Model: bhujith10/bert-large-uncased-setfit_finetuned
Author: bhujith10
Pipeline: text-classification
Library: setfit
Tags: setfit, safetensors, bert, sentence-transformers, text-classification, generated_from_setfit_trainer, dataset:bhujith10/multi_class_classification_dataset, arxiv:2209.11055, base_model:google-bert/bert-large-uncased, base_model:finetune:google-bert/bert-large-uncased, region:us
Base models (finetune): id: google-bert/bert-large-uncased
Card metadata: tags: setfit, sentence-transformers, text-classification, generated_from_setfit_trainer
widget: Title: Robust Contextual Bandit via the Capped-$\ell_{2}$ norm,
Abstract: This paper considers the actor-critic contextual bandit for the mobile health
(mHealth) intervention. The state-of-the-art decision-making methods in mHealth
generally assume that the noise in the dynamic system follows the Gaussian
distribution. Those methods use the least-square-based algorithm to estimate
the expected reward, which is prone to the existence of outliers. To deal with
the issue of outliers, we propose a novel robust actor-critic contextual bandit
method for the mHealth intervention. In the critic updating, the
capped-$\ell_{2}$ norm is used to measure the approximation error, which
prevents outliers from dominating our objective. A set of weights could be
achieved from the critic updating. Considering them gives a weighted objective
for the actor updating. It provides the badly noised sample in the critic
updating with zero weights for the actor updating. As a result, the robustness
of both actor-critic updating is enhanced. There is a key parameter in the
capped-$\ell_{2}$ norm. We provide a reliable method to properly set it by
making use of one of the most fundamental definitions of outliers in
statistics. Extensive experiment results demonstrate that our method can
achieve almost identical results compared with the state-of-the-art methods on
the dataset without outliers and dramatically outperform them on the datasets
noised by outliers., Title: Increasing the Reusability of Enforcers with Lifecycle Events,
Abstract: Runtime enforcement can be effectively used to improve the reliability of
software applications. However, it often requires the definition of ad hoc
policies and enforcement strategies, which might be expensive to identify and
implement. This paper discusses how to exploit lifecycle events to obtain
useful enforcement strategies that can be easily reused across applications,
thus reducing the cost of adoption of the runtime enforcement technology. The
paper finally sketches how this idea can be used to define libraries that can
automatically overcome problems related to applications misusing them., Title: Generalized Minimum Distance Estimators in Linear Regression with Dependent Errors,
Abstract: This paper discusses minimum distance estimation method in the linear
regression model with dependent errors which are strongly mixing. The
regression parameters are estimated through the minimum distance estimation
method, and asymptotic distributional properties of the estimators are
discussed. A simulation study compares the performance of the minimum distance
estimator with other well celebrated estimator. This simulation study shows the
superiority of the minimum distance estimator over another estimator. KoulMde
(R package) which was used for the simulation study is available online. See
section 4 for the detail., Title: On the isoperimetric quotient over scalar-flat conformal classes,
Abstract: Let $(M,g)$ be a smooth compact Riemannian manifold of dimension $n$ with
smooth boundary $\partial M$. Suppose that $(M,g)$ admits a scalar-flat
conformal metric. We prove that the supremum of the isoperimetric quotient over
the scalar-flat conformal class is strictly larger than the best constant of
the isoperimetric inequality in the Euclidean space, and consequently is
achieved, if either (i) $n\ge 12$ and $\partial M$ has a nonumbilic point; or
(ii) $n\ge 10$, $\partial M$ is umbilic and the Weyl tensor does not vanish at
some boundary point., Title: Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds,
Abstract: Monte Carlo Tree Search (MCTS), most famously used in game-play artificial
intelligence (e.g., the game of Go), is a well-known strategy for constructing
approximate solutions to sequential decision problems. Its primary innovation
is the use of a heuristic, known as a default policy, to obtain Monte Carlo
estimates of downstream values for states in a decision tree. This information
is used to iteratively expand the tree towards regions of states and actions
that an optimal policy might visit. However, to guarantee convergence to the
optimal action, MCTS requires the entire tree to be expanded asymptotically. In
this paper, we propose a new technique called Primal-Dual MCTS that utilizes
sampled information relaxation upper bounds on potential actions, creating the
possibility of "ignoring" parts of the tree that stem from highly suboptimal
choices. This allows us to prove that despite converging to a partial decision
tree in the limit, the recommended action from Primal-Dual MCTS is optimal. The
new approach shows significant promise when used to optimize the behavior of a
single driver navigating a graph while operating on a ride-sharing platform.
Numerical experiments on a real dataset of 7,000 trips in New Jersey suggest
that Primal-Dual MCTS improves upon standard MCTS by producing deeper decision
trees and exhibits a reduced sensitivity to the size of the action space.
metrics: accuracy
pipeline_tag: text-classification
library_name: setfit
inference: no
datasets: bhujith10/multi_class_classification_dataset
base_model: google-bert/bert-large-uncased
Likes: 0 | All-time downloads: 67
Created: 2024-12-26 15:05:37