242 citations · 689 across the 13 of their papers we have counts for
6 papers · 1 filter
Estimating Aggregate Properties In Relational Networks With Unobserved Data
Varun Embar, Sriram Srinivasan, Lise Getoor
Aggregate network properties such as cluster cohesion and the number of bridge nodes can be used to glean insights about a network's community structure, spread of influence and th…
OpenSpiel: A Framework for Reinforcement Learning in Games
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau +24
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi…
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi +4
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algo…
Two-temperature logistic regression based on the Tsallis divergence
Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan
We develop a variant of multiclass logistic regression that is significantly more robust to noise. The algorithm has one weight vector per class and the surrogate loss is a functio…
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Parameswaran Raman, Sriram Srinivasan, Shin Matsushima +3
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging. This is primarily because one needs to compute the log-partiti…
Domain-Independent Optimistic Initialization for Reinforcement Learning
Marlos C. Machado, Sriram Srinivasan, Michael Bowling
In Reinforcement Learning (RL), it is common to use optimistic initialization of value functions to encourage exploration. However, such an approach generally depends on the domain…