Meta-Learning is an emerging field where the main challenge is to develop models capable of distilling previous experiences to efficiently learn new tasks. Curriculum Learning, a group of optimization strategies, structures data in a meaningful order which aids learning. However, the extent to which curriculum strategies can optimize the performance of meta-learners remains unclear. Here we study the separate and joint effects of a model-based (ScreenerNet) and a statistics-based (Active Bias) curriculum strategy on the training of a meta-learning model (Neural Processes) which solves 1-D function regression tasks. The findings show that ScreenerNet increases in-task accuracy and accelerates convergence, but decreases the generalization performance. Active Bias achieves mixed generalization results and significantly decreases training efficiency when trained on noisy data-sets. Combining them partially mitigates ScreenerNet's overfitting and stabilizes Active Bias' susceptibility to noise, but further research is necessary in order to achieve consistent improvements to the baseline.

Meta-learning is an important emerging paradigm in machine learning, aimed at improving data-efficiency and generalization performance across learning tasks. Challenges caused by noisy data has been extensively researched in traditional learning settings. However, its impact in the context of meta-learning, especially concerning label noise in meta-training, remains under-explored. Curriculum Learning (CL), is an approach where training data is ordered from easy to complex, and models learn from easier to harder samples. A type of CL , Self-Paced learning (SPL) offers adaptive data curriculum, where ordering is based on per sample model performance during training. Self-Paced Learning (SPL) has proven effective in enhancing model robustness and convergence under noisy data scenarios. However, its application in meta-learning under these conditions remains limited. In this paper, we use a Neural Process model on 1D sinusoidal function regression tasks, with different ratio of clean / noisy training data scenarios to empirically observe the same benefits SPL can potentially offer for noisy meta-learning. In line with findings in traditional learning settings, SPL improved overall training convergence, also lead to increase in generalization thus noise robustness. Furthermore SPL lead the model to be more robust to increasing scale of noise for tasks within the training data distribution.

We investigate whether a teacher-student curriculum learning approach using a teacher network with a simpler structure than the student network can achieve better results at meta-learning. The goal of meta-learning is to learn from a set of tasks, and then perform well on a new, structurally similar but unseen task with minimal retraining. Instead of sampling uniformly from all data to create the training batches, the curriculum-learning approach aims to create a sequence of mini-batches that enhances the training process, also known as a curriculum. During teacher-student curriculum learning a "teacher" network is trained in the standard manner, and then its outputs are used to order the training samples by difficulty and categorise them into mini-batches. This curriculum is then used to train the "student" network. Previous teacher-student models either had pre-trained more complex teachers, or teachers with the same structure as the student network. We investigate whether a teacher network with a simpler structure can also increase accuracy, while preserving computational resources. We find that using such a curriculum worsens performance compared to not using any curriculum at all.

This paper explores the application of evolutionary algorithms to enhance task generation for Neural Processes (NPs) in meta-learning. Meta-learning aims to develop models capable of rapid adaptation to new tasks with minimal data, a necessity in fields where data collection is costly or difficult. By integrating evolutionary strategies, we aim to enhance the efficiency and robustness of NPs. We evaluate our approach using 1-D function regression problems, where Genetic Algorithm generates diverse and challenging tasks. Our results show that the evolutionary approach improves learning efficiency and model performance, achieving lower Root Mean Squared Error (RMSE) compared to traditional methods.

Bayesian Optimization (BO) has demonstrated significant utility across numerous applications. However, due to it being designed as a universal optimizer, its performance can often be suboptimal in specialized environments. To overcome this issue, research has been conducted into the application of transfer learning for enhancing BO performance in these specialized contexts. This paper describes the research done into evaluating the MetaBO algorithm in some specific environments. MetaBO innovates by substituting the acquisition function component in BO with a neural network that serves as an acquisition function, trained via a reinforcement learning framework. Although the results indicate that the algorithm's performance is not optimal in the environments tested, these limitations are ascribed to elements of the implementation rather than the concept of the algorithm itself. Consequently, further research is necessary to refine the implementation process and fully exploit the potential of the MetaBO algorithm.

Bayesian optimisation is a rapidly growing area of research that aims to identify the optimum of the black-box function, as it strategically directs the optimisation process towards promising regions. This paper provides an overview of the theoretical background used by the Entropy Search algorithms under study, mainly Predictive Entropy Search, Max-Value Entropy Search, and Joint Entropy Search. Furthermore, we empirically analyse the performance and sensitivity of the algorithms in different environment settings. In particular, we discuss the impact of function shape, batch size, noise level, and the number of input dimensions on the final simple regret metric. The results show the weak spots of the information-theoretic methods. However, the algorithms perform better for batch optimisation, demonstrating the advantage when considering the information on the maximum function value.

Scientific problems are often concerned with optimization of control variables of complex systems, for instance hyperparameters of machine learning models. A popular solution for such intractable environments is Bayesian optimization. However, many implementations disregard dynamic evaluation costs associated with the optimization procedure. Furthermore, another common trope among
Bayesian algorithms is that they are short-sighted and do not consider long-term effects of their actions. This paper investigates the viability of multitimestep cost-aware Bayesian optimizers and evaluates their performance in environments with delayed rewards. To this end, we combine existing works on parallel Bayesian optimizers and costaware heuristics. Our findings reveal that although
such parallel optimizers yield more optimal results and are more resistant to delayed feedback compared to their myopic counterparts, they are unable to achieve cost-awareness.

Language is an intuitive and effective way for humans to communicate. Large Language Models (LLMs) can interpret and respond well to language. However, their use in deep reinforcement learning is limited as they are sample inefficient. State-of-the-art deep reinforcement learning algorithms are more sample efficient but cannot understand language well. This research aims to study whether RL agents can improve learning by utilizing language assistance and how LLMs can help them. A sentence describing the agent's environment is fed into an LLM to create a semantic embedding, which is consumed by a recurrent Soft Actor-Critic (SAC) agent to create an agent that can listen to natural language. This research shows that the best method for the agent to consume the embedding is concatenating it to each observation. Also, LLM-based embeddings lead to faster and more stable learning than non-LLM-based embeddings. The agent is sensitive to noise in the embedding but not to the embedding's dimensionality. The agent can generalize well across sentences that have a similar meaning to sentences seen during training but are formulated differently, but it can not generalize as well across sentences with unknown subjects and needs the subjects of the sentences to be grounded in training. Lastly, this research shows that the proposed architecture supports scaling language assistance to more complex environments.