You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

no code implementations • 6 May 2022 • Miroslav Dudík, Ziwei Ji, Robert E. Schapire, Matus Telgarsky

Not all convex functions on $\mathbb{R}^n$ have finite minimizers; some can only be minimized by a sequence as it heads to infinity.

no code implementations • NeurIPS 2021 • Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire

We prove that the expected reward accrued by Thompson sampling (TS) with a misspecified prior differs by at most $\tilde{\mathcal{O}}(H^2 \epsilon)$ from TS with a well specified prior, where $\epsilon$ is the total-variation distance between priors and $H$ is the learning horizon.

no code implementations • 19 Jun 2020 • Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky

Recent work across many machine learning disciplines has highlighted that standard descent methods, even without explicit regularization, do not merely minimize the training error, but also exhibit an implicit bias.

no code implementations • ICML 2020 • Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík

We propose a new framework for designing estimators for off-policy evaluation in contextual bandits.

3 code implementations • 30 May 2019 • Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu

Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions.

1 code implementation • 25 Jan 2019 • Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford

We study the exploration problem in episodic MDPs with rich observations generated from a small number of latent states.

2 code implementations • ICML 2018 • Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna Wallach

We present a systematic approach for achieving fairness in a binary classification setting.

no code implementations • ICML 2018 • Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire

A major challenge in contextual bandits is to design general-purpose algorithms that are both practically useful and theoretically well-founded.

no code implementations • ICML 2018 • Hoang M. Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III

We study how to effectively leverage expert feedback to learn sequential decision-making policies.

no code implementations • 5 Nov 2016 • Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan

We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle.

no code implementations • 9 Jun 2016 • Christian Kroer, Miroslav Dudík, Sébastien Lahaie, Sivaraman Balakrishnan

We present a new combinatorial market maker that operates arbitrage-free combinatorial prediction markets specified by integer programs.

1 code implementation • NeurIPS 2017 • Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni

This paper studies the evaluation of policies that recommend an ordered set of items (e. g., a ranking) based on some context---a common scenario in web search, ads, and recommendation.

no code implementations • 7 Oct 2015 • Nikhil Devanur, Miroslav Dudík, Zhiyi Huang, David M. Pennock

We give a detailed characterization of optimal trades under budget constraints in a prediction market with a cost-function-based automated market maker.

no code implementations • 15 Jun 2015 • Matus Telgarsky, Miroslav Dudík, Robert Schapire

This paper proves, in very general settings, that convex risk minimization is a procedure to select a unique conditional probability model determined by the classification problem.

no code implementations • 10 Mar 2015 • Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li

As such, we expect the doubly robust approach to become common practice in policy evaluation and optimization.

no code implementations • 23 Feb 2015 • Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi

The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space.

no code implementations • 30 Jul 2014 • Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan

We study information elicitation in cost-function-based combinatorial prediction markets when the market maker's utility for information decreases over time.

no code implementations • NeurIPS 2008 • Steven J. Phillips, Miroslav Dudík

For the generative case, we derive an entropy-based weighting that maximizes expected log likelihood under the worst-case true class proportions.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.