Sarcasm Suite is a browser-based engine that deploys five of our past papers in sarcasm detection and generation. The sarcasm detection modules use four kinds of incongruity: sentiment incongruity, semantic incongruity, historical context incongruity and conversational context incongruity. The sarcasm generation module is a chatbot that responds sarcastically to user input. With a visually appealing interface that indicates predictions using `faces' of our co-authors from our past papers, Sarcasm Suite is our first demonstration of our work in computational sarcasm.

Stochastic gradient descent (SGD) and its variants have attracted much attention in machine learning due to their efficiency and effectiveness for optimization. To handle large-scale problems, researchers have recently proposed several lock-free strategy based parallel SGD (LF-PSGD) methods for multi-core systems. However, existing works have only proved the convergence of these LF-PSGD methods for convex problems. To the best of our knowledge, no work has proved the convergence of the LF-PSGD methods for non-convex problems. In this paper, we provide the theoretical proof about the convergence of two representative LF-PSGD methods, Hogwild! and AsySVRG, for non-convex problems. Empirical results also show that both Hogwild! and AsySVRG are convergent on non-convex problems, which successfully verifies our theoretical results.

One fundamental problem in causal inference is the treatment effect estimation in observational studies when variables are confounded. Control for confounding effect is generally handled by propensity score. But it treats all observed variables as confounders and ignores the adjustment variables, which have no influence on treatment but are predictive of the outcome. Recently, it has been demonstrated that the adjustment variables are effective in reducing the variance of the estimated treatment effect. However, how to automatically separate the confounders and adjustment variables in observational studies is still an open problem, especially in the scenarios of high dimensional variables, which are common in big data era. In this paper, we propose a Data-Driven Variable Decomposition (D$^2$VD) algorithm, which can 1) automatically separate confounders and adjustment variables with a data driven approach, and 2) simultaneously estimate treatment effect in observational studies with high dimensional variables. Under standard assumptions, we show experimentally that the proposed D$^2$VD algorithm can automatically separate the variables precisely, and estimate treatment effect more accurately and with tighter confidence intervals than the state-of-the-art methods on both synthetic data and real online advertising dataset.

When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.

Existing visual tracking methods usually localize the object with a bounding box, in which the foreground object trackers/detectors are often disturbed by the introduced background information. To handle this problem, we aim to learn a more robust object representation for visual tracking. In particular, the tracked object is represented with a graph structure (i.e., a set of non-overlapping image patches), in which the weight of each node (patch) indicates how likely it belongs to the foreground and edges are also weighed for indicating the appearance compatibility of two neighboring nodes. This graph is dynamically learnt (i.e., the nodes and edges received weights) and applied in object tracking and model updating. We constrain the graph learning from two aspects: i) the global low-rank structure over all nodes and ii) the local sparseness of node neighbors. During the tracking process, our method performs the following steps at each frame. First, the graph is initialized by assigning either 1 or 0 to the weights of some image patches according to the predicted bounding box. Second, the graph is optimized through designing a new ALM (Augmented Lagrange Multiplier) based algorithm. Third, the object feature representation is updated by imposing the weights of patches on the extracted image features. The object location is finally predicted by adopting the Struck tracker. Extensive experiments show that our approach outperforms the state-of-the-art tracking methods on two standard benchmarks, i.e., OTB100 and NUS-PRO.

In this paper we propose a simple yet efficient method for constrained word deletion to compress sentences, based on top-down greedy local flipping from multiple random initializations. The algorithm naturally integrates various grammatical constraints in the compression process, without using time-consuming integer linear programming solvers. Our formulation suits for any objective function involving arbitrary local score definition. Experimental results show that the proposed method achieves nearly identical performance with explicit ILP formulation while being much more efficient.

In general, which entity mentions in a text or dialogue refer to the same however, the difficulty of coreference resolution stems from real-world entity. Despite being actively investigated for 50 its reliance on sophisticated knowledge sources and inference years in the natural language processing (NLP) community, mechanisms (Mitkov et al. 2001). Despite its difficulty, it is still far from being solved. To better understand the difficulty coreference resolution is a core task in information extraction: of the task, consider the following sentence: it is the fundamental technology for consolidating the textual information about an entity, which is crucial for essentially The Queen Mother asked Queen Elizabeth II to transform all high-level NLP applications, such as question her sister, Princess Margaret, into a viable answering, text summarization, and machine translation.

Multidimensional Scaling (MDS) is a classic technique that seeks vectorial representations for data points, given the pairwise distances between them. In recent years, data are usually collected from diverse sources or have multiple heterogeneous representations. However, how to do multidimensional scaling on multiple input distance matrices is still unsolved to our best knowledge. In this paper, we first define this new task formally. Then, we propose a new algorithm called Multi-View Multidimensional Scaling (MVMDS) by considering each input distance matrix as one view. The proposed algorithm can learn the weights of views (i.e., distance matrices) automatically by exploring the consensus information and complementary nature of views. Experimental results on synthetic as well as real datasets demonstrate the effectiveness of MVMDS. We hope that our work encourages a wider consideration in many domains where MDS is needed.

In standard machine learning and regression setting feature values are used to predict some desired information. The privacy challenge considered here is to prevent an adversary from using available feature values to predict confidential information that one wishes to keep secret. We show that this can sometimes be achieved with almost no effect on the qual- ity of predicting desired information. We describe two algorithms aimed at providing such privacy when the predictors have a linear operator in the first stage. The desired effect can be achieved by zeroing out feature components in the approximate null space of the linear operator.

Lack of sufficient training data with exact ages is still a challenge for facial age estimation. To deal with such problem, a method called Label Distribution Learning (LDL) was proposed to utilize the neighboring ages while learning a particular age. Later, an adaptive version of LDL called ALDL was proposed to generate a proper label distribution for each age. However, the adaptation process requires more training data, which creates a dilemma between the performance of ALDL and the training data. In this paper, we propose an algorithm called Semi-supervised Adaptive Label Distribution Learning (SALDL) to solve the dilemma and improve the performance using unlabeled data for facial age estimation. On the one hand, the utilization of unlabeled data helps to improve the adaptation process. On the other hand, the adapted label distributions conversely reinforce the semi-supervised process. As a result, they can promote each other to get better performance. Experimental results show that SALDL performs remarkably better than state-of-the-art algorithms when there are only limited accurately labeled data available.