260,378 research outputs found
LambdaLoss: Metric-Driven Loss for Learning-to Rank
How to directly optimize ranking metrics such as Normalized Discounted Cumulative Gain (NDCG) is an interesting but challenging problem, because ranking metrics are either flat or discontinuous everywhere. Among existing approaches, LambdaRank is a novel algorithm that incorporates metrics into its learning procedure. Though empirically effective, it still lacks theoretical justification. For example, what is the underlying loss that LambdaRank optimizes for? Due to this, it is unclear whether LambdaRank will always converge. In this paper, we present a well-defined loss for LambdaRank in a probabilistic framework and show that LambdaRank is a special configuration in our framework. This framework, which we call LambdaLoss, provides theoretical justification for Lamb-daRank. Furthermore, we propose a few more metric-driven loss functions in our LambdaLoss framework. Our loss functions have clear connection to ranking metrics and can be optimized in our framework efficiently. Experiments on three publicly available data sets show that our methods significantly outperform the state-of-the-art learning-to-rank algorithms. This confirms both the theoretical soundness and the practical effectiveness of the LambdaLoss framework
Grafting for Combinatorial Boolean Model using Frequent Itemset Mining
This paper introduces the combinatorial Boolean model (CBM), which is defined
as the class of linear combinations of conjunctions of Boolean attributes. This
paper addresses the issue of learning CBM from labeled data. CBM is of high
knowledge interpretability but na\"{i}ve learning of it requires exponentially
large computation time with respect to data dimension and sample size. To
overcome this computational difficulty, we propose an algorithm GRAB (GRAfting
for Boolean datasets), which efficiently learns CBM within the
-regularized loss minimization framework. The key idea of GRAB is to
reduce the loss minimization problem to the weighted frequent itemset mining,
in which frequent patterns are efficiently computable. We employ benchmark
datasets to empirically demonstrate that GRAB is effective in terms of
computational efficiency, prediction accuracy and knowledge discovery
A Bayesian Approach toward Active Learning for Collaborative Filtering
Collaborative filtering is a useful technique for exploiting the preference
patterns of a group of users to predict the utility of items for the active
user. In general, the performance of collaborative filtering depends on the
number of rated examples given by the active user. The more the number of rated
examples given by the active user, the more accurate the predicted ratings will
be. Active learning provides an effective way to acquire the most informative
rated examples from active users. Previous work on active learning for
collaborative filtering only considers the expected loss function based on the
estimated model, which can be misleading when the estimated model is
inaccurate. This paper takes one step further by taking into account of the
posterior distribution of the estimated model, which results in more robust
active learning algorithm. Empirical studies with datasets of movie ratings
show that when the number of ratings from the active user is restricted to be
small, active learning methods only based on the estimated model don't perform
well while the active learning method using the model distribution achieves
substantially better performance.Comment: Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
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