A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
John N. Wright, A. Yang, Arvind Ganesh, et al.
This paper casts face recognition as classification among linear regression models and uses sparse representation via l1-minimization to achieve robustness to occlusion and disguise.
Unknown
AutoDEUQ automates deep ensemble construction with uncertainty quantification, evaluated on 10 regression benchmark datasets.
Unknown
RidgeloRA enhances LoRA by incorporating matrix ridge regression to improve parameter-efficient fine-tuning of large language models.
Daniel Lüdecke, Mattan S. Ben‐Shachar, Indrajeet Patil, et al.
The performance R package provides a unified suite of functions with consistent syntax for computing, comparing, and visualizing regression model fit statistics across many model types.
Jiaming Zhong, Reza Valiollahi Mehrizi, Mohammad Pirani, et al.
LAMPC combines agent-based MPC with Gaussian process regression to predict unknown agent contributions, improving vehicle tracking and guaranteeing constraint satisfaction.
Kejia Zhang, Youran Sun, Xinyu Ren, et al.
AutoSR automates symbolic regression by searching persistent research states, preserving scientific evidence to recover algebraically equivalent relations across nine benchmark challenges.
Amgad Muneer, Suliman Mohamed Fati
This paper compares seven machine learning classifiers for detecting cyberbullying on Twitter using a global dataset of 37,373 tweets, finding Logistic Regression achieves the best accuracy and F1 score.
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This paper studies model-based reinforcement learning for regret minimization in finite-hizon episodic settings with a known transition model family, proposing a value-targeted regression approach.
Ilya Kostrikov, Ashvin Nair, S. Levine
Introduces Implicit Q-Learning (IQL), an offline RL algorithm that avoids evaluating actions outside the dataset by using expectile regression and does not require unseen actions.
Unknown
This paper presents new efficient protocols for privacy-preserving machine learning for linear regression, logistic regression, and neural network training using stochastic gradient descent.
Karel G.M. Moons, Johanna AAG Damen, T. K. Kaul, et al.
PROBAST+AI is an updated tool for assessing quality, risk of bias, and applicability of prediction models using regression or AI, with separate signaling questions for model development and evaluation.
John Hancock, Taghi M. Khoshgoftaar
This paper provides the first interdisciplinary survey of CatBoost for big data, covering both its strengths and weaknesses in classification and regression tasks.