Search Results for author:
Found 11 papers, 8 papers with code
On Symbol Error Probability-based Beamforming in MIMO Gaussian Wiretap Channels
This paper investigates beamforming schemes designed to minimize the symbol error probability (SEP) for an authorized user while guaranteeing that the likelihood of an eavesdropper correctly recovering symbols remains below a predefined threshold.
Hard-Negative Sampling for Contrastive Learning: Optimal Representation Geometry and Neural- vs Dimensional-Collapse
In contrast to existing literature, our theoretical results for SCL do not require class-conditional independence of augmented views and work for a general loss function class that includes the widely used InfoNCE loss function.
A principled approach to model validation in domain generalization
Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions.
Trade-off between reconstruction loss and feature alignment for domain generalization
To deal with challenging settings in DG where both data and label of the unseen domain are not available at training time, the most common approach is to design the classifiers based on the domain-invariant representation features, i. e., the latent representations that are unchanged and transferable between domains.
Supervised Contrastive Learning with Hard Negative Samples
In absence of class information, in unsupervised CL (UCL), the negative samples are typically chosen randomly and independently of the anchor from a preset negative sampling distribution over the entire dataset.
Joint covariate-alignment and concept-alignment: a framework for domain generalization
Particularly, our framework proposes to jointly minimize both the covariate-shift as well as the concept-shift between the seen domains for a better performance on the unseen domain.
Conditional entropy minimization principle for learning domain invariant representation features
Invariance-principle-based methods such as Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG).
Barycentric-alignment and reconstruction loss minimization for domain generalization
To bridge this gap between theory and practice, we introduce a new upper bound that is free of terms having such dual dependence, resulting in a fully optimizable risk upper bound for the unseen domain.
On the Uniqueness of Binary Quantizers for Maximizing Mutual Information
Furthermore, we show that an optimal quantizer (possibly with multiple thresholds) is the one with the thresholding vector whose elements are all the solutions of r(y) = r* for some constant r* > 0.
Communication-Channel Optimized Partition
Given an original discrete source X with the distribution p_X that is corrupted by noise to produce the noisy data Y with the given joint distribution p(X, Y).
Minimizing Impurity Partition Under Constraints
In general, the problem of finding a partition that minimizes a given impurity (loss function) is NP-hard.
