11 papers
Active Learning on Adversarially Corrupted Graphs
Marco Bressan, Nicolò Cesa-Bianchi, Tommaso d`Orsi +2
Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of \emph{corrupted vertices} insi…
Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach
Hao Qiu, Mengxiao Zhang, Nicolò Cesa-Bianchi
We study distributed adversarial bandits, where agents cooperate to minimize the global average loss while observing only their own local losses. We show that the minimax regre…
Learning Conditional Averages
Marco Bressan, Nataly Brukhim, Nicolo Cesa-Bianchi +4
We introduce the problem of learning conditional averages in the PAC framework. The learner receives a sample labeled by an unknown target concept from a known concept class, as in…
Online Budget Allocation with Censored Semi-Bandit Feedback
François Bachoc, Nicolò Cesa-Bianchi, Tommaso Cesari +1
We study a stochastic budget-allocation problem over tasks. At each round , the learner chooses an allocation . Task succeeds with probability $F_k(X_{t,k}…
Market Making without Regret
Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni +2
We consider a sequential decision-making setting where, at every round , a market maker posts a bid price and an ask price to an incoming trader (the taker) with a p…
Of Dice and Games: A Theory of Generalized Boosting
Marco Bressan, Nataly Brukhim, Nicolò Cesa-Bianchi +4
Cost-sensitive loss functions are crucial in many real-world prediction problems, where different types of errors are penalized differently; for example, in medical diagnosis, a fa…