Search Results for author: Jared Markowitz

Found 8 papers, 0 papers with code

A Risk-Sensitive Approach to Policy Optimization

no code implementations19 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.

Decision Making

Triangular Dropout: Variable Network Width without Retraining

no code implementations2 May 2022 Edward W. Staley, Jared Markowitz

After training, the layer can be arbitrarily reduced in width to exchange performance for narrowness.

Reinforcement Learning (RL)

A Risk-Sensitive Policy Gradient Method

no code implementations29 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.

Decision Making

Learning a Group-Aware Policy for Robot Navigation

no code implementations22 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.

Social Navigation

Addressing Visual Search in Open and Closed Set Settings

no code implementations11 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.

object-detection Object Detection

On the Complexity of Reconnaissance Blind Chess

no code implementations7 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.

Decision Making

Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning

no code implementations8 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.

General Classification Zero-Shot Learning

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