285 research outputs found
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
Despite the basic premise that next-generation wireless networks (e.g., 6G)
will be artificial intelligence (AI)-native, to date, most existing efforts
remain either qualitative or incremental extensions to existing ``AI for
wireless'' paradigms. Indeed, creating AI-native wireless networks faces
significant technical challenges due to the limitations of data-driven,
training-intensive AI. These limitations include the black-box nature of the AI
models, their curve-fitting nature, which can limit their ability to reason and
adapt, their reliance on large amounts of training data, and the energy
inefficiency of large neural networks. In response to these limitations, this
article presents a comprehensive, forward-looking vision that addresses these
shortcomings by introducing a novel framework for building AI-native wireless
networks; grounded in the emerging field of causal reasoning. Causal reasoning,
founded on causal discovery, causal representation learning, and causal
inference, can help build explainable, reasoning-aware, and sustainable
wireless networks. Towards fulfilling this vision, we first highlight several
wireless networking challenges that can be addressed by causal discovery and
representation, including ultra-reliable beamforming for terahertz (THz)
systems, near-accurate physical twin modeling for digital twins, training data
augmentation, and semantic communication. We showcase how incorporating causal
discovery can assist in achieving dynamic adaptability, resilience, and
cognition in addressing these challenges. Furthermore, we outline potential
frameworks that leverage causal inference to achieve the overarching objectives
of future-generation networks, including intent management, dynamic
adaptability, human-level cognition, reasoning, and the critical element of
time sensitivity
Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework
Deep neural networks (DNNs) are emerging as a potential solution to solve
NP-hard wireless resource allocation problems. However, in the presence of
intricate constraints, e.g., users' quality-of-service (QoS) constraints,
guaranteeing constraint satisfaction becomes a fundamental challenge. In this
paper, we propose a novel unsupervised learning framework to solve the
classical power control problem in a multi-user interference channel, where the
objective is to maximize the network sumrate under users' minimum data rate or
QoS requirements and power budget constraints. Utilizing a differentiable
projection function, two novel deep learning (DL) solutions are pursued. The
first is called Deep Implicit Projection Network (DIPNet), and the second is
called Deep Explicit Projection Network (DEPNet). DIPNet utilizes a
differentiable convex optimization layer to implicitly define a projection
function. On the other hand, DEPNet uses an explicitly-defined projection
function, which has an iterative nature and relies on a differentiable
correction process. DIPNet requires convex constraints; whereas, the DEPNet
does not require convexity and has a reduced computational complexity. To
enhance the sum-rate performance of the proposed models even further,
Frank-Wolfe algorithm (FW) has been applied to the output of the proposed
models. Extensive simulations depict that the proposed DNN solutions not only
improve the achievable data rate but also achieve zero constraint violation
probability, compared to the existing DNNs. The proposed solutions outperform
the classic optimization methods in terms of computation time complexity.Comment: accepted in IEEE Transactions on Communication
Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling
We evaluate the impact of probabilistically-constructed digital identity data
collected from Sep. to Dec. 2017 (approx.), in the context of
Lookalike-targeted campaigns. The backbone of this study is a large set of
probabilistically-constructed "identities", represented as small bags of
cookies and mobile ad identifiers with associated metadata, that are likely all
owned by the same underlying user. The identity data allows to generate
"identity-based", rather than "identifier-based", user models, giving a fuller
picture of the interests of the users underlying the identifiers. We employ
off-policy techniques to evaluate the potential of identity-powered lookalike
models without incurring the risk of allowing untested models to direct large
amounts of ad spend or the large cost of performing A/B tests. We add to
historical work on off-policy evaluation by noting a significant type of
"finite-sample bias" that occurs for studies combining modestly-sized datasets
and evaluation metrics involving rare events (e.g., conversions). We illustrate
this bias using a simulation study that later informs the handling of inverse
propensity weights in our analyses on real data. We demonstrate significant
lift in identity-powered lookalikes versus an identity-ignorant baseline: on
average ~70% lift in conversion rate. This rises to factors of ~(4-32)x for
identifiers having little data themselves, but that can be inferred to belong
to users with substantial data to aggregate across identifiers. This implies
that identity-powered user modeling is especially important in the context of
identifiers having very short lifespans (i.e., frequently churned cookies). Our
work motivates and informs the use of probabilistically-constructed identities
in marketing. It also deepens the canon of examples in which off-policy
learning has been employed to evaluate the complex systems of the internet
economy.Comment: Accepted by WSDM 201
Learning Decentralized Wireless Resource Allocations with Graph Neural Networks
We consider the broad class of decentralized optimal resource allocation
problems in wireless networks, which can be formulated as a constrained
statistical learning problems with a localized information structure. We
develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process
a sequence of delayed and potentially asynchronous graph aggregated state
information obtained locally at each transmitter from multi-hop neighbors. We
further utilize model-free primal-dual learning methods to optimize performance
subject to constraints in the presence of delay and asynchrony inherent to
decentralized networks. We demonstrate a permutation equivariance property of
the resulting resource allocation policy that can be shown to facilitate
transference to dynamic network configurations. The proposed framework is
validated with numerical simulations that exhibit superior performance to
baseline strategies.Comment: 13 pages, 13 figure
Intelligent Computing: The Latest Advances, Challenges and Future
Computing is a critical driving force in the development of human
civilization. In recent years, we have witnessed the emergence of intelligent
computing, a new computing paradigm that is reshaping traditional computing and
promoting digital revolution in the era of big data, artificial intelligence
and internet-of-things with new computing theories, architectures, methods,
systems, and applications. Intelligent computing has greatly broadened the
scope of computing, extending it from traditional computing on data to
increasingly diverse computing paradigms such as perceptual intelligence,
cognitive intelligence, autonomous intelligence, and human-computer fusion
intelligence. Intelligence and computing have undergone paths of different
evolution and development for a long time but have become increasingly
intertwined in recent years: intelligent computing is not only
intelligence-oriented but also intelligence-driven. Such cross-fertilization
has prompted the emergence and rapid advancement of intelligent computing.
Intelligent computing is still in its infancy and an abundance of innovations
in the theories, systems, and applications of intelligent computing are
expected to occur soon. We present the first comprehensive survey of literature
on intelligent computing, covering its theory fundamentals, the technological
fusion of intelligence and computing, important applications, challenges, and
future perspectives. We believe that this survey is highly timely and will
provide a comprehensive reference and cast valuable insights into intelligent
computing for academic and industrial researchers and practitioners
Teacher-Student Architecture for Knowledge Distillation: A Survey
Although Deep neural networks (DNNs) have shown a strong capacity to solve
large-scale problems in many areas, such DNNs are hard to be deployed in
real-world systems due to their voluminous parameters. To tackle this issue,
Teacher-Student architectures were proposed, where simple student networks with
a few parameters can achieve comparable performance to deep teacher networks
with many parameters. Recently, Teacher-Student architectures have been
effectively and widely embraced on various knowledge distillation (KD)
objectives, including knowledge compression, knowledge expansion, knowledge
adaptation, and knowledge enhancement. With the help of Teacher-Student
architectures, current studies are able to achieve multiple distillation
objectives through lightweight and generalized student networks. Different from
existing KD surveys that primarily focus on knowledge compression, this survey
first explores Teacher-Student architectures across multiple distillation
objectives. This survey presents an introduction to various knowledge
representations and their corresponding optimization objectives. Additionally,
we provide a systematic overview of Teacher-Student architectures with
representative learning algorithms and effective distillation schemes. This
survey also summarizes recent applications of Teacher-Student architectures
across multiple purposes, including classification, recognition, generation,
ranking, and regression. Lastly, potential research directions in KD are
investigated, focusing on architecture design, knowledge quality, and
theoretical studies of regression-based learning, respectively. Through this
comprehensive survey, industry practitioners and the academic community can
gain valuable insights and guidelines for effectively designing, learning, and
applying Teacher-Student architectures on various distillation objectives.Comment: 20 pages. arXiv admin note: substantial text overlap with
arXiv:2210.1733
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation
Deep reinforcement learning (DRL) has been proven its efficiency in capturing
users' dynamic interests in recent literature. However, training a DRL agent is
challenging, because of the sparse environment in recommender systems (RS), DRL
agents could spend times either exploring informative user-item interaction
trajectories or using existing trajectories for policy learning. It is also
known as the exploration and exploitation trade-off which affects the
recommendation performance significantly when the environment is sparse. It is
more challenging to balance the exploration and exploitation in DRL RS where RS
agent need to deeply explore the informative trajectories and exploit them
efficiently in the context of recommender systems. As a step to address this
issue, We design a novel intrinsically ,otivated reinforcement learning method
to increase the capability of exploring informative interaction trajectories in
the sparse environment, which are further enriched via a counterfactual
augmentation strategy for more efficient exploitation. The extensive
experiments on six offline datasets and three online simulation platforms
demonstrate the superiority of our model to a set of existing state-of-the-art
methods
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