Search Results for author: Jon Schneider

Found 15 papers, 0 papers with code

Margin-Independent Online Multiclass Learning via Convex Geometry

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.

Multi-class Classification

Prior-free Dynamic Mechanism Design With Limited Liability

no code implementations2 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

Myersonian Regression

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.

Learning Product Rankings Robust to Fake Users

no code implementations10 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.

Reserve Price Optimization for First Price Auctions

no code implementations11 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.

Optimal Contextual Pricing and Extensions

no code implementations3 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.

Prior-Free Dynamic Auctions with Low Regret Buyers

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 $\varepsilon > 0$.

Strategizing against No-regret Learners

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?

Contextual Pricing for Lipschitz Buyers

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 $\sqrt{\log T}$).

Contextual Bandits with Cross-learning

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 $c'\in \mathcal{O}_i(c)$.

Multi-Armed Bandits

Contextual Search via Intrinsic Volumes

no code implementations9 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.

Decision Making

Selling to a No-Regret Buyer

no code implementations25 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.

Multi-armed Bandit Problems with Strategic Arms

no code implementations27 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.

Competitive analysis of the top-K ranking problem

no code implementations12 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).

Recommendation Systems

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