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Found 7 papers, 0 papers with code
Strategizing against No-Regret Learners in First-Price Auctions
On the other hand, Mansour et al. (2022) showed that a more sophisticated class of algorithms called no-polytope-swap-regret algorithms are sufficient to cap the optimizer's utility at the Stackelberg utility in any repeated Bayesian game (including Bayesian first-price auctions), and they pose the open question whether no-polytope-swap-regret algorithms are necessary to cap the optimizer's utility.
Fast swap regret minimization and applications to approximate correlated equilibria
We give a simple and computationally efficient algorithm that, for any constant $\varepsilon>0$, obtains $\varepsilon T$-swap regret within only $T = \mathsf{polylog}(n)$ rounds; this is an exponential improvement compared to the super-linear number of rounds required by the state-of-the-art algorithm, and resolves the main open problem of [Blum and Mansour 2007].
Near Optimal Memory-Regret Tradeoff for Online Learning
In the experts problem, on each of $T$ days, an agent needs to follow the advice of one of $n$ ``experts''.
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics
We consider the fundamental problem of selecting $k$ out of $n$ random variables in a way that the expected highest or second-highest value is maximized.
The Power of Optimization from Samples
In this paper we show that for any monotone submodular function with curvature c there is a (1 - c)/(1 + c - c^2) approximation algorithm for maximization under cardinality constraints when polynomially-many samples are drawn from the uniform distribution over feasible sets.
The Limitations of Optimization from Samples
In particular, our main result shows that there is no constant factor approximation for maximizing coverage functions under a cardinality constraint using polynomially-many samples drawn from any distribution.
On the Worst-Case Approximability of Sparse PCA
It is well known that Sparse PCA (Sparse Principal Component Analysis) is NP-hard to solve exactly on worst-case instances.
