246 research outputs found
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms
We study the problem of designing adaptive multi-armed bandit algorithms that
perform optimally in both the stochastic setting and the adversarial setting
simultaneously (often known as a best-of-both-world guarantee). A line of
recent works shows that when configured and analyzed properly, the
Follow-the-Regularized-Leader (FTRL) algorithm, originally designed for the
adversarial setting, can in fact optimally adapt to the stochastic setting as
well. Such results, however, critically rely on an assumption that there exists
one unique optimal arm. Recently, Ito (2021) took the first step to remove such
an undesirable uniqueness assumption for one particular FTRL algorithm with the
-Tsallis entropy regularizer. In this work, we significantly
improve and generalize this result, showing that uniqueness is unnecessary for
FTRL with a broad family of regularizers and a new learning rate schedule. For
some regularizers, our regret bounds also improve upon prior results even when
uniqueness holds. We further provide an application of our results to the
decoupled exploration and exploitation problem, demonstrating that our
techniques are broadly applicable.Comment: Update the camera-ready version for NeurIPS 202
Multi-sensor Suboptimal Fusion Student's Filter
A multi-sensor fusion Student's filter is proposed for time-series
recursive estimation in the presence of heavy-tailed process and measurement
noises. Driven from an information-theoretic optimization, the approach extends
the single sensor Student's Kalman filter based on the suboptimal
arithmetic average (AA) fusion approach. To ensure computationally efficient,
closed-form density recursion, reasonable approximation has been used in
both local-sensor filtering and inter-sensor fusion calculation. The overall
framework accommodates any Gaussian-oriented fusion approach such as the
covariance intersection (CI). Simulation demonstrates the effectiveness of the
proposed multi-sensor AA fusion-based filter in dealing with outliers as
compared with the classic Gaussian estimator, and the advantage of the AA
fusion in comparison with the CI approach and the augmented measurement fusion.Comment: 8 pages, 8 figure
Comparative Transcriptomes and EVO-DEVO Studies Depending on Next Generation Sequencing
High throughput technology has prompted the progressive omics studies, including genomics and transcriptomics. We have reviewed the improvement of comparative omic studies, which are attributed to the high throughput measurement of next generation sequencing technology. Comparative genomics have been successfully applied to evolution analysis while comparative transcriptomics are adopted in comparison of expression profile from two subjects by differential expression or differential coexpression, which enables their application in evolutionary developmental biology (EVO-DEVO) studies. EVO-DEVO studies focus on the evolutionary pressure affecting the morphogenesis of development and previous works have been conducted to illustrate the most conserved stages during embryonic development. Old measurements of these studies are based on the morphological similarity from macro view and new technology enables the micro detection of similarity in molecular mechanism. Evolutionary model of embryo development, which includes the “funnel-like” model and the “hourglass” model, has been evaluated by combination of these new comparative transcriptomic methods with prior comparative genomic information. Although the technology has promoted the EVO-DEVO studies into a new era, technological and material limitation still exist and further investigations require more subtle study design and procedure
Complex Organ Mask Guided Radiology Report Generation
The goal of automatic report generation is to generate a clinically accurate
and coherent phrase from a single given X-ray image, which could alleviate the
workload of traditional radiology reporting. However, in a real-world scenario,
radiologists frequently face the challenge of producing extensive reports
derived from numerous medical images, thereby medical report generation from
multi-image perspective is needed. In this paper, we propose the Complex Organ
Mask Guided (termed as COMG) report generation model, which incorporates masks
from multiple organs (e.g., bones, lungs, heart, and mediastinum), to provide
more detailed information and guide the model's attention to these crucial body
regions. Specifically, we leverage prior knowledge of the disease corresponding
to each organ in the fusion process to enhance the disease identification phase
during the report generation process. Additionally, cosine similarity loss is
introduced as target function to ensure the convergence of cross-modal
consistency and facilitate model optimization.Experimental results on two
public datasets show that COMG achieves a 11.4% and 9.7% improvement in terms
of BLEU@4 scores over the SOTA model KiUT on IU-Xray and MIMIC, respectively.
The code is publicly available at https://github.com/GaryGuTC/COMG_model.Comment: 12 pages, 7 images. Accepted by WACV 202
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