107 research outputs found
Exchangeable Variable Models
A sequence of random variables is exchangeable if its joint distribution is
invariant under variable permutations. We introduce exchangeable variable
models (EVMs) as a novel class of probabilistic models whose basic building
blocks are partially exchangeable sequences, a generalization of exchangeable
sequences. We prove that a family of tractable EVMs is optimal under zero-one
loss for a large class of functions, including parity and threshold functions,
and strictly subsumes existing tractable independence-based model families.
Extensive experiments show that EVMs outperform state of the art classifiers
such as SVMs and probabilistic models which are solely based on independence
assumptions.Comment: ICML 201
Lifted Probabilistic Inference: An MCMC Perspective
The general consensus seems to be that lifted
inference is concerned with exploiting model
symmetries and grouping indistinguishable
objects at inference time. Since first-order
probabilistic formalisms are essentially tem-
plate languages providing a more compact
representation of a corresponding ground
model, lifted inference tends to work especially well in these models. We show that the
notion of indistinguishability manifests itself
on several dferent levels {the level of constants, the level of ground atoms (variables),
the level of formulas (features), and the level
of assignments (possible worlds). We discuss
existing work in the MCMC literature on ex-
ploiting symmetries on the level of variable
assignments and relate it to novel results in
lifted MCMC
LRMM: Learning to Recommend with Missing Modalities
Multimodal learning has shown promising performance in content-based
recommendation due to the auxiliary user and item information of multiple
modalities such as text and images. However, the problem of incomplete and
missing modality is rarely explored and most existing methods fail in learning
a recommendation model with missing or corrupted modalities. In this paper, we
propose LRMM, a novel framework that mitigates not only the problem of missing
modalities but also more generally the cold-start problem of recommender
systems. We propose modality dropout (m-drop) and a multimodal sequential
autoencoder (m-auto) to learn multimodal representations for complementing and
imputing missing modalities. Extensive experiments on real-world Amazon data
show that LRMM achieves state-of-the-art performance on rating prediction
tasks. More importantly, LRMM is more robust to previous methods in alleviating
data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201
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