arXiv:2507. 08922v3 Announce Type: replace-cross Abstract: Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps.
By Tameem Adel
arXiv:2511. 04666v4 Announce Type: replace Abstract: A fundamental challenge in developing general learning algorithms is their tendency to forget past knowledge as they adapt to new data.
By Ben Sanati, Thomas L. Lee, Trevor McInroe, Aidan Scannell, Esmeralda S. Whitammer, David Abel, Amos Storkey
arXiv:2512. 21315v2 Announce Type: replace Abstract: The data processing inequality is an information-theoretic principle stating that the information content of a signal cannot be increased by processing the observations.
By Roy Turgeman, Tom Tirer
arXiv:2603. 14894v3 Announce Type: replace-cross Abstract: Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations.
By Sumedha Chugh, Ranjitha Prasad, Nazreen Shah
arXiv:2607. 22961v1 Announce Type: new Abstract: Verbalized Machine Learning (VML) parameterizes a model as a natural-language prompt that an LLM evaluates as f(x; theta).
By Yan Zhang, Shikan Lian, Shibo Li
arXiv:2604. 04535v2 Announce Type: replace Abstract: Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction, and periodic model updates.
By Mark Braverman, Roi Livni, Yishay Mansour, Shay Moran, Kobbi Nissim