research
∙
09/20/2023
Statistical Complexity of Quantum Learning
Recent years have seen significant activity on the problem of using data...
share
research
∙
01/16/2023
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management
Optimal resource allocation in modern communication networks calls for t...
research
∙
09/23/2022
Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis
Variational quantum algorithms (VQAs) offer the most promising path to o...
research
∙
01/17/2022
Transfer Learning in Quantum Parametric Classifiers: An Information-Theoretic Generalization Analysis
A key step in quantum machine learning with classical inputs is the desi...
research
∙
10/11/2021
An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints
In vertical federated learning (FL), the features of a data sample are d...
research
∙
06/20/2021
Transfer Bayesian Meta-learning via Weighted Free Energy Minimization
Meta-learning optimizes the hyperparameters of a training procedure, suc...
research
∙
06/01/2021
A unified PAC-Bayesian framework for machine unlearning via information risk minimization
Machine unlearning refers to mechanisms that can remove the influence of...
research
∙
06/01/2021
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning
The overall predictive uncertainty of a trained predictor can be decompo...
research
∙
01/21/2021
An Information-Theoretic Analysis of the Impact of Task Similarity on Meta-Learning
Meta-learning aims at optimizing the hyperparameters of a model class or...
research
∙
11/25/2020
Free Energy Minimization: A Unified Framework for Modelling, Inference, Learning,and Optimization
The goal of these lecture notes is to review the problem of free energy ...
research
∙
11/04/2020
Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization
Meta-learning automatically infers an inductive bias by observing data f...
research
∙
10/21/2020
Conditional Mutual Information Bound for Meta Generalization Gap
Meta-learning infers an inductive bias—typically in the form of the hype...
research
∙
10/13/2020
Information-Theoretic Bounds on Transfer Generalization Gap Based on Jensen-Shannon Divergence
In transfer learning, training and testing data sets are drawn from diff...
research
∙
05/09/2020
Information-Theoretic Generalization Bounds for Meta-Learning and Applications
Meta-learning, or "learning to learn", refers to techniques that infer a...
research
∙
01/08/2020
Address-Event Variable-Length Compression for Time-Encoded Data
Time-encoded signals, such as social network update logs and spiking tra...
research
∙
11/18/2018
Shannon meets von Neumann: A Minimax Theorem for Channel Coding in the Presence of a Jammer
We study the setting of channel coding over a family of channels whose s...
research
∙
01/26/2018
