408 research outputs found
ebalance: A Stata Package for Entropy Balancing
The Stata package ebalance implements entropy balancing, a multivariate reweighting method described in Hainmueller (2012 ) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population
ebalance: A Stata Package for Entropy Balancing
The Stata package ebalance implements entropy balancing, a multivariate reweighting method described in Hainmueller (2012 ) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population
Learning Reward for Physical Skills using Large Language Model
Learning reward functions for physical skills are challenging due to the vast
spectrum of skills, the high-dimensionality of state and action space, and
nuanced sensory feedback. The complexity of these tasks makes acquiring expert
demonstration data both costly and time-consuming. Large Language Models (LLMs)
contain valuable task-related knowledge that can aid in learning these reward
functions. However, the direct application of LLMs for proposing reward
functions has its limitations such as numerical instability and inability to
incorporate the environment feedback. We aim to extract task knowledge from
LLMs using environment feedback to create efficient reward functions for
physical skills. Our approach consists of two components. We first use the LLM
to propose features and parameterization of the reward function. Next, we
update the parameters of this proposed reward function through an iterative
self-alignment process. In particular, this process minimizes the ranking
inconsistency between the LLM and our learned reward functions based on the new
observations. We validated our method by testing it on three simulated physical
skill learning tasks, demonstrating effective support for our design choices.Comment: CoRL 2023, LangRob worksho
How to Tidy Up a Table: Fusing Visual and Semantic Commonsense Reasoning for Robotic Tasks with Vague Objectives
Vague objectives in many real-life scenarios pose long-standing challenges
for robotics, as defining rules, rewards, or constraints for optimization is
difficult. Tasks like tidying a messy table may appear simple for humans, but
articulating the criteria for tidiness is complex due to the ambiguity and
flexibility in commonsense reasoning. Recent advancement in Large Language
Models (LLMs) offers us an opportunity to reason over these vague objectives:
learned from extensive human data, LLMs capture meaningful common sense about
human behavior. However, as LLMs are trained solely on language input, they may
struggle with robotic tasks due to their limited capacity to account for
perception and low-level controls. In this work, we propose a simple approach
to solve the task of table tidying, an example of robotic tasks with vague
objectives. Specifically, the task of tidying a table involves not just
clustering objects by type and functionality for semantic tidiness but also
considering spatial-visual relations of objects for a visually pleasing
arrangement, termed as visual tidiness. We propose to learn a lightweight,
image-based tidiness score function to ground the semantically tidy policy of
LLMs to achieve visual tidiness. We innovatively train the tidiness score using
synthetic data gathered using random walks from a few tidy configurations. Such
trajectories naturally encode the order of tidiness, thereby eliminating the
need for laborious and expensive human demonstrations. Our empirical results
show that our pipeline can be applied to unseen objects and complex 3D
arrangements.Comment: RSSLRL2023 Worksho
Quadratic exponential BSDEs driven by a marked point process
In this paper, the well-posedness of quadratic exponential backward
stochastic differential equations driven by marked point process (MPP) under
unbounded terminal condition is studied based on a fixed point argument,
-method and an approximation procedure. We also prove the solvability
of the mean reflected quadratic exponential backward stochastic differential
equations driven by marked point process via -method
Mean reflected BSDE driven by a marked point process and application in insurance risk management
This paper aims to solve a super-hedging problem along with insurance
re-payment under running risk management constraints. The initial endowment for
the super-heding problem is characterized by a class of mean reflected backward
stochastic differential equation driven by a marked point process (MPP) and a
Brownian motion. By Lipschitz assumptions on the generators and proper
integrability on the terminal value, we give the well-posedness of this kind of
BSDEs by combining a representation theorem with the fixed point argument.Comment: arXiv admin note: text overlap with arXiv:2310.1472
Reflected BSDE driven by a marked point process with a convex/concave generator
In this paper, a class of reflected backward stochastic differential
equations (RBSDE) driven by a marked point process (MPP) with a convex/concave
generator is studied. Based on fixed point argument, -method and
truncation technique, the well-posedness of this kind of RBSDE with unbounded
terminal condition and obstacle is investigated. Besides, we present an
application on the pricing of American options via utility maximization, which
is solved by constructing an RBSDE with a convex generator.Comment: arXiv admin note: substantial text overlap with arXiv:2310.1472
Panel Data Visualization in R (panelView) and Stata (panelview)
We develop an R package panelView and a Stata package panelview for panel data visualization. They are designed to assist causal analysis with panel data and have three main functionalities: (1) They plot the treatment status and missing values in a panel dataset; (2) they visualize the temporal dynamics of the main variables of interest; and (3) they depict the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. These tools can help researchers better understand their panel datasets before conducting statistical analysis
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