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.

We introduce MTLAB, a new algorithm for learning multiple related tasks with strong theoretical guarantees. Its key idea is to perform learning sequentially over the data of all tasks, without interruptions or restarts at task boundaries. Predictors for individual tasks are derived from this process by an additional online-to-batch conversion step.

By learning across task boundaries, MTLAB achieves a sublinear regret of true risks in the number of tasks. In the lifelong learning setting, this leads to an improved generalization bound that converges with the total number of samples across all observed tasks, instead of the number of examples per tasks or the number of tasks independently. At the same time, it is widely applicable: it can handle finite sets of tasks, as common in multi-task learning, as well as stochastic task sequences, as studied in lifelong learning.
A new algorithm for online multi-task learning that learns without restarts at the task borders

Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. Such bonus is summed up with the real task reward - making it possible for RL algorithms to learn from the combined reward. We propose a new curiosity method which uses episodic memory to form the novelty bonus. To determine the bonus, the current observation is compared with the observations in memory. Crucially, the comparison is done based on how many environment steps it takes to reach the current observation from those in memory - which incorporates rich information about environment dynamics. This allows us to overcome the known "couch-potato" issues of prior work - when the agent finds a way to instantly gratify itself by exploiting actions which lead to hardly predictable consequences. We test our approach in visually rich 3D environments in ViZDoom, DMLab and MuJoCo. In navigational tasks from ViZDoom and DMLab, our agent outperforms the state-of-the-art curiosity method ICM. In MuJoCo, an ant equipped with our curiosity module learns locomotion out of the first-person-view curiosity only. The code is available at https://github.com/google-research/episodic-curiosity/.
We propose a novel model of curiosity based on episodic memory and the ideas of reachability which allows us to overcome the known "couch-potato" issues of prior work.

Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. To solve the problem, some regularization adversarial training methods, constraining the output label or logit, have been studied. In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention. Instead of constraining a hard distribution (e.g., one-hot vectors or logit) in adversarial training, ATLPA uses Tolerant Logit which consists of confidence distribution on top-k classes and captures inter-class similarities at the image level. Specifically, in addition to minimizing the empirical loss, ATLPA encourages attention map for pairs of examples to be similar. When applied to clean examples and their adversarial counterparts, ATLPA improves accuracy on adversarial examples over adversarial training. We evaluate ATLPA with the state of the art algorithms, the experiment results show that our method outperforms these baselines with higher accuracy. Compared with previous work, our work is evaluated under highly challenging PGD attack: the maximum perturbation $\epsilon$ is 64 and 128 with 10 to 200 attack iterations.
In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention.