We study a distributionally robust mean square error estimation problem over a nonconvexWasserstein ambiguity set containing only normal distributions.

To this end, we propose DrugRec, a causal inference baseddrugrecommendationmodel.

After prediction, the true labelyt is revealed and the lossℓ(yt,ˆyt)is incurred.

Forclassification tasks, asmentioned inboth ofthe experiments, we could easily adopt a human-machine collaboration framework: since our model is capable of conveying the prediction confidence along with the prediction itself, we could pass thecases where themodel islessassertivetohumans forfurther evaluation. Thistraitisespecially valuable for classification tasks with exceptionally imbalanced data,e.g., fraud detection, and ad click-through rate prediction, where the volume of one class could be orders of magnitude more than the other.

These additive-noise models areprimarily focusing onaccurately estimating theconditional mean E[y|x], while paying less attention to whether the noise distribution can accurately capture the uncertainty ofy given x. For this reason, they may not work well if the distribution ofy given x clearly deviates from the additive-noise assumption. For example, ifp(y|x) is multi-modal, which commonly happens when there are missing categorical covariates inx, then E[y|x] may not be close to any possible true values ofy given that specificx.

Theyaredesigned tohaveagame-theoretic equilibrium where everyone reveals their private information truthfully.

Class incremental learning (CIL) problem, in which a learner should incrementally learn newly arriving class objects while not "catastrophically" forgetting the past learned classes, isone ofthe fundamental, yet still open, problems in machine learning.

We measure the ability of human observers to predict the next datum in a sequence that is generated by a simple statistical process undergoing change at random points in time. Accurate performance in this task requires the identification of changepoints. We assess individual differences between observers both empirically, and using two kinds of models: a Bayesian approach for change detection and a family of cognitively plausible fast and frugal models. Some individuals detect too many changes and hence perform sub-optimally due to excess variability. Other individuals do not detect enough changes, and perform sub-optimally because they fail to notice short-term temporal trends.

Making decisions in complex environments is a key challenge in artificial intelligence (AI). Situations involving multiple decision makers are particularly complex, leading to computation intractability of principled solution methods. A body of work in AI [4, 3, 41, 45, 47, 2] has tried to mitigate this problem by trying to bring down interaction to its core: how does the policy of one agent influence another agent? If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than that of policies [45]. However, so far these notions of influence have been restricted in their applicability to special cases of interaction. In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs) [33]. This generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex settings.

Surprise-based learning allows agents to adapt quickly in non-stationary stochastic environments. Most existing approaches to surprise-based learning and change point detection assume either implicitly or explicitly a simple, hierarchical generative model of observation sequences that are characterized by stationary periods separated by sudden changes. In this work we show that exact Bayesian inference gives naturally rise to a surprise-modulated trade-off between forgetting and integrating the new observations with the current belief. We demonstrate that many existing approximate Bayesian approaches also show surprise-based modulation of learning rates, and we derive novel particle filters and variational filters with update rules that exhibit surprise-based modulation. Our derived filters have a constant scaling in observation sequence length and particularly simple update dynamics for any distribution in the exponential family. Empirical results show that these filters estimate parameters better than alternative approximate approaches and reach comparative levels of performance to computationally more expensive algorithms. The theoretical insight of casting various approaches under the same interpretation of surprise-based learning, as well as the proposed filters, may find useful applications in reinforcement learning in non-stationary environments and in the analysis of animal and human behavior.