Zobrazeno 1 - 10
of 1 147
pro vyhledávání: '"Cesa Bianchi A"'
Autor:
Bressan, Marco, Brukhim, Nataly, Cesa-Bianchi, Nicolò, Esposito, Emmanuel, Mansour, Yishay, Moran, Shay, Thiessen, Maximilian
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 false negative prediction can lead to worse consequences than a false po
Externí odkaz:
http://arxiv.org/abs/2412.08012
Motivated by practical federated learning settings where clients may not be always available, we investigate a variant of distributed online optimization where agents are active with a known probability $p$ at each time step, and communication betwee
Externí odkaz:
http://arxiv.org/abs/2411.16477
We consider a sequential decision-making setting where, at every round $t$, a market maker posts a bid price $B_t$ and an ask price $A_t$ to an incoming trader (the taker) with a private valuation for one unit of some asset. If the trader's valuation
Externí odkaz:
http://arxiv.org/abs/2411.13993
In this research note, we revisit the bandits with expert advice problem. Under a restricted feedback model, we prove a lower bound of order $\sqrt{K T \ln(N/K)}$ for the worst-case regret, where $K$ is the number of actions, $N>K$ the number of expe
Externí odkaz:
http://arxiv.org/abs/2406.16802
Autor:
Bressan, Marco, Cesa-Bianchi, Nicolò, Esposito, Emmanuel, Mansour, Yishay, Moran, Shay, Thiessen, Maximilian
Can a deep neural network be approximated by a small decision tree based on simple features? This question and its variants are behind the growing demand for machine learning models that are *interpretable* by humans. In this work we study such quest
Externí odkaz:
http://arxiv.org/abs/2406.10529
We study stochastic linear bandits where, in each round, the learner receives a set of actions (i.e., feature vectors), from which it chooses an element and obtains a stochastic reward. The expected reward is a fixed but unknown linear function of th
Externí odkaz:
http://arxiv.org/abs/2406.01192
In online bilateral trade, a platform posts prices to incoming pairs of buyers and sellers that have private valuations for a certain good. If the price is lower than the buyers' valuation and higher than the sellers' valuation, then a trade takes pl
Externí odkaz:
http://arxiv.org/abs/2405.13919
This work addresses the mediator feedback problem, a bandit game where the decision set consists of a number of policies, each associated with a probability distribution over a common space of outcomes. Upon choosing a policy, the learner observes an
Externí odkaz:
http://arxiv.org/abs/2402.10282
We study best-of-both-worlds algorithms for $K$-armed linear contextual bandits. Our algorithms deliver near-optimal regret bounds in both the adversarial and stochastic regimes, without prior knowledge about the environment. In the stochastic regime
Externí odkaz:
http://arxiv.org/abs/2312.15433
Many online decision-making problems correspond to maximizing a sequence of submodular functions. In this work, we introduce sum-max functions, a subclass of monotone submodular functions capturing several interesting problems, including best-of-$K$-
Externí odkaz:
http://arxiv.org/abs/2311.05975