Search Results for author:
Found 25 papers, 4 papers with code
SplitEE: Early Exit in Deep Neural Networks with Split Computing
To overcome the issue, various approaches are considered, like offloading part of the computation to the cloud for final inference (split computing) or performing the inference at an intermediary layer without passing through all layers (early exits).
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Online Universal Dirichlet Factor Portfolios
We revisit the online portfolio allocation problem and propose universal portfolios that use factor weighing to produce portfolios that out-perform uniform dirichlet allocation schemes.
UB3: Best Beam Identification in Millimeter Wave Systems via Pure Exploration Unimodal Bandits
In this paper, we develop an algorithm that exploits the unimodal structure of the received signal strengths of the beams to identify the best beam in a finite time using pure exploration strategies.
Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers
Holographic metasurface transceivers (HMT) is an emerging technology for enhancing the coverage and rate of wireless communication systems.
Unsupervised Early Exit in DNNs with Multiple Exits
Hence one is faced with the problem of selecting the optimal exit in an unsupervised setting.
Exploiting Side Information for Improved Online Learning Algorithms in Wireless Networks
In this work, we treat any such measurable quality that has a non-zero correlation with the rate achieved as side-information and study how it can be exploited to quickly learn the channel that offers higher throughput (reward).
Continuous Time Bandits With Sampling Costs
CTMAB is fundamentally different than the usual multi-arm bandit problem (MAB), e. g., even the single-arm case is non-trivial in CTMAB, since the optimal sampling frequency depends on the mean of the arm, which needs to be estimated.
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach
Critical role of Internet of Things (IoT) in various domains like smart city, healthcare, supply chain and transportation has made them the target of malicious attacks.
Stochastic Multi-Armed Bandits with Control Variates
This paper studies a new variant of the stochastic multi-armed bandits problem where auxiliary information about the arm rewards is available in the form of control variates.
Censored Semi-Bandits for Resource Allocation
The loss depends on two hidden parameters, one specific to the arm but independent of the resource allocation, and the other depends on the allocated resource.
Masking Host Identity on Internet: Encrypted TLS/SSL Handshake
As the widespread adoption of HTTPS protocol has made it difficult to classify the traffic looking into the content field, one of the fields the middle-boxes look for is Server Name Indicator (SNI), which goes in plain text.
Cryptography and Security Networking and Internet Architecture
Learning to Optimize Energy Efficiency in Energy Harvesting Wireless Sensor Networks
We study wireless power transmission by an energy source to multiple energy harvesting nodes with the aim to maximize the energy efficiency.
Online Algorithm for Unsupervised Sequential Selection with Contextual Information
In this paper, we study Contextual Unsupervised Sequential Selection (USS), a new variant of the stochastic contextual bandits problem where the loss of an arm cannot be inferred from the observed feedback.
Thompson Sampling for Unsupervised Sequential Selection
The total loss is the sum of the cost incurred for selecting the arm and the stochastic loss associated with the selected arm.
Stochastic Network Utility Maximization with Unknown Utilities: Multi-Armed Bandits Approach
We model this problem setting as a bandit setting where feedback obtained in each round depends on the resource allocated to the agents.
Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach
It is thus important to learn the least amount of energy harvested by nodes so that the source can transmit on a frequency band that maximizes this amount.
Distributed Learning in Ad-Hoc Networks: A Multi-player Multi-armed Bandit Framework
In this paper, we discuss state-of-the-art multi-armed multi-player bandit based distributed learning algorithms that allow users to adapt to the environment and coordinate with other players/users.
Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis
In medical diagnosis, physicians predict the state of a patient by checking measurements (features) obtained from a sequence of tests, e. g., blood test, urine test, followed by invasive tests.
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
We study this novel setting by establishing its `equivalence' to Multiple-Play Multi-Armed Bandits(MP-MAB) and Combinatorial Semi-Bandits.
Online Algorithm for Unsupervised Sensor Selection
We set up the USS problem as a stochastic partial monitoring problem and develop an algorithm with sub-linear regret under the WD property.
Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous Networks
However, such transmissions and sensing for information exchange do not add to network throughput.
Multi-player Multi-armed Bandits for Stable Allocation in Heterogeneous Ad-Hoc Networks
Next generation networks are expected to be ultradense and aim to explore spectrum sharing paradigm that allows users to communicate in licensed, shared as well as unlicensed spectrum.
Multi-Player Bandits: A Trekking Approach
We provide algorithms based on a novel `trekking approach' that guarantees constant regret for the static case and sub-linear regret for the dynamic case with high probability.
Learning Policies for Markov Decision Processes from Data
We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs.
Algorithms for Linear Bandits on Polyhedral Sets
We then provide a nearly optimal algorithm and show that its expected regret scales as $O(N\log^{1+\epsilon}(T))$ for an arbitrary small $\epsilon >0$.
