3 citations · 4 across the 8 of their papers we have counts for
8 papers
Packing Linear Programs and Fractional Knapsack using Comparison Oracles
Ritabrata Barat, Siddharth Barman, Nirjhar Das +1
We study the problem of recovering the objective of a packing linear program when the algorithm accesses only comparison information about optimal solutions under varying constrain…
Task-Specific Multimodal Question Answering Agents via Confidence Calibration and Incremental Reasoning for QANTA 2026
Nirjhar Das, Md. Al-Mamun Provath
We present our submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA). Quanta evaluates multimodal quizbowl sys…
Welfarist Formulations for Diverse Similarity Search
Siddharth Barman, Nirjhar Das, Shivam Gupta +1
Nearest Neighbor Search (NNS) is a fundamental problem in data structures with wide-ranging applications, such as web search, recommendation systems, and, more recently, retrieval-…
Cost Efficient Fairness Audit Under Partial Feedback
Nirjhar Das, Mohit Sharma, Praharsh Nanavati +2
We study the problem of auditing the fairness of a given classifier under partial feedback, where true labels are available only for positively classified individuals, (e.g., loan…
Linear Contextual Bandits with Hybrid Payoff: Revisited
Nirjhar Das, Gaurav Sinha
We study the Linear Contextual Bandit problem in the hybrid reward setting. In this setting every arm's reward model contains arm specific parameters in addition to parameters shar…
Generalized Linear Bandits with Limited Adaptivity
Ayush Sawarni, Nirjhar Das, Siddharth Barman +1
We study the generalized linear contextual bandit problem within the constraints of limited adaptivity. In this paper, we present two algorithms, and $\texttt{R…
Active Preference Optimization for Sample Efficient RLHF
Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano +1
Large Language Models (LLMs) aligned using Reinforcement Learning from Human Feedback (RLHF) have shown remarkable generation abilities in numerous tasks. However, collecting high-…
Inverse Reinforcement Learning With Constraint Recovery
Nirjhar Das, Arpan Chattopadhyay
In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems. In standard IRL problems, the inverse learn…