700 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Online Matching in Geometric Random Graphs
We investigate online maximum cardinality matching, a central problem in ad
allocation. In this problem, users are revealed sequentially, and each new user
can be paired with any previously unmatched campaign that it is compatible
with. Despite the limited theoretical guarantees, the greedy algorithm, which
matches incoming users with any available campaign, exhibits outstanding
performance in practice. Some theoretical support for this practical success
was established in specific classes of graphs, where the connections between
different vertices lack strong correlations - an assumption not always valid.
To bridge this gap, we focus on the following model: both users and campaigns
are represented as points uniformly distributed in the interval , and a
user is eligible to be paired with a campaign if they are similar enough, i.e.
the distance between their respective points is less than , with a
model parameter. As a benchmark, we determine the size of the optimal offline
matching in these bipartite random geometric graphs. In the online setting and
investigate the number of matches made by the online algorithm closest, which
greedily pairs incoming points with their nearest available neighbors. We
demonstrate that the algorithm's performance can be compared to its fluid
limit, which is characterized as the solution to a specific partial
differential equation (PDE). From this PDE solution, we can compute the
competitive ratio of closest, and our computations reveal that it remains
significantly better than its worst-case guarantee. This model turns out to be
related to the online minimum cost matching problem, and we can extend the
results to refine certain findings in that area of research. Specifically, we
determine the exact asymptotic cost of closest in the -excess regime,
providing a more accurate estimate than the previously known loose upper bound
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Improved Asymptotics for Multi-armed Bandit Experiments under Optimism-based Policies: Theory and Applications
The classical multi-armed bandit paradigm is a foundational framework for online decision making underlying a wide variety of important applications, e.g., clinical trials, advertising, sequential assignments, assortment optimization, etc. This work will examine two salient aspects of decision making that arise naturally in settings with large action spaces.
The first issue pertains to the division of samples across arms at the level of a trajectory (or sample-path). Traditional bounds at the ensemble-level (or in expectation) only translate to meaningful pathwise guarantees (high probability bounds) when the separation between mean rewards is ``large,'' commonly referred to as the ``well-separated'' regime in the literature. On the other hand, applications with a large action space are intrinsically endowed with smaller separations between arm-means (e.g., multiple products of similar quality in e-retail). As a result, classical ensemble-level guarantees for such problems become vacuous at the sample-path level in several settings. This theoretical gap in the understanding of bandit algorithms in the ``small gap'' regime can be of significant consequence in applications where considerations such as fairness and post hoc inference play an important role. Our work provides the first systematic treatment and analysis of this aspect under the celebrated UCB class of optimism-based bandit algorithms, including a complete diffusion-limit characterization of its regret. The diffusion-scale lens also reveals profound insights and highlights distinctions between UCB and the popular posterior sampling-based method, Thompson Sampling, such as an ``incomplete learning'' phenomenon that is characteristic of the latter.
The second research question studied in this work concerns the complexity of decision making in problems where the action space is endowed with a large number of substitutable alternatives. For example, it is common in e-retail for multiple brands to offer similar products (in terms of quality-of-service) that compete for revenue within a given product segment. We model the platform's decision problem in this example as a bandit with countably many arms, and investigate limits of achievable performance under canonical bandit algorithms adapted to this setting. We also propose novel rate-optimal algorithms that leverage results for the ``small gap'' regime alluded to earlier, and show that these outperform aforementioned conventional adaptations. We extend the countable-armed bandit paradigm to also serve as a basal motif in sequential assignment and dynamic matching problems typical of settings such as online labor markets.
The last chapter of this thesis investigates achievable performance in the countable-armed bandit problem under non-stationarity that is attributable to vanishing arms. This characteristic abstracts away certain attrition and churn processes observable in online markets, e.g., a popular brand may retract its product from a platform owing to under-exposure within its category -- a potential negative externality of the exploration carried out by the platform's policy
Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques
This article intends to systematically identify and comparatively analyze
state-of-the-art supply chain (SC) forecasting strategies and technologies. A
novel framework has been proposed incorporating Big Data Analytics in SC
Management (problem identification, data sources, exploratory data analysis,
machine-learning model training, hyperparameter tuning, performance evaluation,
and optimization), forecasting effects on human-workforce, inventory, and
overall SC. Initially, the need to collect data according to SC strategy and
how to collect them has been discussed. The article discusses the need for
different types of forecasting according to the period or SC objective. The SC
KPIs and the error-measurement systems have been recommended to optimize the
top-performing model. The adverse effects of phantom inventory on forecasting
and the dependence of managerial decisions on the SC KPIs for determining model
performance parameters and improving operations management, transparency, and
planning efficiency have been illustrated. The cyclic connection within the
framework introduces preprocessing optimization based on the post-process KPIs,
optimizing the overall control process (inventory management, workforce
determination, cost, production and capacity planning). The contribution of
this research lies in the standard SC process framework proposal, recommended
forecasting data analysis, forecasting effects on SC performance, machine
learning algorithms optimization followed, and in shedding light on future
research
Synchronization of data in heterogeneous decentralized systems
Data synchronization is the problem of reconciling the differences between large data stores that differ in a small number of records. It is a common thread among disparate distributed systems ranging from fleets of Internet of Things (IoT) devices to clusters of distributed databases in the cloud. Most recently, data synchronization has arisen in globally distributed public blockchains that build the basis for the envisioned decentralized Internet of the future. Moreover, the parallel development of edge computing has significantly increased the heterogeneity of networks and computing devices. The merger of highly heterogeneous system resources and the decentralized nature of future Internet applications calls for a new approach to data synchronization. In this dissertation, we look at the problem of data synchronization through the prism of set reconciliation and introduce novel tools and protocols that improve the performance of data synchronization in heterogeneous decentralized systems.
First, we compare the analytical properties of the state-of-the-art set reconciliation protocols, and investigate the impact of theoretical assumptions and implementation decisions on the synchronization performance. Second, we introduce GenSync, the first unified set reconciliation middleware. Using GenSync's distinctive benchmarking layer, we find that the best protocol choice is highly sensitive to the system conditions, and a bad protocol choice causes a severe hit in performance. We showcase the evaluative power of GenSync in one of the world's largest wireless network emulators, and demonstrate choosing the best GenSync protocol under a high and low user mobility in an emulated cellular network. Finally, we introduce SREP (Set Reconciliation-Enhanced Propagation), a novel blockchain transaction pool synchronization protocol with quantifiable guarantees. Through simulations, we show that SREP incurs significantly smaller bandwidth overhead than a similar approach from the literature, especially in the networks of realistic sizes (tens of thousands of participants)
Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)
This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023
Learning with Exposure Constraints in Recommendation Systems
Recommendation systems are dynamic economic systems that balance the needs of
multiple stakeholders. A recent line of work studies incentives from the
content providers' point of view. Content providers, e.g., vloggers and
bloggers, contribute fresh content and rely on user engagement to create
revenue and finance their operations. In this work, we propose a contextual
multi-armed bandit setting to model the dependency of content providers on
exposure. In our model, the system receives a user context in every round and
has to select one of the arms. Every arm is a content provider who must receive
a minimum number of pulls every fixed time period (e.g., a month) to remain
viable in later rounds; otherwise, the arm departs and is no longer available.
The system aims to maximize the users' (content consumers) welfare. To that
end, it should learn which arms are vital and ensure they remain viable by
subsidizing arm pulls if needed. We develop algorithms with sub-linear regret,
as well as a lower bound that demonstrates that our algorithms are optimal up
to logarithmic factors.Comment: Published in The Web Conference 2023 (WWW 23
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