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Found 17 papers, 2 papers with code
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes
For example, when the total number of features in the source task's learning model is fixed, we show that it is more advantageous to allocate a greater number of redundant features to the task-specific part rather than the common part.
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning
Fairness plays a crucial role in various multi-agent systems (e. g., communication networks, financial markets, etc.).
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning
However, due to the uniqueness of meta-learning such as task-specific gradient descent inner training and the diversity/fluctuation of the ground-truth signals among training tasks, we find new and interesting properties that do not exist in single-task linear regression.
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization
Decentralized bilevel optimization has received increasing attention recently due to its foundational role in many emerging multi-agent learning paradigms (e. g., multi-agent meta-learning and multi-agent reinforcement learning) over peer-to-peer edge networks.
On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model
Our upper bound reveals that, between the two hidden-layers, the test error descends faster with respect to the number of neurons in the second hidden-layer (the one closer to the output) than with respect to that in the first hidden-layer (the one closer to the input).
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models
Specifically, for a class of learnable functions, we provide a new upper bound of the generalization error that approaches a small limiting value, even when the number of neurons $p$ approaches infinity.
WLAN-Log-Based Superspreader Detection in the COVID-19 Pandemic
Further, the evaluation shows no consistent differences among three vertex centrality measures for long-term (i. e., weekly) contact graphs, which necessitates the inclusion of SEIR simulation in our framework.
Social and Information Networks Computers and Society
Low-Power Status Updates via Sleep-Wake Scheduling
We devise a low-complexity solution to solve this problem and prove that, for practical sensing times that are short, the solution is within a small gap from the optimum AoI performance.
Information Theory Information Theory
Incentive Design and Profit Sharing in Multi-modal Transportation Network
Accordingly, in this paper, we use cooperative game theory coupled with the hyperpath-based stochastic user equilibrium framework to study such a market.
A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19
In this paper, we show that the testing problem can be cast as a sequential learning-based resource allocation problem with constraints, where the input to the problem is provided by a time-varying social contact graph obtained through various contact tracing tools.
The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons
From a given set of items, the learner can make pairwise comparisons on every pair of items, and each comparison returns an independent noisy result about the preferred item.
Multi-Armed Bandits with Local Differential Privacy
To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee.
Learning in Markov Decision Processes under Constraints
In order to measure the performance of a reinforcement learning algorithm that satisfies the average cost constraints, we define an $M+1$ dimensional regret vector that is composed of its reward regret, and $M$ cost regrets.
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons
This paper studies the problem of finding the exact ranking from noisy comparisons.
PAC Ranking from Pairwise and Listwise Queries: Lower Bounds and Upper Bounds
For the PAC top-$k$ ranking problem, we derive a lower bound on the sample complexity (aka number of queries), and propose an algorithm that is sample-complexity-optimal up to an $O(\log(k+l)/\log{k})$ factor.
Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
We study the stochastic multi-armed bandit (MAB) problem in the presence of side-observations across actions that occur as a result of an underlying network structure.
When to Reset Your Keys: Optimal Timing of Security Updates via Learning
As these attacks are often designed to disable a system (or a critical resource, e. g., a user account) repeatedly, it is crucial for the defender to keep updating its security measures to strike a balance between the risk of being compromised and the cost of security updates.
