Advanced techniques in differential privacy for machine learning include sublinear composition theorems, privacy accountants, relaxations of differential privacy, and specialised mechanism designs. Sublinear composition theorems help manage privacy loss more efficiently in iterative processes like training neural networks, allowing for longer training without excessive privacy expenditure. Privacy accountants track the cumulative privacy loss during training, providing accurate measures of privacy consumption. Relaxations of differential privacy offer more practical yet still robust privacy models. Specialised mechanism designs tailor privacy-preserving techniques to specific applications, optimising the trade-off between privacy protection and model utility. These advanced techniques are crucial for effectively implementing differential privacy in complex machine learning tasks.