√n-一致条件付き平均推定のためのデバイアス回帰
本稿では、高次元およびノンパラメトリック回帰を含む一般的な回帰設定において、√n-一致性を達成するデバイアス回帰推定量を提案する。
Debiased Regression: Achieving √n-Consistency in Conditional Mean Estimation for High-Dimensional and Nonparametric Regression
This paper introduces a novel debiasing technique for regression estimators, enabling √n-consistency and asymptotic normality even in high-dimensional and nonparametric settings, which traditionally suffer from slower convergence rates.
Learning Differentiable Surrogate Losses for Structured Prediction Using Contrastive Learning and Explicit Loss Embedding
The paper proposes a novel framework called Explicit Loss Embedding (ELE) that leverages contrastive learning to learn differentiable surrogate losses for structured prediction, improving performance and enabling the prediction of new structures.
Analysis of SLAC Neural Network Library (SNL) and hls4ml for Implementing Machine Learning in Collider Trigger Systems
This research paper compares two high-level synthesis frameworks, SNL and hls4ml, for implementing machine learning algorithms on FPGAs for real-time anomaly detection in collider trigger systems, finding that while hls4ml excels in latency optimization, SNL offers greater resource efficiency, particularly for larger networks.
숨은 교란 요인이 있는 선형 순환 시스템에 대한 강력한 인과 분석
숨겨진 교란 요인과 순환 관계가 있는 선형 시스템에서 인과 관계를 추론하도록 설계된 LLC 알고리즘은 데이터 오염에 취약하며, 이러한 문제를 해결하기 위해 MCD, GDE와 같은 강력한 공분산 추정기를 사용한 확장이 필요하다.
隠れ交絡因子を持つ線形循環システムのロバストな因果分析：ロバスト性評価と頑健な拡張
隠れ交絡因子と循環を持つ線形因果モデルを学習するアルゴリズム「LLC」は、データの汚染に対して脆弱であり、そのロバスト性を向上させるためには、共分散行列の推定に頑健な手法を適用する必要がある。
Robustness Analysis and Improvement of the LLC Algorithm for Causal Discovery in Linear Cyclic Systems with Hidden Confounders
The LLC algorithm, designed for learning causal relationships in linear cyclic systems with hidden confounders, is inherently non-robust to data contamination, but its robustness can be improved by incorporating robust covariance estimators like MCD and GDE.
Distributed Parallel Adaptive Lasso Method for Large-Scale Latent Network Reconstruction Using Multi-directional Signals
The authors propose a distributed parallel adaptive lasso method called PALMS to reconstruct large-scale latent networks by leveraging multi-directional signals from nodal dynamics, significantly improving computational efficiency while maintaining estimation accuracy.
HistoEncoder：一種用於前列腺癌的數位病理學基礎模型
HistoEncoder 是一種針對前列腺癌組織病理學圖像進行預先訓練的基礎模型，它在癌症檢測和預後預測方面展現出優於傳統方法的潛力，並突顯了領域特定數據集在開發高效能模型中的重要性。
전립선암 디지털 병리학을 위한 기반 모델 HistoEncoder 개발 및 성능 평가
HistoEncoder는 방대한 양의 전립선 조직 이미지 데이터로 훈련된 기반 모델로, 최소한의 데이터와 계산 리소스만으로도 전립선암 진단 및 예후 예측에 높은 성능을 보여주는 동시에 기존 방법 대비 월등한 데이터 효율성을 제공합니다.
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