Learning theory and algorithms

On the Fairness of Multitask Representation Learning

Boosting Semi-Supervised Federated Learning with Model Personalization and Client-Variance-Reduction

The Uniqueness Problem of Physical Law Learning

TENSOR COMPLETION FOR EFFICIENT AND ACCURATE HYPERPARAMETER OPTIMISATION IN LARGE-SCALE STATISTICAL LEARNING

Pseudo-Inverted Bottleneck Convolution for DARTS Search Space

ASYMMETRIC POLYNOMIAL LOSS FOR MULTI-LABEL CLASSIFICATION

An Approach to Ontological Learning from Weak Labels

Improving the Stochastic Gradient Descent's test accuracy by manipulating the l_\infty norm of its gradient approximation

Neural networks with quantization constraints.

Training Graph Neural Networks on Growing Stochastic Graphs

Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks

Multi-task Bias-Variance Trade-off Through Functional Constraints