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Found 11 papers, 0 papers with code
Equivalence of the Empirical Risk Minimization to Regularization on the Family of f-Divergences
The solution to empirical risk minimization with $f$-divergence regularization (ERM-$f$DR) is presented under mild conditions on $f$.
Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure
Fundamental generalization metrics, such as the sensitivity of the expected loss, the sensitivity of the empirical risk, and the generalization gap are shown to have closed-form expressions involving the worst-case data-generating probability measure.
On the Validation of Gibbs Algorithms: Training Datasets, Test Datasets and their Aggregation
The dependence on training data of the Gibbs algorithm (GA) is analytically characterized.
Analysis of the Relative Entropy Asymmetry in the Regularization of Empirical Risk Minimization
The analysis of the solution unveils the following properties of relative entropy when it acts as a regularizer in the ERM-RER problem: i) relative entropy forces the support of the Type-II solution to collapse into the support of the reference measure, which introduces a strong inductive bias that dominates the evidence provided by the training data; ii) Type-II regularization is equivalent to classical relative entropy regularization with an appropriate transformation of the empirical risk function.
Empirical Risk Minimization with Relative Entropy Regularization
Among these properties, the solution to this problem, if it exists, is shown to be a unique probability measure, mutually absolutely continuous with the reference measure.
An information theoretic vulnerability metric for data integrity attacks on smart grids
A novel metric that describes the vulnerability of the measurements in power systems to data integrity attacks is proposed.
$2 \times 2$ Zero-Sum Games with Commitments and Noisy Observations
In this paper, $2\times2$ zero-sum games are studied under the following assumptions: $(1)$ One of the players (the leader) commits to choose its actions by sampling a given probability measure (strategy); $(2)$ The leader announces its action, which is observed by its opponent (the follower) through a binary channel; and $(3)$ the follower chooses its strategy based on the knowledge of the leader's strategy and the noisy observation of the leader's action.
Power Injection Measurements are more Vulnerable to Data Integrity Attacks than Power Flow Measurements
The result of computing the VuIx of the measurements in the system yields an ordering of the measurements vulnerability based on the level of exposure to data integrity attacks.
Empirical Risk Minimization with Relative Entropy Regularization: Optimality and Sensitivity Analysis
The optimality and sensitivity of the empirical risk minimization problem with relative entropy regularization (ERM-RER) are investigated for the case in which the reference is a sigma-finite measure instead of a probability measure.
Stealth Data Injection Attacks with Sparsity Constraints
Sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed.
Information Theoretic Data Injection Attacks with Sparsity Constraints
The attack construction is formulated as an optimization problem that aims to minimize the mutual information between the state variables and the observations while guaranteeing the stealth of the attack.
