2 citations · 4 across the 16 of their papers we have counts for
16 papers
Mitigating LLM sycophancy with RL-based fine-tuning: Bayesian Truth Serum approach
Serhii Mytsyk, Yiming Zhang, Vikram Krishnamurthy
Large language models (LLMs) frequently exhibit \emph{sycophancy}: they adapt their answers to a user's stated beliefs or preferences instead of reporting what they hold to be true…
Emergence of Preferential Attachment and Glass-Ceiling Effects in Autonomous Networks of LLMs
Yiming Zhang, Vikram Krishnamurthy
We investigate the emergence of structural disparities in networks of collaborating large language model (LLM) agents. When LLM agents autonomously choose collaborators, the result…
Collaborative QA using Interacting LLMs. Impact of Network Structure, Node Capability and Distributed Data
Adit Jain, Vikram Krishnamurthy, Yiming Zhang
In this paper, we model and analyze how a network of interacting LLMs performs collaborative question-answering (CQA) in order to estimate a ground truth given a distributed set of…
Asymmetric Network Games: -Potential Function and Learning
Kiran Rokade, Adit Jain, Francesca Parise +2
In a network game, players interact over a network and the utility of each player depends on his own action and on an aggregate of his neighbours' actions. Many real world networks…
Information Diffusion and Preferential Attachment in a Network of Large Language Models
Adit Jain, Vikram Krishnamurthy, Yiming Zhang
This paper models information diffusion in a network of Large Language Models (LLMs) that is designed to answer queries from distributed datasets, where the LLMs can hallucinate th…
Interacting Large Language Model Agents. Interpretable Models and Social Learning
Adit Jain, Vikram Krishnamurthy
This paper discusses the theory and algorithms for interacting large language model agents (LLMAs) using methods from statistical signal processing and microeconomics. While both f…
Sparse Linear Bandits with Blocking Constraints
Adit Jain, Soumyabrata Pal, Sunav Choudhary +3
We investigate the high-dimensional sparse linear bandits problem in a data-poor regime where the time horizon is much smaller than the ambient dimension and number of arms. We stu…
Large Deviations Analysis For Regret Minimizing Stochastic Approximation Algorithms
Hongjiang Qian, Vikram Krishnamurthy
Motivated by learning of correlated equilibria in non-cooperative games, we perform a large deviations analysis of a regret minimizing stochastic approximation algorithm. The regre…
Identifying Hate Speech Peddlers in Online Platforms. A Bayesian Social Learning Approach for Large Language Model Driven Decision-Makers
Adit Jain, Vikram Krishnamurthy
This paper studies the problem of autonomous agents performing Bayesian social learning for sequential detection when the observations of the state belong to a high-dimensional spa…
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization
Adit Jain, Vikram Krishnamurthy
This paper studies how a stochastic gradient algorithm (SG) can be controlled to hide the estimate of the local stationary point from an eavesdropper. Such problems are of signific…
Detecting Structural Shifts in Multivariate Hawkes Processes with Fréchet Statistics
Rui Luo, Vikram Krishnamurthy
This paper proposes a new approach for change point detection in multivariate Hawkes processes using Fréchet statistic of a network. The method splits the point process into overla…
Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution
Rui Luo, Vikram Krishnamurthy
This study presents a novel deep learning method, called GATv2-GCN, for predicting player performance in sports. To construct a dynamic player interaction graph, we leverage player…
Fréchet Statistics Based Change Point Detection in Dynamic Social Networks
Rui Luo, Vikram Krishnamurthy
This paper proposes a method to detect change points in dynamic social networks using Fréchet statistics. We address two main questions: (1) what metric can quantify the distances…
Mutual Information Measure for Glass Ceiling Effect in Preferential Attachment Models
Rui Luo, Buddhika Nettasinghe, Vikram Krishnamurthy
We propose a new way to measure inequalities such as the glass ceiling effect in attributed networks. Existing measures typically rely solely on node degree distribution or degree…
Mitigating Misinformation Spread on Blockchain Enabled Social Media Networks
Rui Luo, Vikram Krishnamurthy, Erik Blasch
The paper develops a blockchain protocol for a social media network (BE-SMN) to mitigate the spread of misinformation. BE-SMN is derived based on the information transmission-time…
Unifying Revealed Preference and Revealed Rational Inattention
Kunal Pattanayak, Vikram Krishnamurthy
This paper unifies two key results from economic theory, namely, revealed rational inattention and classical revealed preference. Revealed rational inattention tests for rationalit…