7 citations · 9 across the 2 of their papers we have counts for
6 papers
Deep Successor Reinforcement Learning
Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam +1
Learning robust value functions given raw observations and rewards is now possible with model-free and model-based deep reinforcement learning algorithms. There is a third alternat…
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi +1
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient e…
Nonparametric Spherical Topic Modeling with Word Embeddings
Kayhan Batmanghelich, Ardavan Saeedi, Karthik Narasimhan +1
Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics…
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
Ardavan Saeedi, Matthew Hoffman, Matthew Johnson +1
We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov model (iHMM) that supports a simple, efficient inference scheme. The siHMM is well suited to segmentati…
Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Ardavan Saeedi, Vlad Firoiu, Vikash Mansinghka
Models of complex systems are often formalized as sequential software simulators: computationally intensive programs that iteratively build up probable system configurations given…
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi +1
Markov jump processes (MJPs) are used to model a wide range of phenomena from disease progression to RNA path folding. However, maximum likelihood estimation of parametric models l…