1,355 research outputs found
Approximation results for a minâmax location-routing problem
AbstractThis paper studies a minâmax location-routing problem, which aims to determine both the home depots and the tours for a set of vehicles to service all the customers in a given weighted graph, so that the maximum working time of the vehicles is minimized. The minâmax objective is motivated by the needs of balancing or fairness in vehicle routing applications. We have proved that unless NP=P, it is impossible for the problem to have an approximation algorithm that achieves an approximation ratio of less than 4/3. Thus, we have developed the first constant ratio approximation algorithm for the problem. Moreover, we have developed new approximation algorithms for several variants, which improve the existing best approximation ratios in the previous literature
COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models
Prompt-based learning has been proved to be an effective way in pre-trained
language models (PLMs), especially in low-resource scenarios like few-shot
settings. However, the trustworthiness of PLMs is of paramount significance and
potential vulnerabilities have been shown in prompt-based templates that could
mislead the predictions of language models, causing serious security concerns.
In this paper, we will shed light on some vulnerabilities of PLMs, by proposing
a prompt-based adversarial attack on manual templates in black box scenarios.
First of all, we design character-level and word-level heuristic approaches to
break manual templates separately. Then we present a greedy algorithm for the
attack based on the above heuristic destructive approaches. Finally, we
evaluate our approach with the classification tasks on three variants of BERT
series models and eight datasets. And comprehensive experimental results
justify the effectiveness of our approach in terms of attack success rate and
attack speed. Further experimental studies indicate that our proposed method
also displays good capabilities in scenarios with varying shot counts, template
lengths and query counts, exhibiting good generalizability
Counterfactual Generative Models for Time-Varying Treatments
Estimating the counterfactual outcome of treatment is essential for
decision-making in public health and clinical science, among others. Often,
treatments are administered in a sequential, time-varying manner, leading to an
exponentially increased number of possible counterfactual outcomes.
Furthermore, in modern applications, the outcomes are high-dimensional and
conventional average treatment effect estimation fails to capture disparities
in individuals. To tackle these challenges, we propose a novel conditional
generative framework capable of producing counterfactual samples under
time-varying treatment, without the need for explicit density estimation. Our
method carefully addresses the distribution mismatch between the observed and
counterfactual distributions via a loss function based on inverse probability
weighting. We present a thorough evaluation of our method using both synthetic
and real-world data. Our results demonstrate that our method is capable of
generating high-quality counterfactual samples and outperforms the
state-of-the-art baselines
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