empirical risk minimization
PulseAugur coverage of empirical risk minimization — every cluster mentioning empirical risk minimization across labs, papers, and developer communities, ranked by signal.
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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…