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no code implementations • NeurIPS 2021 • Guru Guruganesh, Allen Liu, Jon Schneider, Joshua Wang

We consider the problem of multi-class classification, where a stream of adversarially chosen queries arrive and must be assigned a label online.

no code implementations • NeurIPS 2021 • Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, Renato Paes Leme, Jon Schneider

We design algorithms for this problem which achieve regret $O(d\log T)$ and $\exp(O(d \log d))$.

no code implementations • 2 Mar 2021 • Mark Braverman, Jon Schneider, S. Matthew Weinberg

We show that under these constraints, the auctioneer can attain a constant fraction of the "sell the business" benchmark, but no more than $2/e$ of this benchmark.

Computer Science and Game Theory Theoretical Economics

no code implementations • NeurIPS 2020 • Allen Liu, Renato Leme, Jon Schneider

Motivated by pricing applications in online advertising, we study a variant of linear regression with a discontinuous loss function that we term Myersonian regression.

no code implementations • 10 Sep 2020 • Negin Golrezaei, Vahideh Manshadi, Jon Schneider, Shreyas Sekar

We first show that existing learning algorithms---that are optimal in the absence of fake users---may converge to highly sub-optimal rankings under manipulation by fake users.

no code implementations • 11 Jun 2020 • Zhe Feng, Sébastien Lahaie, Jon Schneider, Jinchao Ye

The display advertising industry has recently transitioned from second- to first-price auctions as its primary mechanism for ad allocation and pricing.

no code implementations • 3 Mar 2020 • Allen Liu, Renato Paes Leme, Jon Schneider

We provide a generic algorithm with $O(d^2)$ regret where $d$ is the covering dimension of this class.

no code implementations • NeurIPS 2019 • Yuan Deng, Jon Schneider, Balasubramanian Sivan

We show that even in this prior-free setting, it is possible to extract a $(1-\varepsilon)$-approximation of the full economic surplus for any $\varepsilon > 0$.

no code implementations • NeurIPS 2019 • Yuan Deng, Jon Schneider, Balusubramanian Sivan

How should a player who repeatedly plays a game against a no-regret learner strategize to maximize his utility?

no code implementations • NeurIPS 2018 • Jieming Mao, Renato Leme, Jon Schneider

For the symmetric loss $\ell(f(x_t), y_t) = \vert f(x_t) - y_t \vert$, we provide an algorithm for this problem achieving total loss $O(\log T)$ when $d=1$ and $O(T^{(d-1)/d})$ when $d>1$, and show that both bounds are tight (up to a factor of $\sqrt{\log T}$).

no code implementations • NeurIPS 2019 • Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider

We consider the variant of this problem where in addition to receiving the reward $r_{i, t}(c)$, the learner also learns the values of $r_{i, t}(c')$ for some other contexts $c'$ in set $\mathcal{O}_i(c)$; i. e., the rewards that would have been achieved by performing that action under different contexts $c'\in \mathcal{O}_i(c)$.

no code implementations • 9 Apr 2018 • Renato Paes Leme, Jon Schneider

We present an algorithm for the contextual search problem for the symmetric loss function $\ell(\theta, p) = |\theta - p|$ that achieves $O_{d}(1)$ total loss.

no code implementations • 25 Nov 2017 • Mark Braverman, Jieming Mao, Jon Schneider, S. Matthew Weinberg

- There exists a learning algorithm $\mathcal{A}$ such that if the buyer bids according to $\mathcal{A}$ then the optimal strategy for the seller is simply to post the Myerson reserve for $D$ every round.

no code implementations • 27 Jun 2017 • Mark Braverman, Jieming Mao, Jon Schneider, S. Matthew Weinberg

We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round.

no code implementations • 12 May 2016 • Xi Chen, Sivakanth Gopi, Jieming Mao, Jon Schneider

In particular, we present a linear time algorithm for the top-$K$ problem which has a competitive ratio of $\tilde{O}(\sqrt{n})$; i. e. to solve any instance of top-$K$, our algorithm needs at most $\tilde{O}(\sqrt{n})$ times as many samples needed as the best possible algorithm for that instance (in contrast, all previous known algorithms for the top-$K$ problem have competitive ratios of $\tilde{\Omega}(n)$ or worse).

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