20 citations · 35 across the 17 of their papers we have counts for
18 papers
MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs
Shubhadip Nag, Srinjoy Das, Agniva Saha +5
LLMs can be conveniently adapted to a diverse set of tasks, e.g, prediction, question-answering tasks, etc, using appropriate prompts with few-shot examples. Biased or harmful conc…
Sample Efficient Demonstration Selection for In-Context Learning
Kiran Purohit, V Venktesh, Sourangshu Bhattacharya +1
The in-context learning paradigm with LLMs has been instrumental in advancing a wide range of natural language processing tasks. The selection of few-shot examples (exemplars / dem…
EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning
Kiran Purohit, Venktesh V, Raghuram Devalla +3
Answering reasoning-based complex questions over text and hybrid sources, including tables, is a challenging task. Recent advances in large language models (LLMs) have enabled in-c…
A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs
Kiran Purohit, Anurag Reddy Parvathgari, Sourangshu Bhattacharya
Deep convolutional neural networks (CNNs) have achieved impressive performance in many computer vision tasks. However, their large model sizes require heavy computational resources…
VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI
Soumi Das, Shubhadip Nag, Shreyyash Sharma +2
Trustworthy AI is crucial to the widespread adoption of AI in high-stakes applications with fairness, robustness, and accuracy being some of the key trustworthiness metrics. In thi…
In-Context Ability Transfer for Question Decomposition in Complex QA
Venktesh V, Sourangshu Bhattacharya, Avishek Anand
Answering complex questions is a challenging task that requires question decomposition and multistep reasoning for arriving at the solution. While existing supervised and unsupervi…
A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning
Kiran Purohit, Soumi Das, Sourangshu Bhattacharya +1
Federated Learning systems are increasingly subjected to a multitude of model poisoning attacks from clients. Among these, edge-case attacks that target a small fraction of the inp…
Opponent-aware Role-based Learning in Team Competitive Markov Games
Paramita Koley, Aurghya Maiti, Niloy Ganguly +1
Team competition in multi-agent Markov games is an increasingly important setting for multi-agent reinforcement learning, due to its general applicability in modeling many real-lif…
CheckSel: Efficient and Accurate Data-valuation Through Online Checkpoint Selection
Soumi Das, Manasvi Sagarkar, Suparna Bhattacharya +1
Data valuation and subset selection have emerged as valuable tools for application-specific selection of important training data. However, the efficiency-accuracy tradeoffs of stat…
Offsetting Unequal Competition through RL-assisted Incentive Schemes
Paramita Koley, Aurghya Maiti, Sourangshu Bhattacharya +1
This paper investigates the dynamics of competition among organizations with unequal expertise. Multi-agent reinforcement learning has been used to simulate and understand the impa…
MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
Rajdeep Mukherjee, Uppada Vishnu, Hari Chandana Peruri +4
Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account o…
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction
Rajdeep Mukherjee, Tapas Nayak, Yash Butala +2
Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinio…
AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations
Sk Mainul Islam, Sourangshu Bhattacharya
Aspect level sentiment classification (ALSC) is a difficult problem with state-of-the-art models showing less than 80% macro-F1 score on benchmark datasets. Existing models do not…
Finding High-Value Training Data Subset through Differentiable Convex Programming
Soumi Das, Arshdeep Singh, Saptarshi Chatterjee +2
Finding valuable training data points for deep neural networks has been a core research challenge with many applications. In recent years, various techniques for calculating the "v…
Convex Online Video Frame Subset Selection using Multiple Criteria for Data Efficient Autonomous Driving
Soumi Das, Harikrishna Patibandla, Suparna Bhattacharya +3
Training vision-based Urban Autonomous driving models is a challenging problem, which is highly researched in recent times. Training such models is a data-intensive task requiring…
Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach
Paramita Koley, Avirup Saha, Sourangshu Bhattacharya +2
The networked opinion diffusion in online social networks (OSN) is often governed by the two genres of opinions - endogenous opinions that are driven by the influence of social con…
Scalable Backdoor Detection in Neural Networks
Haripriya Harikumar, Vuong Le, Santu Rana +3
Recently, it has been shown that deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model mi…
Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews
Rajdeep Mukherjee, Hari Chandana Peruri, Uppada Vishnu +3
Manually extracting relevant aspects and opinions from large volumes of user-generated text is a time-consuming process. Summaries, on the other hand, help readers with limited tim…