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Omnipredictors for constrained optimization
The notion of omnipredictors (Gopalan, Kalai, Reingold, Sharan and Wieder ITCS 2022),
suggested a new paradigm for loss minimization. Rather than learning a predictor based on …
suggested a new paradigm for loss minimization. Rather than learning a predictor based on …
Classical verification of quantum learning
Quantum data access and quantum processing can make certain classically intractable
learning tasks feasible. However, quantum capabilities will only be available to a select few …
learning tasks feasible. However, quantum capabilities will only be available to a select few …
[PDF][PDF] Refining the Sample Complexity of Comparative Learning
S Ashkezari, R Urner - Proceedings of Machine …, 2025 - raw.githubusercontent.com
Comparative learning, a recently introduced variation of the PAC (Probably Approximately
Correct) framework, interpolates between the two standard extreme settings of realizable …
Correct) framework, interpolates between the two standard extreme settings of realizable …
Generative models of huge objects
This work initiates the systematic study of explicit distributions that are indistinguishable from
a single exponential-size combinatorial object. In this we extend the work of Goldreich …
a single exponential-size combinatorial object. In this we extend the work of Goldreich …
[KIRJA][B] Trustworthiness: Revisiting the Foundations of Machine Learning
L Hu - 2024 - search.proquest.com
As we deploy machine learning models in more and more complex and critical tasks, can we
trust that these models will work as intended in real-world settings? For example, a doctor …
trust that these models will work as intended in real-world settings? For example, a doctor …