3 citations · 6 across the 14 of their papers we have counts for
14 papers
Offline Constrained RLHF with Multiple Preference Oracles
Brenden Latham, Mehrdad Moharrami
We study offline constrained reinforcement learning from human feedback with multiple preference oracles. Motivated by applications that trade off performance with safety or fairne…
Tail Distribution of Regret in Optimistic Reinforcement Learning
Sajad Khodadadian, Mehrdad Moharrami
We derive instance-dependent tail bounds for the regret of optimism-based reinforcement learning in finite-horizon tabular Markov decision processes with unknown transition dynamic…
The Planted Spanning Tree Problem
Mehrdad Moharrami, Cristopher Moore, Jiaming Xu
We study the problem of detecting and recovering a planted spanning tree hidden within a complete, randomly weighted graph . Specifically, each edge has a non-nega…
A Stackelberg Game Model of Flocking
Chenlan Wang, Mehrdad Moharrami, Mingyan Liu
We study a Stackelberg game to examine how two agents determine to cooperate while competing with each other. Each selects an arrival time to a destination, the earlier one fetchin…
Backward and Forward Inference in Interacting Independent-Cascade Processes: A Scalable and Convergent Message-Passing Approach
Nouman Khan, Kangle Mu, Mehrdad Moharrami +1
We study the problems of estimating the past and future evolutions of two diffusion processes that spread concurrently on a network. Specifically, given a known network $G=(V, \ove…
On the Convergence of Modified Policy Iteration in Risk Sensitive Exponential Cost Markov Decision Processes
Yashaswini Murthy, Mehrdad Moharrami, R. Srikant
Modified policy iteration (MPI) is a dynamic programming algorithm that combines elements of policy iteration and value iteration. The convergence of MPI has been well studied in t…
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
Yashaswini Murthy, Mehrdad Moharrami, R. Srikant
Many policy-based reinforcement learning (RL) algorithms can be viewed as instantiations of approximate policy iteration (PI), i.e., where policy improvement and policy evaluation…
Rarest-First with Probabilistic-Mode-Suppression
Nouman Khan, Mehrdad Moharrami, Vijay Subramanian
Recent studies suggested that the BitTorrent's rarest-first protocol, owing to its work-conserving nature, can become unstable in the presence of non-persistent users. Consequently…
A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP
Mehrdad Moharrami, Yashaswini Murthy, Arghyadip Roy +1
We study the risk-sensitive exponential cost MDP formulation and develop a trajectory-based gradient algorithm to find the stationary point of the cost associated with a set of par…
Learning to Admit Optimally in an Queueing System with Unknown Service Rate
Saghar Adler, Mehrdad Moharrami, Vijay Subramanian
Motivated by applications of the Erlang-B blocking model and the extended model that allows for some queueing, beyond communication networks to sizing and pricing in pr…
Structural Stability of a Family of Group Formation Games
Chenlan Wang, Mehrdad Moharrami, Kun Jin +3
We introduce and study a group formation game in which individuals/agents, driven by self-interest, team up in disjoint groups so as to be in groups of high collective strength. Th…
The Erlang Weighted Tree, A New Branching Process
Mehrdad Moharrami, Vijay Subramanian, Mingyan Liu +1
In this paper, we study a new discrete tree and the resulting branching process, which we call the \textbf{E}rlang \textbf{W}eighted \textbf{T}ree(\textbf{EWT}). The EWT appears as…
The Planted Matching Problem: Phase Transitions and Exact Results
Mehrdad Moharrami, Cristopher Moore, Jiaming Xu
We study the problem of recovering a planted matching in randomly weighted complete bipartite graphs . For some unknown perfect matching , the weight of an edge is dr…
Impact of Community Structure on Cascades
Mehrdad Moharrami, Vijay Subramanian, Mingyan Liu +1
We study cascades under the threshold model on sparse random graphs with community structure. In this model, individuals adopt the new behavior based on how many neighbors have alr…