67,515 research outputs found
Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning
A general framework of unsupervised learning for combinatorial optimization
(CO) is to train a neural network (NN) whose output gives a problem solution by
directly optimizing the CO objective. Albeit with some advantages over
traditional solvers, the current framework optimizes an averaged performance
over the distribution of historical problem instances, which misaligns with the
actual goal of CO that looks for a good solution to every future encountered
instance. With this observation, we propose a new objective of unsupervised
learning for CO where the goal of learning is to search for good initialization
for future problem instances rather than give direct solutions. We propose a
meta-learning-based training pipeline for this new objective. Our method
achieves good empirical performance. We observe that even just the initial
solution given by our model before fine-tuning can significantly outperform the
baselines under various evaluation settings including evaluation across
multiple datasets, and the case with big shifts in the problem scale. The
reason we conjecture is that meta-learning-based training lets the model be
loosely tied to each local optima for a training instance while being more
adaptive to the changes of optimization landscapes across instances.Comment: Our code is available at: https://github.com/Graph-COM/Meta_C
Multi-View Learning and Link Farm Discovery
The first part of this abstract focuses on estimation of mixture models for problems in which multiple views of the instances are available. Examples of this setting include clustering web pages or research papers that have intrinsic (text) and extrinsic (references) attributes. Mixture model estimation is a key problem for both semi-supervised and unsupervised learning. An appropriate optimization criterion quantifies the likelihood and the consensus among models in the individual views; maximizing this consensus minimizes a bound on the risk of assigning an instance to an incorrect mixture component. An EM algorithm maximizes this criterion. The second part of this abstract focuses on the problem of identifying link spam. Search engine optimizers inflate the page rank of a target site by spinning an artificial web for the sole purpose of providing inbound links to the target. Discriminating natural from artificial web sites is a difficult multi-view problem
Unsupervised Prediction Aggregation
Consider the scenario where votes from multiple experts utilizing different data modalities or modeling assumptions are available for a given prediction task. The task of combining these signals with the goal of obtaining a better prediction is ubiquitous in Information Retrieval (IR), Natural Language Processing (NLP) and many other areas. In IR, for instance, meta-search aims to combine the outputs of multiple search engines to produce a better ranking. In NLP, aggregation of the outputs of computer systems generating natural language translations [7], syntactic dependency parses [8], identifying intended meanings of words [1], and others has received considerable recent attention. Most existing learning approaches to aggregation address the supervised setting. However, for complex prediction tasks such as these, data annotation is a very labor intensive and time consuming process. In this line of work, we first derive a mathematical and algorithmic framework for learning to combine predictions from multiple signals without supervision. In particular, we use the extended Mallows formalism (e.g. [5, 4]) for modeling aggregation, and derive an unsupervised learning procedure for estimating the model parameters [2]. While direct application of the learning framework can be computationally expensive in general, we propose alternatives to keep learning and inferenc
Co-regularized Alignment for Unsupervised Domain Adaptation
Deep neural networks, trained with large amount of labeled data, can fail to
generalize well when tested with examples from a \emph{target domain} whose
distribution differs from the training data distribution, referred as the
\emph{source domain}. It can be expensive or even infeasible to obtain required
amount of labeled data in all possible domains. Unsupervised domain adaptation
sets out to address this problem, aiming to learn a good predictive model for
the target domain using labeled examples from the source domain but only
unlabeled examples from the target domain. Domain alignment approaches this
problem by matching the source and target feature distributions, and has been
used as a key component in many state-of-the-art domain adaptation methods.
However, matching the marginal feature distributions does not guarantee that
the corresponding class conditional distributions will be aligned across the
two domains. We propose co-regularized domain alignment for unsupervised domain
adaptation, which constructs multiple diverse feature spaces and aligns source
and target distributions in each of them individually, while encouraging that
alignments agree with each other with regard to the class predictions on the
unlabeled target examples. The proposed method is generic and can be used to
improve any domain adaptation method which uses domain alignment. We
instantiate it in the context of a recent state-of-the-art method and observe
that it provides significant performance improvements on several domain
adaptation benchmarks.Comment: NIPS 2018 accepted versio
Zero-Annotation Object Detection with Web Knowledge Transfer
Object detection is one of the major problems in computer vision, and has
been extensively studied. Most of the existing detection works rely on
labor-intensive supervision, such as ground truth bounding boxes of objects or
at least image-level annotations. On the contrary, we propose an object
detection method that does not require any form of human annotation on target
tasks, by exploiting freely available web images. In order to facilitate
effective knowledge transfer from web images, we introduce a multi-instance
multi-label domain adaption learning framework with two key innovations. First
of all, we propose an instance-level adversarial domain adaptation network with
attention on foreground objects to transfer the object appearances from web
domain to target domain. Second, to preserve the class-specific semantic
structure of transferred object features, we propose a simultaneous transfer
mechanism to transfer the supervision across domains through pseudo strong
label generation. With our end-to-end framework that simultaneously learns a
weakly supervised detector and transfers knowledge across domains, we achieved
significant improvements over baseline methods on the benchmark datasets.Comment: Accepted in ECCV 201
Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees
This paper investigates an important problem in stream mining, i.e.,
classification under streaming emerging new classes or SENC. The common
approach is to treat it as a classification problem and solve it using either a
supervised learner or a semi-supervised learner. We propose an alternative
approach by using unsupervised learning as the basis to solve this problem. The
SENC problem can be decomposed into three sub problems: detecting emerging new
classes, classifying for known classes, and updating models to enable
classification of instances of the new class and detection of more emerging new
classes. The proposed method employs completely random trees which have been
shown to work well in unsupervised learning and supervised learning
independently in the literature. This is the first time, as far as we know,
that completely random trees are used as a single common core to solve all
three sub problems: unsupervised learning, supervised learning and model update
in data streams. We show that the proposed unsupervised-learning-focused method
often achieves significantly better outcomes than existing
classification-focused methods
- …