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Found 12 papers, 3 papers with code
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis
This paper introduces the Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), a new method providing time-continuous functional outputs for both single and competing risks scenarios in survival analysis.
GamutMLP: A Lightweight MLP for Color Loss Recovery
Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values.
Policy Optimization with Linear Temporal Logic Constraints
We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints.
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications.
Imitation-Projected Programmatic Reinforcement Learning
First, we view our learning task as optimization in policy space, modulo the constraint that the desired policy has a programmatic representation, and solve this optimization problem using a form of mirror descent that takes a gradient step into the unconstrained policy space and then projects back onto the constrained space.
Batch Policy Learning under Constraints
When learning policies for real-world domains, two important questions arise: (i) how to efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate among different competing objectives and constraints.
A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability
The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective.
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics.
Hierarchical Imitation and Reinforcement Learning
We study how to effectively leverage expert feedback to learn sequential decision-making policies.
Coordinated Multi-Agent Imitation Learning
We study the problem of imitation learning from demonstrations of multiple coordinating agents.
Smooth Imitation Learning for Online Sequence Prediction
We study the problem of smooth imitation learning for online sequence prediction, where the goal is to train a policy that can smoothly imitate demonstrated behavior in a dynamic and continuous environment in response to online, sequential context input.
Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees
We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e. g., players in a basketball game).
