#### Filter Results:

#### Publication Year

2007

2016

#### Publication Type

#### Co-author

#### Key Phrase

#### Publication Venue

#### Data Set Used

Learn More

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)

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)

This paper examines two-stage techniques for learning kernels based on a notion of alignment. It presents a number of novel theoretical, al-gorithmic, and empirical results for alignment-based 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… (More)

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 L 1 regular-ization admits only a √ log p dependency on the number of kernels, which… (More)

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)

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)

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)

This paper presents a novel theoretical study of the general problem of multiple source adaptation using the notion of Rényi divergence. Our results build on our previous work [12], but significantly broaden the scope of that work in several directions. We extend previous multiple source loss guarantees based on distribution weighted combinations to… (More)