empirical risk minimization
PulseAugur coverage of empirical risk minimization — every cluster mentioning empirical risk minimization across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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New Differentially Private Algorithm for Weighted Empirical Risk Minimization Developed
Researchers have developed a new differentially private algorithm for weighted empirical risk minimization (wERM), a generalization of standard ERM that accounts for varying individual contributions to the objective fun…
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New research tackles domain generalization challenges in Human Activity Recognition
A new research paper explores the challenges of domain generalization in Human Activity Recognition (HAR) due to distribution shifts. The study systematically evaluates four types of shifts—device type, sensor placement…
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New privacy framework 'predictability' complements differential privacy
Researchers have introduced a new privacy framework called "privacy via predictability" that offers a more fine-grained approach than traditional differential privacy (DP). This new method accounts for an attacker's spe…
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New Research Tackles Privacy-Preserving Ad Conversion Prediction
A new research paper on arXiv introduces a method for statistical learning from attribution sets, addressing privacy constraints in advertising domains where direct links between ad clicks and conversions are unavailabl…
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Research paper unifies CoCoA and ADMM optimization algorithms
A new research paper explores the relationship between two families of distributed optimization algorithms, CoCoA and ADMM. By unifying them through a primal-dual perspective, the study reveals that certain ADMM variant…
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New Research Unveils Fundamental Limits of k-Fold Cross-Validation
A new research paper explores the theoretical limitations of k-fold cross-validation, a widely used technique for estimating the performance of machine learning models. The study, focusing on the majority algorithm in b…
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New bound links generalization gap to data entropy
Researchers have developed a new method to bound the generalization gap in machine learning models, which is a key factor in understanding overfitting. This novel approach establishes a model-independent upper bound for…
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New paper proposes multi-axis fairness for toxicity detection models
A new paper introduces a framework for evaluating fairness in toxicity detection models, considering ranking, calibration, and abstention. The research found that standard training methods like Empirical Risk Minimizati…
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New framework improves U-statistics with active inference for costly labels
Researchers have developed a new active inference framework for U-statistics, aiming to improve estimation efficiency when labeling data is expensive. This approach selectively queries informative labels within a fixed …
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New Quadratic Objective Perturbation method enhances differential privacy for ML
Researchers have introduced Quadratic Objective Perturbation (QOP) as a novel method for differential privacy in machine learning. Unlike Linear Objective Perturbation (LOP), which requires bounded gradients, QOP uses a…
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AI researchers develop PAC-learning algorithm for consensus elicitation
Researchers have developed a new theoretical framework called Probably Approximately Consensus to identify broadly agreeable ideas on online platforms. This approach models consensus as an interval within a one-dimensio…