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- Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
- Adaptive computation and machine learning
- 2012

- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
- NIPS
- 2009

This paper studies the general problem of learning kernels based on a polynomial combination of base kernels. We analyze this problem in the case of regression and the kernel ridge regression algorithm. We examine the corresponding learning kernel optimization problem, show how that minimax problem can be reduced to a simpler minimization problem, and prove… (More)

- Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh
- COLT
- 2009

This paper addresses the general problem of domain adaptation which arises in a variety of applications where the distribution of the labeled sample available somewhat differs from that of the test data. Building on previous work by Ben-David et al. (2007), we introduce a novel distance between distributions, discrepancy distance, that is tailored to… (More)

- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
- ICML
- 2010

This paper examines two-stage techniques for learning kernels based on a notion of alignment. It presents a number of novel theoretical, algorithmic, and empirical results for alignmentbased techniques. Our results build on previous work by Cristianini et al. (2001), but we adopt a different definition of kernel alignment and significantly extend that work… (More)

- Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh
- NIPS
- 2008

This paper presents a theoretical analysis of the problem of adaptation with multiple sources. For each source domain, the distribution over the input points as well as a hypothesis with error at most ǫ are given. The problem consists of combining these hypotheses to derive a hypothesis with small error with respect to the target domain. We present several… (More)

- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
- Journal of Machine Learning Research
- 2012

This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combination solution that has proven to be difficult to improve upon in the past, as well as other algorithms for learning kernels based on convex combinations of base kernels… (More)

- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
- ICML
- 2010

This paper presents several novel generalization bounds for the problem of learning kernels based on a combinatorial analysis of the Rademacher complexity of the corresponding hypothesis sets. Our bound for learning kernels with a convex combination of p base kernels using L1 regularization admits only a √ log p dependency on the number of kernels, which is… (More)

The choice of the kernel is critical to the success of many learning algorithms but it is typically left to the user. Instead, the training data can be used to learn the kernel by selecting it out of a given family, such as that of non-negative linear combinations of p base kernels, constrained by a trace or L1 regularization. This paper studies the problem… (More)

This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution. This relies on weights derived by various estimation techniques based… (More)

We address the problem of balancing the traffic load in multi-hop wireless networks. We consider a point-to-point communicating network with a uniform distribution of source-sink pairs. When routing along shortest paths, the nodes that are centrally located forward a disproportionate amount of traffic. This translates into increased congestion and energy… (More)