88 research outputs found
A Sample Reuse Strategy for Dynamic Influence Maximization Problem
Dynamic influence maximization problem (DIMP) aims to maintain a group of
influential users within an evolving social network, so that the influence
scope can be maximized at any given moment. A primary category of DIMP
algorithms focuses on the renewal of reverse reachable (RR) sets, which is
designed for static social network scenarios, to accelerate the estimation of
influence spread. And the generation time of RR sets plays a crucial role in
algorithm efficiency. However, their update approaches require sequential
updates for each edge change, leading to considerable computational cost. In
this paper, we propose a strategy for batch updating the changes in network
edge weights to efficiently maintain RR sets. By calculating the probability
that previous RR sets can be regenerated at the current moment, we retain those
with a high probability. This method can effectively avoid the computational
cost associated with updating and sampling these RR sets. Besides, we propose
an resampling strategy that generates high-probability RR sets to make the
final distribution of RR sets approximate to the sampling probability
distribution under the current social network. The experimental results
indicate that our strategy is both scalable and efficient. On the one hand,
compared to the previous update strategies, the running time of our strategy is
insensitive to the number of changes in network weight; on the other hand, for
various RR set-based algorithms, our strategy can reduce the running time while
maintaining the solution quality that is essentially consistent with the static
algorithms
Automatic Construction of Parallel Portfolios via Explicit Instance Grouping
Simultaneously utilizing several complementary solvers is a simple yet
effective strategy for solving computationally hard problems. However, manually
building such solver portfolios typically requires considerable domain
knowledge and plenty of human effort. As an alternative, automatic construction
of parallel portfolios (ACPP) aims at automatically building effective parallel
portfolios based on a given problem instance set and a given rich design space.
One promising way to solve the ACPP problem is to explicitly group the
instances into different subsets and promote a component solver to handle each
of them.This paper investigates solving ACPP from this perspective, and
especially studies how to obtain a good instance grouping.The experimental
results showed that the parallel portfolios constructed by the proposed method
could achieve consistently superior performances to the ones constructed by the
state-of-the-art ACPP methods,and could even rival sophisticated hand-designed
parallel solvers
How Good Is Neural Combinatorial Optimization?
Traditional solvers for tackling combinatorial optimization (CO) problems are
usually designed by human experts. Recently, there has been a surge of interest
in utilizing Deep Learning, especially Deep Reinforcement Learning, to
automatically learn effective solvers for CO. The resultant new paradigm is
termed Neural Combinatorial Optimization (NCO). However, the advantages and
disadvantages of NCO over other approaches have not been well studied
empirically or theoretically. In this work, we present a comprehensive
comparative study of NCO solvers and alternative solvers. Specifically, taking
the Traveling Salesman Problem as the testbed problem, we assess the
performance of the solvers in terms of five aspects, i.e., effectiveness,
efficiency, stability, scalability and generalization ability. Our results show
that in general the solvers learned by NCO approaches still fall short of
traditional solvers in nearly all these aspects. A potential benefit of the
former would be their superior time and energy efficiency on small-size problem
instances when sufficient training instances are available. We hope this work
would help better understand the strengths and weakness of NCO, and provide a
comprehensive evaluation protocol for further benchmarking NCO approaches
against other approaches
On Performance Estimation in Automatic Algorithm Configuration
Over the last decade, research on automated parameter tuning, often referred
to as automatic algorithm configuration (AAC), has made significant progress.
Although the usefulness of such tools has been widely recognized in real world
applications, the theoretical foundations of AAC are still very weak. This
paper addresses this gap by studying the performance estimation problem in AAC.
More specifically, this paper first proves the universal best performance
estimator in a practical setting, and then establishes theoretical bounds on
the estimation error, i.e., the difference between the training performance and
the true performance for a parameter configuration, considering finite and
infinite configuration spaces respectively. These findings were verified in
extensive experiments conducted on four algorithm configuration scenarios
involving different problem domains. Moreover, insights for enhancing existing
AAC methods are also identified.Comment: accepted by AAAI 202
Multi-Domain Learning From Insufficient Annotations
Multi-domain learning (MDL) refers to simultaneously constructing a model or
a set of models on datasets collected from different domains. Conventional
approaches emphasize domain-shared information extraction and domain-private
information preservation, following the shared-private framework (SP models),
which offers significant advantages over single-domain learning. However, the
limited availability of annotated data in each domain considerably hinders the
effectiveness of conventional supervised MDL approaches in real-world
applications. In this paper, we introduce a novel method called multi-domain
contrastive learning (MDCL) to alleviate the impact of insufficient annotations
by capturing both semantic and structural information from both labeled and
unlabeled data.Specifically, MDCL comprises two modules: inter-domain semantic
alignment and intra-domain contrast. The former aims to align annotated
instances of the same semantic category from distinct domains within a shared
hidden space, while the latter focuses on learning a cluster structure of
unlabeled instances in a private hidden space for each domain. MDCL is readily
compatible with many SP models, requiring no additional model parameters and
allowing for end-to-end training. Experimental results across five textual and
image multi-domain datasets demonstrate that MDCL brings noticeable improvement
over various SP models.Furthermore, MDCL can further be employed in
multi-domain active learning (MDAL) to achieve a superior initialization,
eventually leading to better overall performance.Comment: This paper has been accepted to ECAI-2
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
Graph-based collaborative filtering has emerged as a powerful paradigm for
delivering personalized recommendations. Despite their demonstrated
effectiveness, these methods often neglect the underlying intents of users,
which constitute a pivotal facet of comprehensive user interests. Consequently,
a series of approaches have arisen to tackle this limitation by introducing
independent intent representations. However, these approaches fail to capture
the intricate relationships between intents of different users and the
compatibility between user intents and item properties.
To remedy the above issues, we propose a novel method, named uniformly
co-clustered intent modeling. Specifically, we devise a uniformly contrastive
intent modeling module to bring together the embeddings of users with similar
intents and items with similar properties. This module aims to model the
nuanced relations between intents of different users and properties of
different items, especially those unreachable to each other on the user-item
graph. To model the compatibility between user intents and item properties, we
design the user-item co-clustering module, maximizing the mutual information of
co-clusters of users and items. This approach is substantiated through
theoretical validation, establishing its efficacy in modeling compatibility to
enhance the mutual information between user and item representations.
Comprehensive experiments on various real-world datasets verify the
effectiveness of the proposed framework.Comment: In submissio
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