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Found 10 papers, 3 papers with code
A note on generalization bounds for losses with finite moments
Moreover, the paper derives a high-probability PAC-Bayes bound for losses with a bounded variance.
More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime-validity
Firstly, for losses with a bounded range, we recover a strengthened version of Catoni's bound that holds uniformly for all parameter values.
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization
To date, no "information-theoretic" frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic convex optimization.
Quadratic Signaling Games with Channel Combining Ratio
Regarding the Nash equilibrium, we explicitly characterize affine equilibria for the single-stage setup and show that the optimal encoder (resp.
Optimization and Control Information Theory Information Theory
Tighter expected generalization error bounds via Wasserstein distance
This work presents several expected generalization error bounds based on the Wasserstein distance.
On Random Subset Generalization Error Bounds and the Stochastic Gradient Langevin Dynamics Algorithm
In this work, we unify several expected generalization error bounds based on random subsets using the framework developed by Hellstr\"om and Durisi [1].
Region-based Energy Neural Network for Approximate Inference
We term our model Region-based Energy Neural Network (RENN).
A Variational Approach to Privacy and Fairness
In this article, we propose a new variational approach to learn private and/or fair representations.
Worst-Case Detection Performance for Distributed SIMO Physical Layer Authentication
However, with PLA-aware attack strategies, an attacker can maximize the probability of successfully impersonating the legitimate devices.
The Convex Information Bottleneck Lagrangian
In this paper, we (i) present a general family of Lagrangians which allow for the exploration of the IB curve in all scenarios; (ii) provide the exact one-to-one mapping between the Lagrange multiplier and the desired compression rate $r$ for known IB curve shapes; and (iii) show we can approximately obtain a specific compression level with the convex IB Lagrangian for both known and unknown IB curve shapes.
