We study a dynamic pricing problem for third-party platform service fees under strategic, far-sighted customers. In each time period, the platform sets a service fee based on historical data, observes the resulting transaction quantities, and collects revenue. The platform also monitors equilibrium prices influenced by both demand and supply. The objective is to maximize total revenue over a time horizon T. Our problem incorporates three practical challenges: (a) initially, the platform lacks knowledge of the demand side beforehand, necessitating a balance between exploring (learning the demand curve) and exploiting (maximizing revenue) simultaneously; (b) since only equilibrium prices and quantities are observable, traditional Ordinary Least Squares (OLS) estimators would be biased and inconsistent; (c) buyers are rational and strategic, seeking to maximize their consumer surplus and potentially misrepresenting their preferences. To address these challenges, we propose novel algorithmic solutions. Our approach involves: (i) a carefully designed active randomness injection to balance exploration and exploitation effectively; (ii) using non-i.i.d.

We study the overfitting behavior of fully connected deep Neural Networks (NNs) with binary weights fitted to perfectly classify a noisy training set. We consider interpolation using both the smallest NN (having the minimal number of weights) and a random interpolating NN. For both learning rules, we prove overfitting is tempered. Our analysis rests on a new bound on the size of a threshold circuit consistent with a partial function. To the best of our knowledge, ours are the first theoretical results on benign or tempered overfitting that: (1) apply to deep NNs, and (2) do not require a very high or very low input dimension.

Consider the following problem faced by a geographical query service (e.g.

Humans easily interpret expressions that describe unfamiliar situations composed from familiar parts ("greet the pink brontosaurus by the ferris wheel"). Modern neural networks, by contrast, struggle to interpret novel compositions. In this paper, we introduce a new benchmark, gSCAN, for evaluating compositional generalization in situated language understanding. Going beyond a related benchmark that focused on syntactic aspects of generalization, gSCAN defines a language grounded in the states of a grid world, facilitating novel evaluations of acquiring linguistically motivated rules. For example, agents must understand how adjectives such as'small' are interpreted relative to the current world state or how adverbs such as'cautiously' combine with new verbs. We test a strong multi-modal baseline model and a state-of-the-art compositional method finding that, in most cases, they fail dramatically when generalization requires systematic compositional rules.

However, the datasets they worked on, such as MELD [1], IEMOCAP [2], UR-FUNNY [3], MUStARD [4], etc., have annotations on solely one or two types of affection, and inter-relatedness between tasks is absent. Without an explicit annotation of cross-task correlations, the potential of multi-modal multi-affection joint detection could not be fully explored, neither deepen the understanding on human complicated affections. We fill the gap by constructing a large-scale benchmark multi-modal multi-affection conversational dataset. We manage to tackle the following main challenges for building such a dataset: (1) multiaffection joint judgment: the subjectivity and creativity of human language make it hard to judge different affections at the same time accurately; (2) multi-affection correlation: different affections can be indistinguishable at certain circumstances, and it is difficult to accurately measure their relatedness; (3) context effect: an utterance may express different affections in different conversational contexts. Each utterance is annotated with sentiment (including pride and romantic love), emotion, sarcasm and humor labels, accompanied by sentiment-emotion and sarcasm-humor inter-relatedness measures. Considering that the external knowledge implicitly influences the speaker's affective state, the speaker's background (i.e., name, profession, sex, personality) and the topic of each conversation are provided, an example as illustrated in Figure 1. Each utterance contains textual, visual and acoustic information, which are stored in.CSV,.MP4,.WAV files. We have also collected real dialogue samples from the 10086 customer service of China Mobile Communication Group Tianjin Co., but due to the protection of user privacy and company regulations, we cannot publicly disclose these samples. Metropolitan opera Romance, Idol " 都挺好 " (All Is Well) Crime thriller Crime " 心理罪 " (Guilty of Mind) The statistics of the TV are shown in Table 1. We could notice that our domain includes six comedy shows, four metropolitan opera shows, five dramas and four thrillers, which is well-proportioned (6:4:5:4). Moreover, such TV shows cover various styles, e.g., costume, idol, romance, war, family, history, crime, which provide numerous conversation topics. Both actions will ensure us to collect balanced sentiment, emotion, sarcasm, humor, pride and love labels in the best possible way. We argue that the speaker's information is also collected.