In order to better protect and conserve biodiversity, ecologists use machine learning and statistics to understand how species respond to their environment and to predict how they will respond to future climate change, habitat loss and other threats. A fundamental modeling task is to estimate the probability that a given species is present in (or uses) a site, conditional on environmental variables such as precipitation and temperature. For a limited number of species, survey data consisting of both presence and absence records are available, and can be used to fit a variety of conventional classification and regression models. For most species, however, the available data consist only of occurrence records --- locations where the species has been observed. In two closely-related but separate bodies of ecological literature, diverse special-purpose models have been developed that contrast occurrence data with a random sample of available environmental conditions. The most widespread statistical approaches involve either fitting an exponential model of species' conditional probability of presence, or fitting a naive logistic model in which the random sample of available conditions is treated as absence data; both approaches have well-known drawbacks, and do not necessarily produce valid probabilities. After summarizing existing methods, we overcome their drawbacks by introducing a new scaled binomial loss function for estimating an underlying logistic model of species presence/absence. Like the Expectation-Maximization approach of Ward et al. and the method of Steinberg and Cardell, our approach requires an estimate of population prevalence, $\Pr(y=1)$, since prevalence is not identifiable from occurrence data alone. In contrast to the latter two methods, our loss function is straightforward to integrate into a variety of existing modeling frameworks such as generalized linear and additive models and boosted regression trees. We also demonstrate that approaches by Lele and Keim and by Lancaster and Imbens that surmount the identifiability issue by making parametric data assumptions do not typically produce valid probability estimates.

In this paper we present a data-driven approach to modeling end user energy consumption in residential and commercial buildings. Our model is based upon a data set of monthly electricity and gas bills, collected by a utility over the course of several years, for approximately 6,500 buildings in Cambridge, MA. In addition, we use publicly available tax assessor records and geographical survey information to determine corresponding features for the buildings. Using both parametric and non-parametric learning methods, we learn models that predict distributions over energy usage based upon these features, and use these models to develop two end-user systems. For utilities or authorized institutions (those who may obtain access to the full data) we provide a system that visualizes energy consumption for each building in the city; this allows companies to quickly identify outliers (buildings which use much more energy than expected even after conditioning on the relevant predictors), for instance allowing them to target homes for potential retrofits or tiered pricing schemes. For other end users, we provide an interface for entering their own electricity and gas usage, along with basic information about their home, to determine how their consumption compares to that of similar buildings as predicted by our model. Merely allowing users to contextualize their consumption in this way, relating it to the consumption in similar buildings, can itself produce behavior changes to significantly reduce consumption.

Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit models of the ecological phenomenon of interest and the process by which it is observed. However, existing latent variable methods rely on hand-formulated parametric models, which are expensive to design and require extensive preprocessing of the data. Nonparametric methods (such as regression trees) automate these decisions and produce highly accurate models. However, existing tree methods learn direct mappings from inputs to outputs — they cannot be applied to latent variable models. This paper describes a methodology for integrating nonparametric tree methods into probabilistic latent variable models by extending functional gradient boosting. The approach is presented in the context of occupancy-detection (OD) modeling, where the goal is to model the distribution of a species from imperfect detections. Experiments on 12 real and 3 synthetic bird species compare standard and tree-boosted OD models (latent variable models) with standard and tree-boosted logistic regression models (without latent structure). All methods perform similarly when predicting the observed variables, but the OD models learn better representations of the latent process. Most importantly, tree-boosted OD models learn the best latent representations when nonlinearities and interactions are present.

Variable selection problems are typically addressed under apenalized optimization framework. Nonconvex penalties such as the minimax concave plus (MCP) and smoothly clipped absolute deviation(SCAD), have been demonstrated to have the properties of sparsity practically and theoretically. In this paper we propose a new nonconvex penalty that we call exponential-type penalty. The exponential-type penalty is characterized by a positive parameter,which establishes a connection with the ell 0 and ell 1 penalties.We apply this new penalty to sparse supervised learning problems. To solve to resulting optimization problem, we resort to a reweighted ell 1 minimization method. Moreover, we devise an efficient method for the adaptive update of the tuning parameter. Our experimental results are encouraging. They show that the exponential-type penalty is competitive with MCP and SCAD.

This paper presents a new Monte-Carlo search algorithm for very large sequential decision-making problems. We apply non-linear regression within Monte-Carlo search, online, to estimate a state-action value function from the outcomes of random roll-outs. This value function generalizes between related states and actions, and can therefore provide more accurate evaluations after fewer rollouts. A further significant advantage of this approach is its ability to automatically extract and leverage domain knowledge from external sources such as game manuals. We apply our algorithm to the game of Civilization II, a challenging multi-agent strategy game with an enormous state space and around 10^21 joint actions. We approximate the value function by a neural network, augmented by linguistic knowledge that is extracted automatically from the official game manual. We show that this non-linear value function is significantly more efficient than a linear value function, which is itself more efficient than Monte-Carlo tree search. Our non-linear Monte-Carlo search wins over 78% of games against the built-in AI of Civilization II.

This paper identifies a widely existing phenomenon in web data, which we call the "words of few mouths" phenomenon. This phenomenon, in the context of online reviews, refers to the case that a large fraction of the reviews are each voted only by very few users. We discuss the challenges of "words of few mouths" in the development of recommender systems based on users' opinions and advocate probabilistic methodologies to handle such challenges. We develop a probabilistic model and correspondingly a logistic regression based learning algorithm for review helpfulness prediction. Our experimental results indicate that the proposed model outperforms the current state-of-the-art algorithms not only in the presence of the "words of few mouths" phenomenon, but also in the absence of such phenomena.

The possibility that influenza activity can be generally detected through search log analysis has been explored in recent years. However, previous studies have mainly focused on influenza, and little attention has been paid to other epidemics. With an analysis of web user behavior data, we consider the problem of predicting the tendency of hand-foot -and-mouth disease  (HFMD), whose out-break in 2010 resulted in a great panic in China. In addi-tion to search queries, we consider users’ interactions with search engines. Given the collected search logs, we cluster HFMD-related search queries, medical pages and news reports into the following sets: epidemic-related queries (ERQs), epidemic-related pages (ERPs) and ep-idemic-related news (ERNs). Furthermore, we count their own frequencies as different features, and we conduct a regression analysis with current HFMD occurrences. The experimental results show that these features exhibit good performances on both accuracy and timeliness.

In this paper, we propose a novel approach for learning regression rules by transforming the regression problem into a classification problem. Unlike previous approaches to regression by classification, in our approach the discretization of the class variable is tightly integrated into the rule learning algorithm. The key idea is to dynamically define a region around the target value predicted by the rule, and considering all examples within that region as positive and all examples outside that region as negative. In this way, conventional rule learning heuristics may be used for inducing regression rules. Our results show that our heuristic algorithm outperforms approaches that use a static discretization of the target variable, and performs en par with other comparable rule-based approaches, albeit without reaching the performance of statistical approaches.

In this paper, we introduce a probabilistic classification model to address the task of semi-supervised learning. The major novelty of our proposal stems from measuring distributional relationships between the labeled and unlabeled data. This is achieved from a stochastic translation model between data distributions that is estimated from a mixture model. The proposed classifier is defined from the combination of both the translation model and a kernel logistic regression on labeled data. Experimental results obtained over synthetic and real-world data sets validate the usefulness of our proposal.

Online discussion boards, or Internet forums, are a significant part of the Internet. People use Internet forums to post questions, provide advice and participate in discussions. These online conversations are represented as threads, and the conversation trees within these threads are important in understanding the behaviour of online users. Unfortunately, the reply structures of these threads are generally not publicly accessible or not maintained. Hence, in this paper, we introduce an efficient and simple approach to reconstruct the reply structure in threaded conversations. We contrast its accuracy against three baseline algorithms, and show that our algorithm can accurately recreate the in and out degree distributions of forum reply graphs built from the reconstructed reply structures.