Classical model-based partitional clustering algorithms, such ask-means or mixture of Gaussians, provide only loose and indirect control over the size of the resulting clusters. In this work, we present a family of probabilistic clustering models that can be steered towards clusters of desired size by providing a prior distribution over the possible sizes, allowing the analyst to fine-tune exploratory analysis or to produce clusters of suitable size for future down-stream processing.Our formulation supports arbitrary multimodal prior distributions, generalizing the previous work on clustering algorithms searching for clusters of equal size or algorithms designed for the microclustering task of finding small clusters. We provide practical methods for solving the problem, using integer programming for making the cluster assignments, and demonstrate that we can also automatically infer the number of clusters.

For tasks such as medical diagnosis and knowledge base completion, a classifier may only have access to positive and unlabeled examples, where the unlabeled data consists of both positive and negative examples. One way that enables learning from this type of data is knowing the true class prior. In this paper, we propose a simple yet effective method for estimating the class prior, by estimating the probability that a positive example is selected to be labeled. Our key insight is that subdomains of the data give a lower bound on this probability. This lower bound gets closer to the real probability as the ratio of labeled examples increases. Finding such subsets can naturally be done via top-down decision tree induction. Experiments show that our method makes estimates which are equivalently accurate as those of the state of the art methods, and is an order of magnitude faster.

Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, medicine and many other areas. A fundamental problem is to understand the relationship between the covariates and the (distribution of) survival times(times-to-event). Much of the previous work has approached the problem by viewing the survival time as the first hitting time of a stochastic process, assuming a specific form for the underlying stochastic process, using available data to learn the relationship between the covariates and the parameters of the model, and then deducing the relationship between covariates and the distribution of first hitting times (the risk). However, previous models rely on strong parametric assumptions that are often violated. This paper proposes a very different approach to survival analysis, DeepHit, that uses a deep neural network to learn the distribution of survival times directly.DeepHit makes no assumptions about the underlying stochastic process and allows for the possibility that the relationship between covariates and risk(s) changes over time. Most importantly, DeepHit smoothly handles competing risks; i.e. settings in which there is more than one possible event of interest.Comparisons with previous models on the basis of real and synthetic datasets demonstrate that DeepHit achieves large and statistically significant performance improvements over previous state-of-the-art methods.

Proximity search on heterogeneous graphs aims to measure the proximity between two nodes on a graph w.r.t. some semantic relation for ranking. Pioneer work often tries to measure such proximity by paths connecting the two nodes. However, paths as linear sequences have limited expressiveness for the complex network connections. In this paper, we explore a more expressive DAG (directed acyclic graph) data structure for modeling the connections between two nodes. Particularly, we are interested in learning a representation for the DAGs to encode the proximity between two nodes. We face two challenges to use DAGs, including how to efficiently generate DAGs and how to effectively learn DAG embedding for proximity search. We find distance-awareness as important for proximity search and the key to solve the above challenges. Thus we develop a novel Distance-aware DAG Embedding (D2AGE) model. We evaluate D2AGE on three benchmark data sets with six semantic relations, and we show that D2AGE outperforms the state-of-the-art baselines. We release the code on https://github.com/shuaiOKshuai.

This paper presents a Generative Adversarial Network (GAN) to model multiturn dialogue generation, which trains a latent hierarchical recurrent encoder-decoder simultaneously with a discriminative classifier that make the prior approximate to the posterior. Experiments show that our model achieves better results.

Variational encoder-decoders have shown promising results in seq2seq tasks. However, the training process is known difficult to be controlled because latent variables tend to be ignored while decoding. In this paper, we thoroughly analyze the reason behind this training difficulty, compare different ways of alleviating it and propose a new framework that helps significantly improve the overall performance.

This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under specific scenarios, and 3) keeps evolving with the growth of anomaly detection experience. Essentially, the anomaly detector is powered by the Recurrent Neural Network (RNN) and adopts the Reinforcement Learning (RL) method to achieve the self-learning process. Our initial experiments demonstrate promising results of using the detector in network time series anomaly detection problems.

Cross-lingual Learning can help to bring state-of-the-art deep learning solutions to smaller languages. These languages in general lack resource for training advanced neural networks. With transfer of knowledge across languages we can improve the results for various NLP tasks.

Artificial Intelligence (AI) is becoming increasingly important. Thus, sound knowledge about the principles of AI will be a crucial factor for future careers of young people as well as for the development of novel, innovative products. Addressing this challenge, we present an ambitious 3-year project focusing on developing and implementing a professional, internationally accepted, standardized training and certification system for AI which will also be recognized by the industry and educational institutions. The approach is based on already implemented and evaluated pilot projects in the area of AI education. The project’s main goal is to train and certify teachers and mentors as well as students and young people in basic and advanced AI topics, fostering AI literacy among this target audience.

Thymio is a small, inexpensive, mass-produced mobile robot with widespread use in primary and secondary education. In order to make it more versatile and effectively use it in later educational stages, including university levels, we have expanded Thymio's capabilities by adding off-the-shelf hardware and open software components. The resulting robot, that we call Mighty Thymio, provides additional sensing functionalities, increased computing power, networking, and full ROS integration. We present the architecture of Mighty Thymio and show its application in advanced educational activities.