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25 06 2025 E T

Full exam for Online Learning Applications in the Computer Engineering degree programme at Politecnico di Milano. The document covers: Online Learning Applications Exam 25-06-2025 Exam of Online Learning Applications 25-06-2025 Name: Surname: Student ID: Signature: Instructions: • The total number of points is 16. • The duration of the exam is: 2h00min. • During this exam you are not allowed to use books,

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Full exam for Online Learning Applications in the Computer Engineering degree programme at Politecnico di Milano. The document covers: Online Learning Applications Exam 25-06-2025 Exam of Online Learning Applications 25-06-2025 Name: Surname: Student ID: Signature: Instructions: • The total number of points is 16. • The duration of the exam is: 2h00min. • During this exam you are not allowed to use books,

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Online Learning Applications Exam 25-06-2025 Exam of Online Learning Applications 25-06-2025 Name: Surname: Student ID: Signature: Instructions: • The total number of points is 16. • The duration of the exam is: 2h00min. • During this exam you are not allowed to use books, notes, and electronic devices. • You are allowed to write the exam either with a pen or a pencil. • You are allowed to withdraw from the exam at any time. • Before you exit the room, you must hand in your exam. Online Learning Applications Exam 25-06-2025 1. (4 points) Answer the following questions about online convex optimization: • Define the online convex optimization framework • Describe the online gradient descent algorithm • Show how gradient descent can be applied to the expert problem Online Learning Applications Exam 25-06-2025 2. (4 points) Answer the following questions about truthful auctions: • Describe some possible pacing strategies with their advantages and drawbacks • Describe the differences between truthful and not-truthful auctions • Describe an algorithm for online bidding in second-price auctions Online Learning Applications Exam 25-06-2025 3. (4 points) Answer the following questions about contextual bandits: • Describe the contextual bandit model • Define the pseudo-regret for contextual bandits • Describe an algorithm for contextual bandits with a small number of contexts • Explain why the previous algorithm performs poorly when there are many contexts Online Learning Applications Exam 25-06-2025 4. (4 points) Answer the following questions about dynamic pricing: • Describe the limits of algorithms for online pricing which use discretization • Describe how gaussian processes can be exploited to remove these limitations • Suppose that, at each round t ∈ [T ], you can observe…

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