In Supreme Court parlance and the political science literature, an ideal point positions a justice in a continuous space and can be interpreted as a quantification of the justice's policy preferences. We present an automated approach to infer such ideal points for justices of the US Supreme Court. This approach combines topic modeling over case opinions with the voting (and endorsing) behavior of justices. Furthermore, given a topic of interest, say the Fourth Amendment, the topic model can be optionally seeded with supervised information to steer the inference of ideal points. Application of this methodology over five years of cases provides interesting perspectives into the leaning of justices on crucial issues, coalitions underlying specific topics, and the role of swing justices in deciding the outcomes of cases.

Matching a question to its best answer is a common task in community question answering. In this paper, we focus on the non-factoid questions and aim to pick out the best answer from its candidate answers. Most of the existing deep models directly measure the similarity between question and answer by their individual sentence embeddings. In order to tackle the problem of the information lack in question's descriptions and the lexical gap between questions and answers, we propose a novel deep architecture namely SPAN in this paper. Specifically we introduce support answers to help understand the question, which are defined as the best answers of those similar questions to the original one. Then we can obtain two kinds of similarities, one is between question and the candidate answer, and the other one is between support answers and the candidate answer. The matching score is finally generated by combining them. Experiments on Yahoo! Answers demonstrate that SPAN can outperform the baseline models.

Zero-shot Recognition (ZSR) is to learn recognition models for novel classes without labeled data. It is a challenging task and has drawn considerable attention in recent years. The basic idea is to transfer knowledge from seen classes via the shared attributes. This paper focus on the transductive ZSR, i.e., we have unlabeled data for novel classes. Instead of learning models for seen and novel classes separately as in existing works, we put forward a novel joint learning approach which learns the shared model space (SMS) for models such that the knowledge can be effectively transferred between classes using the attributes. An effective algorithm is proposed for optimization. We conduct comprehensive experiments on three benchmark datasets for ZSR. The results demonstrates that the proposed SMS can significantly outperform the state-of-the-art related approaches which validates its efficacy for the ZSR task.

Instead of using a uniform metric, instance specific distance learning methods assign multiple metrics for different localities, which take data heterogeneity into consideration. Therefore, they may improve the performance of distance based classifiers, e.g., kNN. Existing methods obtain multiple metrics of test data by either transductively assigning metrics for unlabeled instances or designing distance functions manually, which are with limited generalization ability. In this paper, we propose isMets (Instance Specific METric Subspace) framework which can automatically span the whole metric space in a generative manner and is able to inductively learn a specific metric subspace for each instance via inferring the expectation over the metric bases in a Bayesian manner. The whole framework can be solved with Variational Bayes (VB). Experiment on synthetic data shows that the learned results are with good interpretability. Moreover, comprehensive results on real world datasets validate the effectiveness and robustness of isMets.

With the recent proliferation of large-scale learning problems, there have been a lot of interest on distributed machine learning algorithms, particularly those that are based on stochastic gradient descent (SGD) and its variants. However, existing algorithms either suffer from slow convergence due to the inherent variance of stochastic gradients, or have a fast linear convergence rate but at the expense of poorer solution quality. In this paper, we combine their merits by proposing a fast distributed asynchronous SGD-based algorithm with variance reduction. A constant learning rate can be used, and it is also guaranteed to converge linearly to the optimal solution. Experiments on the Google Cloud Computing Platform demonstrate that the proposed algorithm outperforms state-of-the-art distributed asynchronous algorithms in terms of both wall clock time and solution quality.

Previous work for relation extraction from free text is mainly based on intra-sentence information. As relations might be mentioned across sentences, inter-sentence information can be leveraged to improve distantly supervised relation extraction. To effectively exploit inter-sentence information, we propose a ranking based approach, which first learns a scoring function based on a listwise learning-to-rank model and then uses it for multi-label relation extraction. Experimental results verify the effectiveness of our method for aggregating information across sentences. Additionally, to further improve the ranking of high-quality extractions, we propose an effective method to rank relations from different entity pairs. This method can be easily integrated into our overall relation extraction framework, and boosts the precision significantly.

Policy Iteration (PI) (Howard 1960) is a classical method for computing an optimal policy for a finite Markov Decision Problem (MDP). The method is conceptually simple: starting from some initial policy, “policy improvement” is repeatedly performed to obtain progressively dominating policies, until eventually, an optimal policy is reached. Being remarkably efficient in practice, PI is often favoured over alternative approaches such as Value Iteration and Linear Programming. Unfortunately, even after several decades of study, theoretical bounds on the complexity of PI remain unsatisfactory. For an MDP with n states and k actions, Mansour and Singh (1999) bound the number of iterations taken by Howard’s PI, the canonical variant of the method, by O ( k n / n ). This bound merely improves upon the trivial bound of kn by a linear factor. However, a randomised variant of PI introduced by Mansour and Singh (1999) does yield an exponential improvement, with its expected number of iterations bounded by O(((1 + 2/log 2 ( k )) k / 2) n ).With the objective of furnishing improved upper bounds for PI, we introduce two randomised procedures in this paper. Our first contribution is a routine to find a good initial policy for PI. After evaluating a number of randomly generated policies, this procedure applies a novel criterion to pick one to initialise PI. When PI is subsequently applied, we show that the expected number of policy evaluations—including both the initialisation and the improvement stages—remains bounded in expectation by O ( k n /2 ). The key construction employed in this routine is a total order on the set of policies. Our second contribution is a randomised action-switching rule for PI, which admits a bound of O((2 + ln( k – 1)) n ) on the expected number of iterations. To the best of our knowledge, this is the tightest complexity bound known for PI when k >= 3.  

In timeseries classification, shapelets are subsequences of timeseries with high discriminative power. Existing methods perform a combinatorial search for shapelet discovery. Even with speedup heuristics such as pruning, clustering, and dimensionality reduction, the search remains computationally expensive. In this paper, we take an entirely different approach and reformulate the shapelet discovery task as a numerical optimization problem. In particular, the shapelet positions are learned by combining the generalized eigenvector method and fused lasso regularizer to encourage a sparse and blocky solution. Extensive experimental results show that the proposed method is orders of magnitudes faster than the state-of-the-art shapelet-based methods, while achieving comparable or even better classification accuracy.

This paper considers survey prediction from social media. We use topic models to correlate social media messages with survey outcomes and to provide an interpretable representation of the data. Rather than rely on fully unsupervised topic models, we use existing aggregated survey data to inform the inferred topics, a class of topic model supervision referred to as collective supervision. We introduce and explore a variety of topic model variants and provide an empirical analysis, with conclusions of the most effective models for this task.

Poker is a family of card games that includes many varia- tions. We hypothesize that most poker games can be solved as a pattern matching problem, and propose creating a strong poker playing system based on a unified poker representa- tion. Our poker player learns through iterative self-play, and improves its understanding of the game by training on the results of its previous actions without sophisticated domain knowledge. We evaluate our system on three poker games: single player video poker, two-player Limit Texas Hold’em, and finally two-player 2-7 triple draw poker. We show that our model can quickly learn patterns in these very different poker games while it improves from zero knowledge to a competi- tive player against human experts. The contributions of this paper include: (1) a novel represen- tation for poker games, extendable to different poker vari- ations, (2) a Convolutional Neural Network (CNN) based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that signif- icantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players.