341 research outputs found
ChoiceRank: Identifying Preferences from Node Traffic in Networks
Understanding how users navigate in a network is of high interest in many
applications. We consider a setting where only aggregate node-level traffic is
observed and tackle the task of learning edge transition probabilities. We cast
it as a preference learning problem, and we study a model where choices follow
Luce's axiom. In this case, the marginal counts of node visits are a
sufficient statistic for the transition probabilities. We show how to
make the inference problem well-posed regardless of the network's structure,
and we present ChoiceRank, an iterative algorithm that scales to networks that
contains billions of nodes and edges. We apply the model to two clickstream
datasets and show that it successfully recovers the transition probabilities
using only the network structure and marginal (node-level) traffic data.
Finally, we also consider an application to mobility networks and apply the
model to one year of rides on New York City's bicycle-sharing system.Comment: Accepted at ICML 201
Just Sort It! A Simple and Effective Approach to Active Preference Learning
We address the problem of learning a ranking by using adaptively chosen
pairwise comparisons. Our goal is to recover the ranking accurately but to
sample the comparisons sparingly. If all comparison outcomes are consistent
with the ranking, the optimal solution is to use an efficient sorting
algorithm, such as Quicksort. But how do sorting algorithms behave if some
comparison outcomes are inconsistent with the ranking? We give favorable
guarantees for Quicksort for the popular Bradley-Terry model, under natural
assumptions on the parameters. Furthermore, we empirically demonstrate that
sorting algorithms lead to a very simple and effective active learning
strategy: repeatedly sort the items. This strategy performs as well as
state-of-the-art methods (and much better than random sampling) at a minuscule
fraction of the computational cost.Comment: Accepted at ICML 201
The Entropy of Conditional Markov Trajectories
To quantify the randomness of Markov trajectories with fixed initial and
final states, Ekroot and Cover proposed a closed-form expression for the
entropy of trajectories of an irreducible finite state Markov chain. Numerous
applications, including the study of random walks on graphs, require the
computation of the entropy of Markov trajectories conditioned on a set of
intermediate states. However, the expression of Ekroot and Cover does not allow
for computing this quantity. In this paper, we propose a method to compute the
entropy of conditional Markov trajectories through a transformation of the
original Markov chain into a Markov chain that exhibits the desired conditional
distribution of trajectories. Moreover, we express the entropy of Markov
trajectories - a global quantity - as a linear combination of local entropies
associated with the Markov chain states.Comment: Accepted for publication in IEEE Transactions on Information Theor
Regression Networks for Meta-Learning Few-Shot Classification
We propose regression networks for the problem of few-shot classification,
where a classifier must generalize to new classes not seen in the training set,
given only a small number of examples of each class. In high dimensional
embedding spaces the direction of data generally contains richer information
than magnitude. Next to this, state-of-the-art few-shot metric methods that
compare distances with aggregated class representations, have shown superior
performance. Combining these two insights, we propose to meta-learn
classification of embedded points by regressing the closest approximation in
every class subspace while using the regression error as a distance metric.
Similarly to recent approaches for few-shot learning, regression networks
reflect a simple inductive bias that is beneficial in this limited-data regime
and they achieve excellent results, especially when more aggregate class
representations can be formed with multiple shots.Comment: 7th ICML Workshop on Automated Machine Learning (2020
Mitigating Epidemics through Mobile Micro-measures
Epidemics of infectious diseases are among the largest threats to the quality
of life and the economic and social well-being of developing countries. The
arsenal of measures against such epidemics is well-established, but costly and
insufficient to mitigate their impact. In this paper, we argue that mobile
technology adds a powerful weapon to this arsenal, because (a) mobile devices
endow us with the unprecedented ability to measure and model the detailed
behavioral patterns of the affected population, and (b) they enable the
delivery of personalized behavioral recommendations to individuals in real
time. We combine these two ideas and propose several strategies to generate
such recommendations from mobility patterns. The goal of each strategy is a
large reduction in infections, with a small impact on the normal course of
daily life. We evaluate these strategies over the Orange D4D dataset and show
the benefit of mobile micro-measures, even if only a fraction of the population
participates. These preliminary results demonstrate the potential of mobile
technology to complement other measures like vaccination and quarantines
against disease epidemics.Comment: Presented at NetMob 2013, Bosto
Fast Interactive Search with a Scale-Free Comparison Oracle
A comparison-based search algorithm lets a user find a target item in a
database by answering queries of the form, ``Which of items and is
closer to ?'' Instead of formulating an explicit query (such as one or
several keywords), the user navigates towards the target via a sequence of such
(typically noisy) queries.
We propose a scale-free probabilistic oracle model called -CKL for
such similarity triplets , which generalizes the CKL triplet model
proposed in the literature. The generalization affords independent control over
the discriminating power of the oracle and the dimension of the feature space
containing the items.
We develop a search algorithm with provably exponential rate of convergence
under the -CKL oracle, thanks to a backtracking strategy that deals
with the unavoidable errors in updating the belief region around the target.
We evaluate the performance of the algorithm both over the posited oracle and
over several real-world triplet datasets. We also report on a comprehensive
user study, where human subjects navigate a database of face portraits
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