98 citations · 352 across the 15 of their papers we have counts for
4 papers · 1 filter
Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Slavin Ross, Been Kim +2
We often desire our models to be interpretable as well as accurate. Prior work on optimizing models for interpretability has relied on easy-to-quantify proxies for interpretability…
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…
Variational Particle Approximations
Ardavan Saeedi, Tejas D Kulkarni, Vikash Mansinghka +1
Approximate inference in high-dimensional, discrete probabilistic models is a central problem in computational statistics and machine learning. This paper describes discrete partic…
Distance Dependent Infinite Latent Feature Models
Samuel J. Gershman, Peter I. Frazier, David M. Blei
Latent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP…