As a result we can learn a kernel that transfers to new tasks; we call this Deep Kernel Transfer (DKT) .

The proposed methodology is a variant of principal component regression (PCR).

We analyze a reweighted version of the Kikuchi approximation for estimating the log partition function of a product distribution defined over a region graph. We establish sufficient conditions for the concavity of our reweighted objective function in terms of weight assignments in the Kikuchi expansion, and show that a reweighted version of the sum product algorithm applied to the Kikuchi region graph will produce global optima of the Kikuchi approximation whenever the algorithm converges. When the region graph has two layers, corresponding to a Bethe approximation, we show that our sufficient conditions for concavity are also necessary. Finally, we provide an explicit characterization of the polytope of concavity in terms of the cycle structure of the region graph. We conclude with simulations that demonstrate the advantages of the reweighted Kikuchi approach.

One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest "weak" paths in a unknown causal graph. The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of "path cancellations" that will imply conditional independencies but do not rule out the existence of confounding causal paths. The outcome is a posterior distribution over bounds on the average causal effect via a linear programming approach and Bayesian inference. We claim this approach should be used in regular practice to complement other default tools in observational studies.

Importance sampling is an essential component of off-policy model-free reinforcement learning algorithms. However, its most effective variant, weighted importance sampling, does not carry over easily to function approximation and, because of this, it is not utilized in existing off-policy learning algorithms. In this paper, we take two steps toward bridging this gap. First, we show that weighted importance sampling can be viewed as a special case of weighting the error of individual training samples, and that this weighting has theoretical and empirical benefits similar to those of weighted importance sampling. Second, we show that these benefits extend to a new weighted-importance-sampling version of off-policy LSTD(). We show empirically that our new WIS-LSTD() algorithm can result in much more rapid and reliable convergence than conventional off-policy LSTD() (Y u 2010, Bertsekas & Y u 2009).

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper introduces a full model for tracking while allowing for multiple and varying number of hypothesis and clutter. It promises a clear notation and fast algorithms through the use of variational/Baum-Welch type inference. Experiments appear extensive and are performed on real-world data. The key novelty of this paper is the assignment problem (aka data association). Tracking itself, as the authors acknowledge, is a well-trodden field.