139 research outputs found
Learning to Rank from Samples of Variable Quality
Training deep neural networks requires many training samples, but in
practice, training labels are expensive to obtain and may be of varying
quality, as some may be from trusted expert labelers while others might be from
heuristics or other sources of weak supervision such as crowd-sourcing. This
creates a fundamental quality-versus quantity trade-off in the learning
process. Do we learn from the small amount of high-quality data or the
potentially large amount of weakly-labeled data? We argue that if the learner
could somehow know and take the label-quality into account when learning the
data representation, we could get the best of both worlds. To this end, we
introduce "fidelity-weighted learning" (FWL), a semi-supervised student-teacher
approach for training deep neural networks using weakly-labeled data. FWL
modulates the parameter updates to a student network (trained on the task we
care about) on a per-sample basis according to the posterior confidence of its
label-quality estimated by a teacher (who has access to the high-quality
labels). Both student and teacher are learned from the data. We evaluate FWL on
document ranking where we outperform state-of-the-art alternative
semi-supervised methods.Comment: Presented at The First International SIGIR2016 Workshop on Learning
From Limited Or Noisy Data For Information Retrieval. arXiv admin note:
substantial text overlap with arXiv:1711.0279
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Users try to articulate their complex information needs during search
sessions by reformulating their queries. To make this process more effective,
search engines provide related queries to help users in specifying the
information need in their search process. In this paper, we propose a
customized sequence-to-sequence model for session-based query suggestion. In
our model, we employ a query-aware attention mechanism to capture the structure
of the session context. is enables us to control the scope of the session from
which we infer the suggested next query, which helps not only handle the noisy
data but also automatically detect session boundaries. Furthermore, we observe
that, based on the user query reformulation behavior, within a single session a
large portion of query terms is retained from the previously submitted queries
and consists of mostly infrequent or unseen terms that are usually not included
in the vocabulary. We therefore empower the decoder of our model to access the
source words from the session context during decoding by incorporating a copy
mechanism. Moreover, we propose evaluation metrics to assess the quality of the
generative models for query suggestion. We conduct an extensive set of
experiments and analysis. e results suggest that our model outperforms the
baselines both in terms of the generating queries and scoring candidate queries
for the task of query suggestion.Comment: Accepted to be published at The 26th ACM International Conference on
Information and Knowledge Management (CIKM2017
Generalized Group Profiling for Content Customization
There is an ongoing debate on personalization, adapting results to the unique
user exploiting a user's personal history, versus customization, adapting
results to a group profile sharing one or more characteristics with the user at
hand. Personal profiles are often sparse, due to cold start problems and the
fact that users typically search for new items or information, necessitating to
back-off to customization, but group profiles often suffer from accidental
features brought in by the unique individual contributing to the group. In this
paper we propose a generalized group profiling approach that teases apart the
exact contribution of the individual user level and the "abstract" group level
by extracting a latent model that captures all, and only, the essential
features of the whole group. Our main findings are the followings. First, we
propose an efficient way of group profiling which implicitly eliminates the
general and specific features from users' models in a group and takes out the
abstract model representing the whole group. Second, we employ the resulting
models in the task of contextual suggestion. We analyse different grouping
criteria and we find that group-based suggestions improve the customization.
Third, we see that the granularity of groups affects the quality of group
profiling. We observe that grouping approach should compromise between the
level of customization and groups' size.Comment: Short paper (4 pages) published in proceedings of ACM SIGIR
Conference on Human Information Interaction and Retrieval (CHIIR'16
Out-of-pocket costs analysis of ifosfamide, epirubicin, and etoposide (IEV) and etoposide, solu-medrol-methylprednisolone, high-dose ara-C-cytarabine, and platinol-cisplatin (ESHAP) regimens in the patients with relapsed and refractory lymphoma in Iran
BACKGROUND: This is an out-of-pocket costs analysis of ifosfamide, epirubicin, and etoposide (IEV) and etoposide, solu-medrol-methylprednisolone, high-dose ara-C-cytarabine, and platinol-cisplatin (ESHAP) drug regimens in treatment of lymphoma in Iran.METHODS: This cross-sectional study was conducted in Shiraz City. Data were collected using a data-collection form. The social perspective was used to collect cost data. Three types of costs were measured, medical direct costs, non-medical direct costs, and indirect costs.RESULTS: 65 patients were treated with these two methods; 27 patients were treated with IEV and 38 with ESHAP. Moreover, the mean direct cost in IEV and ESHAP regimens in 2014 were 1191.10 ± 610.74 and 1819.57 ± 789.73 United States dollars (USD), respectively. The difference was statistically significant (P < 0.001).CONCLUSION: In this study, costs in the IEV regimen were significantly lower than the ESHAP regimen. This was particularly caused by an earlier discharge of patients under IEV regimen; since these patients experienced a trend toward less neutropenia and, hence, had a trend toward fewer hospitalization days, the related cost was 3451.76 USD with savings of 6479.61 USD compared with the ESHAP regimen. Overall, most of patient’s income was spent on out-of-pocket costs for all expenditures incurred because of lymphoma
Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
Deep neural networks have become a primary tool for solving problems in many
fields. They are also used for addressing information retrieval problems and
show strong performance in several tasks. Training these models requires large,
representative datasets and for most IR tasks, such data contains sensitive
information from users. Privacy and confidentiality concerns prevent many data
owners from sharing the data, thus today the research community can only
benefit from research on large-scale datasets in a limited manner. In this
paper, we discuss privacy preserving mimic learning, i.e., using predictions
from a privacy preserving trained model instead of labels from the original
sensitive training data as a supervision signal. We present the results of
preliminary experiments in which we apply the idea of mimic learning and
privacy preserving mimic learning for the task of document re-ranking as one of
the core IR tasks. This research is a step toward laying the ground for
enabling researchers from data-rich environments to share knowledge learned
from actual users' data, which should facilitate research collaborations.Comment: SIGIR 2017 Workshop on Neural Information Retrieval
(Neu-IR'17)}{}{August 7--11, 2017, Shinjuku, Tokyo, Japa
Fidelity-Weighted Learning
Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations.Comment: Published as a conference paper at ICLR 201
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