A critical yet often overlooked aspect in online continual learning is the label delay, where new data may not be labeled due to slow and costly annotation processes. We introduce a new continual learning framework with explicit modeling of the label delay between data and label streams over time steps. In each step, the framework reveals both unlabeled data from the current time step t and labels delayed with d steps, from the time step t d. In our extensive experiments amounting to 25000 GPU hours, we show that merely increasing the computational resources is insufficient to tackle this challenge. Our findings highlight significant performance declines when solely relying on labeled data when the label delay becomes significant. More surprisingly, state-of-the-art Self-Supervised Learning and Test-Time Adaptation techniques that utilize the newer, unlabeled data, fail to surpass the performance of a naïve method that simply trains on the delayed supervised stream. To this end, we propose a simple, robust method, called Importance Weighted Memory Sampling that can effectively bridge the accuracy gap caused by label delay by prioritising memory samples that resemble the most to the newest unlabeled samples. We show experimentally that our method is the least affected by the label delay factor, and successfully recovers the accuracy of the non-delayed counterpart.

Existing online learning algorithms for adversarial Markov Decision Processes achieve O( T) regret after T rounds of interactions even if the loss functions are chosen arbitrarily by an adversary, with the caveat that the transition function has to be fixed. This is because it has been shown that adversarial transition functions make no-regret learning impossible. Despite such impossibility results, in this work, we develop algorithms that can handle both adversarial losses and adversarial transitions, with regret increasing smoothly in the degree of maliciousness of the adversary.

Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's output. Without proper modeling, such incomplete supervision inevitably misleads model estimation, especially in a bandit learning setting where the feedback is acquired on the fly. In this work, we perform contextual bandit learning with implicit feedback by modeling the feedback as a composition of user result examination and relevance judgment. Since users' examination behavior is unobserved, we introduce latent variables to model it. We perform Thompson sampling on top of variational Bayesian inference for arm selection and model update. Our upper regret bound analysis of the proposed algorithm proves its feasibility of learning from implicit feedback in a bandit setting; and extensive empirical evaluations on click logs collected from a major MOOC platform further demonstrate its learning effectiveness in practice.

Neural Information Processing Systems http://nips.cc/

Figure 1: Illustration of angularly distributed features on 2-D space.

Such feedback can be stochastic, e.g. the

We study the following question: given an efficient approximation algorithm for an optimization problem, can we learn efficiently in the same setting? We give a formal affirmative answer to this question in the form of a reduction from online learning to offline approximate optimization using an efficient algorithm that guarantees near optimal regret. The algorithm is efficient in terms of the number of oracle calls to a given approximation oracle - it makes only logarithmically many such calls per iteration.

Consider a player that in each of T rounds chooses one of K arms.

Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which marginal inference is always tractable. These properties follow from the conditions of completeness and decomposability, which must be respected by the structure of the network. As a result, it is not easy to specify a valid sum-product network by hand and therefore structure learning techniques are typically used in practice. This paper describes a new online structure learning technique for feed-forward and recurrent SPNs. The algorithm is demonstrated on real-world datasets with continuous features and sequence datasets of varying length for which the best network architecture is not obvious.