? We consider the choice of fairness metrics through the lens of metric elicitation -- a principled framework for selecting performance metrics tha…
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…
Online Diverse Learning to Rank from Partial-Click Feedback
Prakhar Gupta, Gaurush Hiranandani, Harvineet Singh +3
Learning to rank is an important problem in machine learning and recommender systems. In a recommender system, a user is typically recommended a list of items. Since the user is un…
Clustered Monotone Transforms for Rating Factorization
Gaurush Hiranandani, Raghav Somani, Oluwasanmi Koyejo +1
Exploiting low-rank structure of the user-item rating matrix has been the crux of many recommendation engines. However, existing recommendation engines force raters with heterogene…
Performance Metric Elicitation from Pairwise Classifier Comparisons
Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta +1
Given a binary prediction problem, which performance metric should the classifier optimize? We address this question by formalizing the problem of Metric Elicitation. The goal of m…
Generating Appealing Brand Names
Gaurush Hiranandani, Pranav Maneriker, Harsh Jhamtani
Providing appealing brand names to newly launched products, newly formed companies or for renaming existing companies is highly important as it can play a crucial role in deciding…
Small circulant complex Hadamard matrices of Butson type
Gaurush Hiranandani, Jean-Marc Schlenker
We study the circulant complex Hadamard matrices of order whose entries are -th roots of unity. For prime we prove that the only such matrix, up to equivalence, is the…