In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[EX Q]: A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch. In contrast, online (regret based) learning considers a sequential setting in which problems are revealed one after the other, but conventionally train only a single model without any task-specific adaptation. This work introduces an online meta-learning setting, which merges ideas from both the aforementioned paradigms to better capture the spirit and practice of continual lifelong learning. We propose the follow the meta leader (FTML) algorithm which extends the MAML algorithm to this setting. Theoretically, this work provides an O(logT) regret guarantee for the FTML algorithm. Our experimental evaluation on three different large-scale tasks suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches.
[EX A]: We introduce the online meta learning problem setting to better capture the spirit and practice of continual lifelong learning.

[EX Q]: This paper proposes a method for efficient training of Q-function for continuous-state Markov Decision Processes (MDP), such that the traces of the resulting policies satisfy a Linear Temporal Logic (LTL) property. LTL, a modal logic, can express a wide range of time-dependent logical properties including safety and liveness. We convert the LTL property into a limit deterministic Buchi automaton with which a synchronized product MDP is constructed. The control policy is then synthesised by a reinforcement learning algorithm assuming that no prior knowledge is available from the MDP. The proposed method is evaluated in a numerical study to test the quality of the generated control policy and is compared against conventional methods for policy synthesis such as MDP abstraction (Voronoi quantizer) and approximate dynamic programming (fitted value iteration).
[EX A]: As safety is becoming a critical notion in machine learning we believe that this work can act as a foundation for a number of research directions such as safety-aware learning algorithms.

[EX Q]: We study the problem of generating source code in a strongly typed,
Java-like programming language, given a label (for example a set of
API calls or types) carrying a small amount of information about the
code that is desired. The generated programs are expected to respect a
'"realistic" relationship between programs and labels, as exemplified
by a corpus of labeled programs available during training.

Two challenges in such *conditional program generation* are that
the generated programs must satisfy a rich set of syntactic and
semantic constraints, and that source code contains many low-level
features that impede learning. We address these problems by training
a neural generator not on code but on *program sketches*, or
models of program syntax that abstract out names and operations that
do not generalize across programs. During generation, we infer a
posterior distribution over sketches, then concretize samples from
this distribution into type-safe programs using combinatorial
techniques. We implement our ideas in a system for generating
API-heavy Java code, and show that it can often predict the entire
body of a method given just a few API calls or data types that appear
in the method.
[EX A]:
We give a method for generating type-safe programs in a Java-like language, given a small amount of syntactic information about the desired code.