3 citations · 9 across the 22 of their papers we have counts for
27 papers
Social herding in mean field games
Deepanshu Vasal
In this paper, we consider a mean field model of social behavior where there are an infinite number of players, each of whom observes a type privately that represents her preferenc…
Mean field teams and games with correlated types
Deepanshu Vasal
Mean field games have traditionally been defined~[1,2] as a model of large scale interaction of players where each player has a private type that is independent across the players.…
Master equation of discrete-time Stackelberg mean field games with multiple leaders
Deepanshu Vasal
In this paper, we consider a discrete-time Stackelberg mean field game with a finite number of leaders, a finite number of major followers and an infinite number of minor followers…
A dynamic program to achieve capacity of multiple access channel with noiseless feedback
Deepanshu Vasal
In this paper, we consider the problem of evaluating capacity expression of a multiple access channel (MAC) with noiseless feedback. So far, the capacity expression for this channe…
Master Equation for Discrete-Time Stackelberg Mean Field Games with single leader
Deepanshu Vasal, Randall Berry
In this paper, we consider a discrete-time Stackelberg mean field game with a leader and an infinite number of followers. The leader and the followers each observe types privately…
A dynamic program for linear sequential coding for Gaussian MAC with noisy feedback
Deepanshu Vasal
In this paper consider a two user multiple access channel with noisy feedback. There are two senders with independent messages who transmit symbols across an additive white Gaussia…
Convergence of Generalized Belief Propagation Algorithm on Graphs with Motifs
Yitao Chen, Deepanshu Vasal
Belief propagation is a fundamental message-passing algorithm for numerous applications in machine learning. It is known that belief propagation algorithm is exact on tree graphs.…
A Phase Transition in Large Network Games
Abhishek Shende, Deepanshu Vasal, Sriram Vishwanath
In this paper, we use a model of large random network game where the agents plays selfishly and are affected by their neighbors, to explore the conditions under which the Nash equi…
Linear Coding for AWGN channels with Noisy Output Feedback via Dynamic Programming
Rajesh Mishra, Deepanshu Vasal, Hyeji Kim
The optimal coding scheme for communicating a Gaussian message over an Additive White Gaussian noise (AWGN) channel with AWGN output feedback, with a limited number of transmission…
Network Design for Social Welfare
Abhishek Shende, Deepanshu Vasal, Sriram Vishwanath
In this paper, we consider the problem of network design on network games. We study the conditions on the adjacency matrix of the underlying network to design a game such that the…
Multi-Agent Decentralized Belief Propagation on Graphs
Yitao Chen, Deepanshu Vasal
We consider the problem of interactive partially observable Markov decision processes (I-POMDPs), where the agents are located at the nodes of a communication network. Specifically…
Existence of structured perfect Bayesian equilibrium in dynamic games of asymmetric information
Deepanshu Vasal
In~[1],authors considered a general finite horizon model of dynamic game of asymmetric information, where N players have types evolving as independent Markovian process, where each…
Model-free Reinforcement Learning for Stochastic Stackelberg Security Games
Rajesh K Mishra, Deepanshu Vasal, Sriram Vishwanath
In this paper, we consider a sequential stochastic Stackelberg game with two players, a leader and a follower. The follower has access to the state of the system while the leader d…
Dynamic information design
Deepanshu Vasal
We consider the problem of dynamic information design with one sender and one receiver where the sender observers a private state of the system and takes an action to send a signal…
Fault Tolerant Equilibria in Anonymous Games: best response correspondences and fixed points
Deepanshu Vasal, Randall Berry
The notion of fault tolerant Nash equilibria has been introduced as a way of studying the robustness of Nash equilibria. Under this notion, a fixed number of players are allowed to…
Sequential decomposition of stochastic Stackelberg games
Deepanshu Vasal
In this paper, we consider a discrete-time stochastic Stackelberg game with a single leader and multiple followers. Both the followers and the leader together have conditionally in…
Model-free Reinforcement Learning for Non-stationary Mean Field Games
Rajesh K Mishra, Deepanshu Vasal, Sriram Vishwanath
In this paper, we consider a finite horizon, non-stationary, mean field games (MFG) with a large population of homogeneous players, sequentially making strategic decisions, where e…
Decentralized multi-agent reinforcement learning with shared actions
Rajesh K Mishra, Deepanshu Vasal, Sriram Vishwanath
In this paper, we propose a novel model-free reinforcement learning algorithm to compute the optimal policies for a multi-agent system with cooperative agents where each agent…
Mechanism Design for Large Scale Network Utility Maximization
Meng Zhang, Deepanshu Vasal
Network utility maximization (NUM) is a general framework for designing distributed optimization algorithms for large-scale networks. An economic challenge arises in the presence o…
Sequential decomposition of discrete memoryless channel with noisy feedback
Deepanshu Vasal
In this paper, we consider a discrete memoryless point to point channel with noisy feedback, where there is a sender with a private message that she wants to communicate to a recei…
Master equation of discrete time graphon mean field games and teams
Deepanshu Vasal, Rajesh K Mishra, Sriram Vishwanath
In this paper, we present a sequential decomposition algorithm equivalent of Master equation to compute GMFE of GMFG and graphon optimal Markovian policies (GOMPs) of graphon mean…
Markov perfect equilibria in non-stationary mean-field games
Deepanshu Vasal
In this paper, we consider both finite and infinite horizon discounted dynamic mean-field games where there is a large population of homogeneous players sequentially making strateg…
Incentive design for learning in user-recommendation systems with time-varying states
Deepanshu Vasal, Vijay Subramanian, Achilleas Anastasopoulos
We consider the problem of how strategic users with asymmetric information can learn an underlying time varying state in a user-recommendation system. Users who observe private sig…
Sequential decomposition of repeated games with asymmetric information and dependent states
Deepanshu Vasal
We consider a finite horizon repeated game with selfish players who observe their types privately and take actions, which are publicly observed. Their actions and types jointly…