no code implementations • 19 Aug 2022 • Jared Markowitz, Ryan W. Gardner, Ashley Llorens, Raman Arora, I-Jeng Wang
Without cost constraints, we find that pessimistic risk profiles can be used to reduce cost while improving total reward accumulation.
no code implementations • 2 May 2022 • Edward W. Staley, Jared Markowitz
After training, the layer can be arbitrarily reduced in width to exchange performance for narrowness.
no code implementations • 1 Dec 2021 • Edward W. Staley, Chace Ashcraft, Benjamin Stoler, Jared Markowitz, Gautam Vallabha, Christopher Ratto, Kapil D. Katyal
Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time.
no code implementations • 29 Sep 2021 • Jared Markowitz, Ryan Gardner, Ashley Llorens, Raman Arora, I-Jeng Wang
Standard deep reinforcement learning (DRL) agents aim to maximize expected reward, considering collected experiences equally in formulating a policy.
no code implementations • 22 Dec 2020 • Kapil Katyal, Yuxiang Gao, Jared Markowitz, Sara Pohland, Corban Rivera, I-Jeng Wang, Chien-Ming Huang
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments.
no code implementations • 11 Dec 2020 • Nathan Drenkow, Philippe Burlina, Neil Fendley, Onyekachi Odoemene, Jared Markowitz
We interpret both detection problems through a probabilistic, Bayesian lens, whereby the objectness maps produced by our method serve as priors in a maximum-a-posteriori approach to the detection step.
no code implementations • 7 Nov 2018 • Jared Markowitz, Ryan W. Gardner, Ashley J. Llorens
This paper provides a complexity analysis for the game of reconnaissance blind chess (RBC), a recently-introduced variant of chess where each player does not know the positions of the opponent's pieces a priori but may reveal a subset of them through chosen, private sensing actions.
no code implementations • 8 Dec 2017 • Jared Markowitz, Aurora C. Schmidt, Philippe M. Burlina, I-Jeng Wang
We address zero-shot (ZS) learning, building upon prior work in hierarchical classification by combining it with approaches based on semantic attribute estimation.