no code implementations • 13 Feb 2024 • Aviad Rubinstein, Junyao Zhao
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.
no code implementations • 30 Oct 2023 • Binghui Peng, Aviad Rubinstein
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].
no code implementations • 3 Mar 2023 • Binghui Peng, Aviad Rubinstein
In the experts problem, on each of $T$ days, an agent needs to follow the advice of one of $n$ ``experts''.
no code implementations • NeurIPS 2020 • Aranyak Mehta, Uri Nadav, Alexandros Psomas, Aviad Rubinstein
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.
no code implementations • NeurIPS 2016 • Eric Balkanski, Aviad Rubinstein, Yaron Singer
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.
no code implementations • 19 Dec 2015 • Eric Balkanski, Aviad Rubinstein, Yaron Singer
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.
no code implementations • 21 Jul 2015 • Siu On Chan, Dimitris Papailiopoulos, Aviad Rubinstein
It is well known that Sparse PCA (Sparse Principal Component Analysis) is NP-hard to solve exactly on worst-case instances.