no code implementations • ICML 2020 • Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh

We provide communication-efficient ensemble algorithms for federated learning, where per-round communication cost is independent of the size of the ensemble.

no code implementations • ICML 2020 • Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang

A general framework for online learning with partial information is one where feedback graphs specify which losses can be observed by the learner.

no code implementations • 18 Jul 2024 • Anqi Mao, Mehryar Mohri, Yutao Zhong

Recent research has introduced a key notion of $H$-consistency bounds for surrogate losses.

no code implementations • 18 Jul 2024 • Anqi Mao, Mehryar Mohri, Yutao Zhong

We present a comprehensive study of surrogate loss functions for learning to defer.

no code implementations • 18 Jul 2024 • Anqi Mao, Mehryar Mohri, Yutao Zhong

We also extend our multi-label logistic losses to more comprehensive multi-label comp-sum losses, adapting comp-sum losses from standard classification to the multi-label learning.

no code implementations • 9 Jul 2024 • Corinna Cortes, Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong

We introduce a new target loss function tailored to this setting that accounts for both the classification error and the cardinality of the set predicted.

no code implementations • 10 Jun 2024 • Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan

Abernethy et al. (2011) showed that Blackwell approachability and no-regret learning are equivalent, in the sense that any algorithm that solves a specific Blackwell approachability instance can be converted to a sublinear regret algorithm for a specific no-regret learning instance, and vice versa.

no code implementations • 9 May 2024 • Anqi Mao, Mehryar Mohri, Yutao Zhong

This paper presents a comprehensive analysis of the growth rate of $H$-consistency bounds (and excess error bounds) for various surrogate losses used in classification.

no code implementations • 28 Mar 2024 • Anqi Mao, Mehryar Mohri, Yutao Zhong

In this work, we introduce a novel framework of regression with deferral, which involves deferring the prediction to multiple experts.

no code implementations • 28 Mar 2024 • Anqi Mao, Mehryar Mohri, Yutao Zhong

For these functions, we derive cost-sensitive comp-sum and constrained surrogate losses, establishing their $H$-consistency bounds and Bayes-consistency.

no code implementations • 28 Mar 2024 • Anqi Mao, Mehryar Mohri, Yutao Zhong

Next, we prove a series of novel $H$-consistency bounds for surrogate loss functions of the squared loss, under the assumption of a symmetric distribution and a bounded hypothesis set.

no code implementations • 23 Oct 2023 • Anqi Mao, Mehryar Mohri, Yutao Zhong

We present a study of surrogate losses and algorithms for the general problem of learning to defer with multiple experts.

no code implementations • 23 Oct 2023 • Anqi Mao, Mehryar Mohri, Yutao Zhong

We introduce new families of surrogate losses for the abstention loss function, which include the state-of-the-art surrogate losses in the single-stage setting and a novel family of loss functions in the two-stage setting.

no code implementations • 23 Oct 2023 • Anqi Mao, Mehryar Mohri, Yutao Zhong

We study the key framework of learning with abstention in the multi-class classification setting.

no code implementations • 5 Jul 2023 • Anqi Mao, Mehryar Mohri, Yutao Zhong

We introduce a novel framework of ranking with abstention, where the learner can abstain from making prediction at some limited cost $c$.

no code implementations • 15 Jun 2023 • Raef Bassily, Corinna Cortes, Anqi Mao, Mehryar Mohri

This is the modern problem of supervised domain adaptation from a public source to a private target domain.

no code implementations • 10 May 2023 • Pranjal Awasthi, Corinna Cortes, Mehryar Mohri

We show how these bounds can guide the design of learning algorithms that we discuss in detail.

no code implementations • 14 Apr 2023 • Anqi Mao, Mehryar Mohri, Yutao Zhong

These are non-asymptotic guarantees that upper bound the zero-one loss estimation error in terms of the estimation error of a surrogate loss, for the specific hypothesis set $H$ used.

no code implementations • 3 Feb 2023 • Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan

We then use that to show, modulo mild normalization assumptions, that there exists an $\ell_\infty$-approachability algorithm whose convergence is independent of the dimension of the original vectorial payoff.

no code implementations • NeurIPS 2021 • Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert

Thompson Sampling is one of the most effective methods for contextual bandits and has been generalized to posterior sampling for certain MDP settings.

no code implementations • 12 Aug 2022 • Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh

A key problem in a variety of applications is that of domain adaptation from a public source domain, for which a relatively large amount of labeled data with no privacy constraints is at one's disposal, to a private target domain, for which a private sample is available with very few or no labeled data.

no code implementations • 20 Jun 2022 • Teodor V. Marinov, Mehryar Mohri, Julian Zimmert

We revisit the problem of stochastic online learning with feedback graphs, with the goal of devising algorithms that are optimal, up to constants, both asymptotically and in finite time.

no code implementations • 19 Jun 2022 • Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

This paper presents a theoretical analysis of such policies and provides the first regret and sample-complexity bounds for reinforcement learning with myopic exploration.

no code implementations • 17 May 2022 • Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan

We study repeated two-player games where one of the players, the learner, employs a no-regret learning strategy, while the other, the optimizer, is a rational utility maximizer.

no code implementations • 16 May 2022 • Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong

We also show that previous excess error bounds can be recovered as special cases of our general results.

no code implementations • 21 Apr 2022 • Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh

For the family of linear hypotheses, we give a pure DP learning algorithm that benefits from relative deviation margin guarantees, as well as an efficient DP learning algorithm with margin guarantees.

no code implementations • 3 Dec 2021 • Pranjal Awasthi, Natalie S. Frank, Mehryar Mohri

Our results can provide a useful tool for a subsequent study of surrogate losses in adversarial robustness and their consistency properties.

no code implementations • NeurIPS 2021 • Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian U. Stich, Ananda Theertha Suresh

Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of the data across different clients which gives rise to the client drift phenomenon.

no code implementations • NeurIPS 2021 • Pranjal Awasthi, Natalie Frank, Mehryar Mohri

Adversarial robustness is a critical property in a variety of modern machine learning applications.

no code implementations • NeurIPS 2021 • Corinna Cortes, Mehryar Mohri, Dmitry Storcheus, Ananda Theertha Suresh

We study the problem of learning accurate ensemble predictors, in particular boosting, in the presence of multiple source domains.

2 code implementations • 14 Jul 2021 • Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu

Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.

no code implementations • NeurIPS 2020 • Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert

Given access to an online oracle for square loss regression, our algorithm attains optimal regret and -- in particular -- optimal dependence on the misspecification level, with no prior knowledge.

no code implementations • NeurIPS 2021 • Christoph Dann, Teodor V. Marinov, Mehryar Mohri, Julian Zimmert

Our results show that optimistic algorithms can not achieve the information-theoretic lower bounds even in deterministic MDPs unless there is a unique optimal policy.

no code implementations • NeurIPS 2021 • Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

In this work, we consider the more realistic setting of agnostic RL with rich observation spaces and a fixed class of policies $\Pi$ that may not contain any near-optimal policy.

no code implementations • 4 May 2021 • Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong

Moreover, our calibration results, combined with the previous study of consistency by Awasthi et al. (2021), also lead to more general $H$-consistency results covering common hypothesis sets.

no code implementations • NeurIPS 2021 • Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong

We then give a characterization of H-calibration and prove that some surrogate losses are indeed H-calibrated for the adversarial loss, with these hypothesis sets.

no code implementations • 6 Apr 2021 • Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh

We propose a communication-efficient distributed algorithm called Agnostic Federated Averaging (or AgnosticFedAvg) to minimize the domain-agnostic objective proposed in Mohri et al. (2019), which is amenable to other private mechanisms such as secure aggregation.

no code implementations • NeurIPS 2021 • Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh

We show that for high-dimensional mean estimation, empirical risk minimization with smooth losses, stochastic convex optimization, and learning hypothesis classes with finite metric entropy, the privacy cost decreases as $O(1/\sqrt{m})$ as users provide more samples.

no code implementations • NeurIPS 2020 • Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri

We present a series of new PAC-Bayes learning guarantees for randomized algorithms with sample-dependent priors.

no code implementations • NeurIPS 2020 • Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus

We further implement the algorithm in a popular symbolic gradient computation framework and empirically demonstrate on a number of datasets the benefits of $\almo$ framework versus learning with a fixed mixture weights distribution.

no code implementations • 25 Aug 2020 • Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang

We present a new discriminative technique for the multiple-source adaptation, MSA, problem.

no code implementations • 21 Aug 2020 • Pranjal Awasthi, Corinna Cortes, Yishay Mansour, Mehryar Mohri

In the adversarial setting, we design efficient algorithms with competitive ratio guarantees.

1 code implementation • 8 Aug 2020 • Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh

Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of the data across different clients which gives rise to the client drift phenomenon.

no code implementations • 21 Jul 2020 • Pranjal Awasthi, Natalie Frank, Mehryar Mohri

Linear predictors form a rich class of hypotheses used in a variety of learning algorithms.

no code implementations • 19 Jul 2020 • Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh, Ke wu

We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner has at disposal a large amount of labeled data from multiple source domains.

no code implementations • 26 Jun 2020 • Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh

We present a series of new and more favorable margin-based learning guarantees that depend on the empirical margin loss of a predictor.

no code implementations • 16 Jun 2020 • Raman Arora, Teodor V. Marinov, Mehryar Mohri

We study the problem of corralling stochastic bandit algorithms, that is combining multiple bandit algorithms designed for a stochastic environment, with the goal of devising a corralling algorithm that performs almost as well as the best base algorithm.

no code implementations • NeurIPS 2020 • Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

We study episodic reinforcement learning in Markov decision processes when the agent receives additional feedback per step in the form of several transition observations.

no code implementations • ICML 2020 • Pranjal Awasthi, Natalie Frank, Mehryar Mohri

We give upper and lower bounds for the adversarial empirical Rademacher complexity of linear hypotheses with adversarial perturbations measured in $l_r$-norm for an arbitrary $r \geq 1$.

1 code implementation • 25 Feb 2020 • Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh

The standard objective in machine learning is to train a single model for all users.

no code implementations • ICML 2020 • Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang

We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels.

9 code implementations • 10 Dec 2019 • Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.

no code implementations • NeurIPS 2019 • Corinna Cortes, Mehryar Mohri, Dmitry Storcheus

We fill this gap by deriving data-dependent learning guarantees for \GB\ used with \emph{regularization}, expressed in terms of the Rademacher complexities of the constrained families of base predictors.

no code implementations • NeurIPS 2019 • Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang

Generative adversarial networks (GANs) generate data based on minimizing a divergence between two distributions.

7 code implementations • ICML 2020 • Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh

We obtain tight convergence rates for FedAvg and prove that it suffers from `client-drift' when the data is heterogeneous (non-iid), resulting in unstable and slow convergence.

no code implementations • NeurIPS 2019 • Raman Arora, Teodor V. Marinov, Mehryar Mohri

We give a new algorithm whose regret guarantee depends only on the domination number of the graph.

1 code implementation • 30 Apr 2019 • Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes

AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for automatically learning high-quality ensembles with minimal expert intervention.

no code implementations • NeurIPS 2019 • Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan

Our main result is a generalization bound for data-dependent hypothesis sets expressed in terms of a notion of hypothesis set stability and a notion of Rademacher complexity for data-dependent hypothesis sets that we introduce.

6 code implementations • 1 Feb 2019 • Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh

A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.

no code implementations • NeurIPS 2018 • Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Dmitry Storcheus, Scott Yang

In this paper, we design efficient gradient computation algorithms for two broad families of structured prediction loss functions: rational and tropical losses.

no code implementations • NeurIPS 2018 • Raman Arora, Michael Dinitz, Teodor V. Marinov, Mehryar Mohri

We revisit the notion of policy regret and first show that there are online learning settings in which policy regret and external regret are incompatible: any sequence of play that achieves a favorable regret with respect to one definition must do poorly with respect to the other.

no code implementations • NeurIPS 2018 • Judy Hoffman, Mehryar Mohri, Ningshan Zhang

This work includes a number of novel contributions for the multiple-source adaptation problem.

no code implementations • 18 Apr 2018 • Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth

We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction.

no code implementations • 25 Mar 2018 • Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan

Starting with the simple observation that the logistic loss is $1$-mixable, we design a new efficient improper learning algorithm for online logistic regression that circumvents the aforementioned lower bound with a regret bound exhibiting a doubly-exponential improvement in dependence on the predictor norm.

no code implementations • 15 Mar 2018 • Vitaly Kuznetsov, Mehryar Mohri

We present data-dependent learning bounds for the general scenario of non-stationary non-mixing stochastic processes.

no code implementations • NeurIPS 2017 • Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan

We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning.

no code implementations • NeurIPS 2017 • Vitaly Kuznetsov, Mehryar Mohri

In this paper, we introduce and analyze Discriminative State-Space Models for forecasting non-stationary time series.

no code implementations • NeurIPS 2017 • Mehryar Mohri, Scott Yang

A by-product of our study is an algorithm for swap regret, which, under mild assumptions, is more efficient than existing ones, and a substantially more efficient algorithm for time selection swap regret.

no code implementations • 14 Nov 2017 • Judy Hoffman, Mehryar Mohri, Ningshan Zhang

We present a detailed theoretical analysis of the problem of multiple-source adaptation in the general stochastic scenario, extending known results that assume a single target labeling function.

no code implementations • 29 Oct 2017 • Corinna Cortes, Giulia Desalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang

We show that the notion of discrepancy can be used to design very general algorithms and a unified framework for the analysis of multi-armed rested bandit problems with non-stationary rewards.

no code implementations • 29 Apr 2017 • Mehryar Mohri, Scott Yang

We consider a general framework of online learning with expert advice where regret is defined with respect to sequences of experts accepted by a weighted automaton.

no code implementations • ICML 2018 • Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Scott Yang

In the stochastic setting, we first point out a bias problem that limits the straightforward extension of algorithms such as UCB-N to time-varying feedback graphs, as needed in this context.

no code implementations • NeurIPS 2016 • Scott Yang, Mehryar Mohri

We introduce the general and powerful scheme of predicting information re-use in optimization algorithms.

no code implementations • NeurIPS 2016 • Corinna Cortes, Giulia Desalvo, Mehryar Mohri

We present a new boosting algorithm for the key scenario of binary classification with abstention where the algorithm can abstain from predicting the label of a point, at the price of a fixed cost.

no code implementations • 25 Oct 2016 • Borja Balle, Mehryar Mohri

We present new data-dependent generalization guarantees for learning weighted automata expressed in terms of the Rademacher complexity of these families.

2 code implementations • ICML 2017 • Corinna Cortes, Xavi Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang

We present new algorithms for adaptively learning artificial neural networks.

no code implementations • NeurIPS 2016 • Corinna Cortes, Mehryar Mohri, Vitaly Kuznetsov, Scott Yang

We give new data-dependent margin guarantees for structured prediction for a very wide family of loss functions and a general family of hypotheses, with an arbitrary factor graph decomposition.

no code implementations • NeurIPS 2015 • Mehryar Mohri, Andres Munoz

We present a revenue optimization algorithm for posted-price auctions when facing a buyer with random valuations who seeks to optimize his $\gamma$-discounted surplus.

no code implementations • NeurIPS 2015 • Vitaly Kuznetsov, Mehryar Mohri

We present data-dependent learning bounds for the general scenario of non-stationary non-mixing stochastic processes.

no code implementations • 29 Sep 2015 • Mehryar Mohri, Afshin Rostamizadeh, Dmitry Storcheus

The generalization error bound is based on a careful analysis of the empirical Rademacher complexity of the relevant hypothesis set.

no code implementations • 18 Sep 2015 • Mehryar Mohri, Scott Yang

We present a powerful general framework for designing data-dependent optimization algorithms, building upon and unifying recent techniques in adaptive regularization, optimistic gradient predictions, and problem-dependent randomization.

no code implementations • 14 Sep 2015 • Corinna Cortes, Prasoon Goyal, Vitaly Kuznetsov, Mehryar Mohri

This paper presents an algorithm, Voted Kernel Regularization , that provides the flexibility of using potentially very complex kernel functions such as predictors based on much higher-degree polynomial kernels, while benefitting from strong learning guarantees.

no code implementations • 8 Jun 2015 • Mehryar Mohri, Andres Munoz Medina

To our knowledge, this is the first attempt to apply learning algorithms to the problem of reserve price optimization in GSP auctions.

no code implementations • NeurIPS 2014 • Vitaly Kuznetsov, Mehryar Mohri, Umar Syed

We give new data-dependent learning bounds for convex ensembles in the multi-class classification setting expressed in terms of the Rademacher complexities of the sub-families composing the base classifier set, and the mixture weight assigned to each sub-family.

no code implementations • NeurIPS 2014 • Mehryar Mohri, Andres Munoz

We study revenue optimization learning algorithms for posted-price auctions with strategic buyers.

no code implementations • NeurIPS 2014 • Mehryar Mohri, Scott Yang

We introduce a natural extension of the notion of swap regret, conditional swap regret, that allows for action modifications conditioned on the player’s action history.

no code implementations • 23 Nov 2014 • Mehryar Mohri, Andres Muñoz Medina

We study revenue optimization learning algorithms for posted-price auctions with strategic buyers.

no code implementations • 7 May 2014 • Corinna Cortes, Mehryar Mohri, Andres Muñoz Medina

We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm previously shown to outperform a number of algorithms for this task.

no code implementations • NeurIPS 2013 • Corinna Cortes, Marius Kloft, Mehryar Mohri

We use the notion of local Rademacher complexity to design new algorithms for learning kernels.

no code implementations • 22 Oct 2013 • Corinna Cortes, Spencer Greenberg, Mehryar Mohri

We present an extensive analysis of relative deviation bounds, including detailed proofs of two-sided inequalities and their implications.

no code implementations • 21 Oct 2013 • Mehryar Mohri, Andres Muñoz Medina

Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly de- pends on the outcome of such auctions.

no code implementations • 6 Jun 2013 • Spencer Greenberg, Mehryar Mohri

We give the proof of a tight lower bound on the probability that a binomial random variable exceeds its expected value.

no code implementations • 1 May 2013 • Mehryar Mohri, Afshin Rostamizadeh

We present a brief survey of existing mistake bounds and introduce novel bounds for the Perceptron or the kernel Perceptron algorithm.

no code implementations • NeurIPS 2012 • Stephen Boyd, Corinna Cortes, Mehryar Mohri, Ana Radovanovic

We introduce a new notion of classification accuracy based on the top $\tau$-quantile values of a scoring function, a relevant criterion in a number of problems arising for search engines.

no code implementations • 2 Mar 2012 • Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh

Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment.

no code implementations • NeurIPS 2010 • Corinna Cortes, Yishay Mansour, Mehryar Mohri

This paper presents an analysis of importance weighting for learning from finite samples and gives a series of theoretical and algorithmic results.

no code implementations • NeurIPS 2009 • Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Corinna Cortes, Mehryar Mohri

We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score.

no code implementations • NeurIPS 2009 • Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh

This paper studies the general problem of learning kernels based on a polynomial combination of base kernels.

no code implementations • NeurIPS 2009 • Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

A crucial technique for scaling kernel methods to very large data sets reaching or exceeding millions of instances is based on low-rank approximation of kernel matrices.

no code implementations • NeurIPS 2009 • Ryan Mcdonald, Mehryar Mohri, Nathan Silberman, Dan Walker, Gideon S. Mann

Training conditional maximum entropy models on massive data requires significant time and computational resources.

no code implementations • 19 Feb 2009 • Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh

This motivates our analysis of the problem of minimizing the empirical discrepancy for various loss functions for which we also give novel algorithms.

no code implementations • NeurIPS 2008 • Mehryar Mohri, Afshin Rostamizadeh

In particular, they are data-dependent and measure the complexity of a class of hypotheses based on the training sample.

no code implementations • NeurIPS 2008 • Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh

The problem consists of combining these hypotheses to derive a hypothesis with small error with respect to the target domain.

no code implementations • NeurIPS 2007 • Mehryar Mohri, Afshin Rostamizadeh

We also illustrate their application in the case of several general classes of learning algorithms, including Support Vector Regression and Kernel Ridge Regression.

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