44 research outputs found
Conversational Exploratory Search via Interactive Storytelling
Conversational interfaces are likely to become more efficient, intuitive and
engaging way for human-computer interaction than today's text or touch-based
interfaces. Current research efforts concerning conversational interfaces focus
primarily on question answering functionality, thereby neglecting support for
search activities beyond targeted information lookup. Users engage in
exploratory search when they are unfamiliar with the domain of their goal,
unsure about the ways to achieve their goals, or unsure about their goals in
the first place. Exploratory search is often supported by approaches from
information visualization. However, such approaches cannot be directly
translated to the setting of conversational search.
In this paper we investigate the affordances of interactive storytelling as a
tool to enable exploratory search within the framework of a conversational
interface. Interactive storytelling provides a way to navigate a document
collection in the pace and order a user prefers. In our vision, interactive
storytelling is to be coupled with a dialogue-based system that provides verbal
explanations and responsive design. We discuss challenges and sketch the
research agenda required to put this vision into life.Comment: Accepted at ICTIR'17 Workshop on Search-Oriented Conversational AI
(SCAI 2017
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank
Unbiased CLTR requires click propensities to compensate for the difference
between user clicks and true relevance of search results via IPS. Current
propensity estimation methods assume that user click behavior follows the PBM
and estimate click propensities based on this assumption. However, in reality,
user clicks often follow the CM, where users scan search results from top to
bottom and where each next click depends on the previous one. In this cascade
scenario, PBM-based estimates of propensities are not accurate, which, in turn,
hurts CLTR performance. In this paper, we propose a propensity estimation
method for the cascade scenario, called CM-IPS. We show that CM-IPS keeps CLTR
performance close to the full-information performance in case the user clicks
follow the CM, while PBM-based CLTR has a significant gap towards the
full-information. The opposite is true if the user clicks follow PBM instead of
the CM. Finally, we suggest a way to select between CM- and PBM-based
propensity estimation methods based on historical user clicks.Comment: 4 pages, 2 figures, 43rd International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR '20
ViTOR: Learning to Rank Webpages Based on Visual Features
The visual appearance of a webpage carries valuable information about its
quality and can be used to improve the performance of learning to rank (LTR).
We introduce the Visual learning TO Rank (ViTOR) model that integrates
state-of-the-art visual features extraction methods by (i) transfer learning
from a pre-trained image classification model, and (ii) synthetic saliency heat
maps generated from webpage snapshots. Since there is currently no public
dataset for the task of LTR with visual features, we also introduce and release
the ViTOR dataset, containing visually rich and diverse webpages. The ViTOR
dataset consists of visual snapshots, non-visual features and relevance
judgments for ClueWeb12 webpages and TREC Web Track queries. We experiment with
the proposed ViTOR model on the ViTOR dataset and show that it significantly
improves the performance of LTR with visual featuresComment: In Proceedings of the 2019 World Wide Web Conference (WWW 2019), May
2019, San Francisc
Towards stable real-world equation discovery with assessing differentiating quality influence
This paper explores the critical role of differentiation approaches for
data-driven differential equation discovery. Accurate derivatives of the input
data are essential for reliable algorithmic operation, particularly in
real-world scenarios where measurement quality is inevitably compromised. We
propose alternatives to the commonly used finite differences-based method,
notorious for its instability in the presence of noise, which can exacerbate
random errors in the data. Our analysis covers four distinct methods:
Savitzky-Golay filtering, spectral differentiation, smoothing based on
artificial neural networks, and the regularization of derivative variation. We
evaluate these methods in terms of applicability to problems, similar to the
real ones, and their ability to ensure the convergence of equation discovery
algorithms, providing valuable insights for robust modeling of real-world
processes
Safe Exploration for Optimizing Contextual Bandits
Contextual bandit problems are a natural fit for many information retrieval
tasks, such as learning to rank, text classification, recommendation, etc.
However, existing learning methods for contextual bandit problems have one of
two drawbacks: they either do not explore the space of all possible document
rankings (i.e., actions) and, thus, may miss the optimal ranking, or they
present suboptimal rankings to a user and, thus, may harm the user experience.
We introduce a new learning method for contextual bandit problems, Safe
Exploration Algorithm (SEA), which overcomes the above drawbacks. SEA starts by
using a baseline (or production) ranking system (i.e., policy), which does not
harm the user experience and, thus, is safe to execute, but has suboptimal
performance and, thus, needs to be improved. Then SEA uses counterfactual
learning to learn a new policy based on the behavior of the baseline policy.
SEA also uses high-confidence off-policy evaluation to estimate the performance
of the newly learned policy. Once the performance of the newly learned policy
is at least as good as the performance of the baseline policy, SEA starts using
the new policy to execute new actions, allowing it to actively explore
favorable regions of the action space. This way, SEA never performs worse than
the baseline policy and, thus, does not harm the user experience, while still
exploring the action space and, thus, being able to find an optimal policy. Our
experiments using text classification and document retrieval confirm the above
by comparing SEA (and a boundless variant called BSEA) to online and offline
learning methods for contextual bandit problems.Comment: 23 pages, 3 figure
Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation
Heart rate variability (HRV) indices describe properties of interbeat
intervals in electrocardiogram (ECG). Usually HRV is measured exclusively in
normal sinus rhythm (NSR) excluding any form of paroxysmal rhythm. Atrial
fibrillation (AF) is the most widespread cardiac arrhythmia in human
population. Usually such abnormal rhythm is not analyzed and assumed to be
chaotic and unpredictable. Nonetheless, ranges of HRV indices differ between
patients with AF, yet physiological characteristics which influence them are
poorly understood. In this study, we propose a statistical model that describes
relationship between HRV indices in NSR and AF. The model is based on
Mahalanobis distance, the k-Nearest neighbour approach and multivariate normal
distribution framework. Verification of the method was performed using 10 min
intervals of NSR and AF that were extracted from long-term Holter ECGs. For
validation we used Bhattacharyya distance and Kolmogorov-Smirnov 2-sample test
in a k-fold procedure. The model is able to predict at least 7 HRV indices with
high precision.Comment: Ural-Siberian Conference on Computational Technologies in Cognitive
Science, Genomics and Biomedicine 2022 (CSGB 2022
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Online ranker evaluation is one of the key challenges in information
retrieval. While the preferences of rankers can be inferred by interleaving
methods, the problem of how to effectively choose the ranker pair that
generates the interleaved list without degrading the user experience too much
is still challenging. On the one hand, if two rankers have not been compared
enough, the inferred preference can be noisy and inaccurate. On the other, if
two rankers are compared too many times, the interleaving process inevitably
hurts the user experience too much. This dilemma is known as the exploration
versus exploitation tradeoff. It is captured by the -armed dueling bandit
problem, which is a variant of the -armed bandit problem, where the feedback
comes in the form of pairwise preferences. Today's deployed search systems can
evaluate a large number of rankers concurrently, and scaling effectively in the
presence of numerous rankers is a critical aspect of -armed dueling bandit
problems.
In this paper, we focus on solving the large-scale online ranker evaluation
problem under the so-called Condorcet assumption, where there exists an optimal
ranker that is preferred to all other rankers. We propose Merge Double Thompson
Sampling (MergeDTS), which first utilizes a divide-and-conquer strategy that
localizes the comparisons carried out by the algorithm to small batches of
rankers, and then employs Thompson Sampling (TS) to reduce the comparisons
between suboptimal rankers inside these small batches. The effectiveness
(regret) and efficiency (time complexity) of MergeDTS are extensively evaluated
using examples from the domain of online evaluation for web search. Our main
finding is that for large-scale Condorcet ranker evaluation problems, MergeDTS
outperforms the state-of-the-art dueling bandit algorithms.Comment: Accepted at TOI
NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training. One popular communication-compression method for data-parallel SGD is QSGD (Alistarh et al., 2017), which quantizes and encodes gradients to reduce communication costs. The baseline variant of QSGD provides strong theoretical guarantees, however, for practical purposes, the authors proposed a heuristic variant which we call QSGDinf, which demonstrated impressive empirical gains for distributed training of large neural networks. In this paper, we build on this work to propose a new gradient quantization scheme, and show that it has both stronger theoretical guarantees than QSGD, and matches and exceeds the empirical performance of the QSGDinf heuristic and of other compression methods
Subclinical Hypothyroidism after Radioiodine Exposure: Ukrainian–American Cohort Study of Thyroid Cancer and Other Thyroid Diseases after the Chornobyl Accident (1998–2000)
BackgroundHypothyroidism is the most common thyroid abnormality in patients treated with high doses of iodine-131 (131I). Data on risk of hypothyroidism from low to moderate 131I thyroid doses are limited and inconsistent.ObjectiveThis study was conducted to quantify the risk of hypothyroidism prevalence in relation to 131I doses received because of the Chornobyl accident.MethodsThis is a cross-sectional (1998-2000) screening study of thyroid diseases in a cohort of 11,853 individuals < 18 years of age at the time of the accident, with individual thyroid radioactivity measurements taken within 2 months of the accident. We measured thyroid-stimulating hormone (TSH), free thyroxine, and antibodies to thyroid peroxidase (ATPO) in serum.ResultsMean age at examination of the analysis cohort was 21.6 years (range, 12.2-32.5 years), with 49% females. Mean 131I thyroid dose was 0.79 Gy (range, 0-40.7 Gy). There were 719 cases with hypothyroidism (TSH > 4 mIU/L), including 14 with overt hypothyroidism. We found a significant, small association between (131)I thyroid doses and prevalent hypothyroidism, with the excess odds ratio (EOR) per gray of 0.10 (95% confidence interval, 0.03-0.21). EOR per gray was higher in individuals with ATPO < or = 60 U/mL compared with individuals with ATPO > 60 U/mL (p < 0.001).ConclusionsThis is the first study to find a significant relationship between prevalence of hypothyroidism and individual (131)I thyroid doses due to environmental exposure. The radiation increase in hypothyroidism was small (10% per Gy) and limited largely to subclinical hypothyroidism. Prospective data are needed to evaluate the dynamics of radiation-related hypothyroidism and clarify the role of antithyroid antibodies