Algorithms for equilibrium computation generally make no attempt to ensure that the computed strategies are understandable by humans. For instance the strategies for the strongest poker agents are represented as massive binary files. In many situations, we would like to compute strategies that can actually be implemented by humans, who may have computational limitations and may only be able to remember a small number of features or components of the strategies that have been computed. We study poker games where private information distributions can be arbitrary. We create a large training set of game instances and solutions, by randomly selecting the information probabilities, and present algorithms that learn from the training instances in order to perform well in games with unseen information distributions. We are able to conclude several new fundamental rules about poker strategy that can be easily implemented by humans.

Machine learning can infuse every application with predictive power. Data scientists use these sophisticated algorithms to dissect, search, sift, sort, infer, foretell, and otherwise make sense of the growing amounts of data in our world. Fundamentally, machine learning is a productivity tool for data scientists. As the heart of systems that can learn from data, machine learning allows data scientists to train a model on an example data set and then leverage algorithms that automatically generalize and learn both from that example and from fresh data feeds. With unsupervised approaches, data scientists can dispense with training examples entirely and use machine learning to distill insights directly and continuously from the data.

Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample where two or more training examples may share common features. We propose an efficient weighting method for learning from networked examples and show the sample error bound which is better than previous work.

Long-term automated monitoring of residential or small in- dustrial properties is an important task within the broader scope of human activity recognition. We present a device- free wifi-based localization system for smart indoor spaces, developed in a collaboration between McGill University and Aerˆıal Technologies. The system relies on existing wifi net- work signals and semi-supervised learning, in order to au- tomatically detect entrance into a residential unit, and track the location of a moving subject within the sensing area. The implemented real-time monitoring platform works by detect- ing changes in the characteristics of the wifi signals collected via existing off-the-shelf wifi-enabled devices in the environ- ment. This platform has been deployed in several apartments in the Montreal area, and the results obtained show the poten- tial of this technology to turn any regular home with an ex- isting wifi network into a smart home equipped with intruder alarm and room-level location detector. The machine learn- ing component has been devised so as to minimize the need for user annotation and overcome temporal instabilities in the input signals. We use a semi-supervised learning framework which works in two phases. First, we build a base learner for mapping wifi signals to different physical locations in the en- vironment from a small amount of labeled data; during its lifetime, the learner automatically re-trains when the uncer- tainty level rises significantly, without the need for further supervision. This paper describes the technical and practical issues arising in the design and implementation of such a sys- tem for real residential units, and illustrates its performance during on-going deployment.

Quasi-supervised Learning (QSL) computes the posterior probabilities of two classes at the data points in Z. The class labels are stored in the vector Y. k denotes the number of points to be included in the reference set for exhaustive nearest neighbor classifications, and D is the symmetrical matrix of pairwise distances of the points in Z. In order to use qsl function, you need to add kstar.R function into your workspace.

We study the problem of structured prediction under test-time budget constraints. We propose a novel approach based on selectively acquiring computationally costly features during test-time in order to reduce the computational cost of pre- diction with minimal performance degradation. We formulate a novel empirical risk minimization (ERM) for policy learning. We show that policy learning can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two structured prediction tasks, optical character recognition and dependency parsing and show significant reduction in the feature costs without degrading accuracy.

Given any complicated or specialized video content search query, e.g. ”Batkid (a kid in batman costume)” or ”destroyed buildings”, existing methods require manually labeled data to build detectors for searching. We present a demonstration of an artificial intelligence application, Webly-labeled Learning (WELL) that enables learning of ad-hoc concept detectors over unlimited Internet videos without any manual an-notations. A considerable number of videos on the web are associated with rich but noisy contextual information, such as the title, which provides a type of weak annotations or la-bels of the video content. To leverage this information, our system employs state-of-the-art webly-supervised learning(WELL) (Liang et al. ). WELL considers multi-modal information including deep learning visual, audio and speech features, to automatically learn accurate video detectors based on the user query. The learned detectors from a large number of web videos allow users to search relevant videos over their personal video archives, not requiring any textual metadata,but as convenient as searching on Youtube.

We propose a framework to improve the performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction. We further extend this framework to make a novel use of document structure: in some small, well-structured corpora, sections can be identified that correspond to relation arguments, and distantly-labeled examples from such sections tend to have good precision. Using these as seeds we extract additional relation examples by applying label propagation on a graph composed of noisy examples extracted from a large unstructured testing corpus. Combined with the soft constraint that concept examples should have the same type as the second argument of the relation, we get significant improvements over several state-of-the-art approaches to distantly-supervised relation extraction, and reasonable extraction performance even with very small set of distant labels.

Time series data is common in a wide range of disciplines including finance, biology, sociology, and computer science. Analyzing and modeling time series data is fundamental for studying various problems in those fields. For instance, studying time series physiological data can be used to discriminate patients’ abnormal recovery trajectories and normal ones (Hripcsak, Albers, and Perotte 2015). GPS data are useful for studying collective decision making of groupliving animals (Strandburg-Peshkin et al. 2015). There are different methods for studying time series data such as clustering, regression, and anomaly detection. In this proposal, we are interested in structured prediction problems in time series data. Structured prediction focuses on prediction task where the outputs are structured and interdependent, contrary to the non-structured prediction which assumes that the outputs are independent of other predicted outputs. Structured prediction is an important problem as there are structures inherently existing in time series data. One difficulty for structured prediction is that the number of possible outputs can be exponential which makes modeling all the potential outputs intractable.

Machine Learning and Inference methods have become ubiquitous in our attempt to induce more abstract representations of natural language text, visual scenes, and other messy, naturally occurring data, and support decisions that depend on it. However, learning models for these tasks is difficult partly because generating the necessary supervision signals for it is costly and does not scale. This paper describes several learning paradigms that are designed to alleviate the supervision bottleneck. It will illustrate their benefit in the context of multiple problems, all pertaining to inducing various levels of semantic representations from text. In particular, we discuss (i) esponse Driven Learning of models, a learning protocol that supports inducing meaning representations simply by observing the model's behavior in its environment, (ii) the exploitation of Incidental Supervision signals that exist in the data, independently of the task at hand, to learn models that identify and classify semantic predicates, and (iii) the use of weak supervision to combine simple models to support global decisions where joint supervision is not available.