Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution

arXiv — cs.LG•Thursday, October 30, 2025 at 4:00:00 AM

A new framework called rank-conditioned committee (RCC) has been introduced to enhance machine learning-assisted directed evolution (MLDE) in navigating antibody fitness landscapes. This innovative approach addresses limitations of existing MLDE pipelines by effectively separating conformational uncertainty from epistemic uncertainty. By leveraging ranked conformations, the RCC framework promises to improve the efficiency and accuracy of antibody development, which is crucial for advancements in therapeutic applications.

— via World Pulse Now AI Editorial System

Read Original

Was this article worth reading? Share it

Continue Readings

arXiv — cs.CV20 hours ago

Rank Matters: Understanding and Defending Model Inversion Attacks via Low-Rank Feature Filtering

PositiveArtificial Intelligence

Recent research has highlighted the vulnerabilities of machine learning models to Model Inversion Attacks (MIAs), which can reconstruct sensitive training data. A new study proposes a defense mechanism utilizing low-rank feature filtering to mitigate privacy risks by reducing the attack surface of these models. The findings suggest that higher-rank features are more susceptible to privacy leakage, prompting the need for effective countermeasures.

Read full article

via arXiv — cs.CV

arXiv — cs.LG2 days ago

Open-Set Domain Adaptation Under Background Distribution Shift: Challenges and A Provably Efficient Solution

PositiveArtificial Intelligence

A new method called ours{} has been developed to address the challenges of open-set recognition in machine learning, particularly in scenarios where the background distribution of known classes shifts. This method is designed to maintain model performance even as new classes emerge or existing class distributions change, providing theoretical guarantees of its effectiveness in a simplified overparameterized setting.

Read full article

via arXiv — cs.LG

arXiv — cs.LG2 days ago

Escaping Collapse: The Strength of Weak Data for Large Language Model Training

PositiveArtificial Intelligence

Recent research has formalized the role of synthetically-generated data in training large language models (LLMs), highlighting the risks of performance plateauing or collapsing without adequate curation. The study proposes a theoretical framework to determine the necessary level of data curation to ensure continuous improvement in LLM performance, drawing inspiration from the boosting technique in machine learning.

Read full article

via arXiv — cs.LG

arXiv — stat.ML2 days ago

Provably Safe Model Updates

PositiveArtificial Intelligence

A new framework for provably safe model updates has been introduced, addressing the challenges posed by dynamic environments in machine learning. This framework formalizes the computation of the largest locally invariant domain (LID), ensuring that updated models meet performance specifications despite distribution shifts and evolving requirements.

Read full article

via arXiv — stat.ML

$Overfitting has a limitation: a model-independent generalization gap bound based on R\'enyi entropy$

arXiv — stat.ML2 days ago

Overfitting has a limitation: a model-independent generalization gap bound based on R\'enyi entropy

NeutralArtificial Intelligence

A recent study has introduced a model-independent upper bound for the generalization gap in machine learning, focusing on the role of R'enyi entropy. This research addresses the limitations of traditional analyses that link error bounds to model complexity, particularly as machine learning models scale up. The findings suggest that a small generalization gap can be maintained even with large architectures, which is crucial for the future of machine learning applications.

Read full article

via arXiv — stat.ML