In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are "sensitive," in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.

Feature engineering is the key to successful application of machine learning algorithms to real-world data. The discovery of informative features often requires domain knowledge or human inspiration, and data scientists expend a certain amount of effort into exploring feature spaces. Crowdsourcing is considered a promising approach for allowing many people to be involved in feature engineering; however, there is a demand for a sophisticated strategy that enables us to acquire good features at a reasonable crowdsourcing cost. In this paper, we present a novel algorithm called AdaFlock to efficiently obtain informative features through crowdsourcing. AdaFlock is inspired by AdaBoost, which iteratively trains classifiers by increasing the weights of samples misclassified by previous classifiers. AdaFlock iteratively generates informative features; at each iteration of AdaFlock, crowdsourcing workers are shown samples selected according to the classification errors of the current classifiers and are asked to generate new features that are helpful for correctly classifying the given examples. The results of our experiments conducted using real datasets indicate that AdaFlock successfully discovers informative features with fewer iterations and achieves high classification accuracy.

Animating digital characters has an important role in computer assisted experiences, from video games to movies to interactive robotics. A critical challenge in the field is to generate animations which accurately reflect the state of the animated characters, without looking repetitive or unnatural. In this work, we investigate the problem of procedurally generating a diverse variety of facial animations that express a given semantic quality (e.g., very happy). To that end, we introduce a new learning heuristic called Precision Variety Learning (PVL) which actively identifies and exploits the fundamental trade-off between precision (how accurate positive labels are) and variety (how diverse the set of positive labels is). We both identify conditions where important theoretical properties can be guaranteed, and show good empirical performance in variety of conditions. Lastly, we apply our PVL heuristic to our motivating problem of generating smile animations, and perform several user studies to validate the ability of our method to produce a perceptually diverse variety of smiles for different target intensities.

Badges are a common, and sometimes the only, method of incentivizing users to perform certain actions on on- line sites. However, due to many competing factors influencing user temporal dynamics, it is difficult to determine whether the badge had (or will have) the intended effect or not. In this paper, we introduce two complementary approaches for determining badge influence on users. In the first one, we cluster users’ temporal traces (represented with Poisson processes) and apply covariates (user features) to regularize results. In the second approach, we first classify users’ temporal traces with a novel statistical framework, and then we refine the classification results with a semi-supervised clustering of covariates. Outcomes obtained from an evaluation on synthetic datasets and experiments on two badges from a pop- ular Q&A platform confirm that it is possible to validate, characterize and to some extent predict users affected by the badge.

Link prediction is a fundamental problem with a wide range of applications in various domains, which predicts the links that are not yet observed or the links that may appear in the future. Most existing works in this field only focus on modeling a single network, while real-world networks are actually aligned with each other. Network alignments contain valuable additional information for understanding the networks, and provide a new direction for addressing data insufficiency and alleviating cold start problem. However, there are rare works leveraging network alignments for better link prediction. Besides, neural network is widely employed in various domains while its capability of capturing high-level patterns and correlations for link prediction problem has not been adequately researched yet. Hence, in this paper we target atlink prediction over aligned networks using neural networks. The major challenge is the heterogeneousness of the considered networks, as the networks may have different characteristics, link purposes, etc. To overcome this, we propose a novel multi-neural-network framework MNN, where we have one individual neural network for each heterogeneous target or feature while the vertex representations are shared. We further discuss training methods for the multi-neural-network framework. Extensive experiments demonstrate that MNN outperforms the state-of-the-art methods and achieves 3% to 5% relative improvement of AUC score across different settings, particularly over 8% for cold start scenarios.

We present a novel method for automated segmentation of overlapping cytoplasm in cervical smear images based on contour fragments. We formulate the segmentation problem as a graphical model, and employ the contour fragments generated from cytoplasm clump to construct the graph. Compared with traditional methods that are based on pixels, our contour fragment-based solution can take more geometric information into account and hence generate more accurate prediction of the overlapping boundaries. We further design a novel energy function for the graph, and by minimizing the energy function, fragments that come from the same cytoplasm are selected into the same set. To construct the energy function, our fragments-based data term and pairwise term are measured from the spatial relation and shape prior, which offer more geometric information for the occluded boundary inference. Afterwards, occluded boundaries are inferred using the minimal path model, in which shape of each individual cytoplasm is reconstructed on the selected fragments set. Constructed shape is used as a constraint to locate the searching area, and curvature regulation is enforced to promote the smoothness of inference result. The inference result, in turn, is used as the shape prior to construct a high-level shape regulation energy term of the built graph, and then graph energy is updated. In other words, fragments selection and occluded boundary inference are iterative processed; this interaction makes more potential shape information accessible. Using two cervical smear datasets, the performance of our method is extensively evaluated and compared with that of the state-of-the-art approaches; the results show the superiority of the proposed method.

With widespread use of machine learning methods in numerous domains involving humans, several studies have raised questions about the potential for unfairness towards certain individuals or groups. A number of recent works have proposed methods to measure and eliminate unfairness from machine learning models. However, most of this work has focused on only one dimension of fair decision making: distributive fairness, i.e., the fairness of the decision outcomes. In this work, we leverage the rich literature on organizational justice and focus on another dimension of fair decision making: procedural fairness, i.e., the fairness of the decision making process. We propose measures for procedural fairness that consider the input features used in the decision process, and evaluate the moral judgments of humans regarding the use of these features. We operationalize these measures on two real world datasets using human surveys on the Amazon Mechanical Turk (AMT) platform, demonstrating that our measures capture important properties of procedurally fair decision making. We provide fast submodular mechanisms to optimize the tradeoff between procedural fairness and prediction accuracy. On our datasets, we observe empirically that procedural fairness may be achieved with little cost to outcome fairness, but that some loss of accuracy is unavoidable.

Many information retrieval tasks rely on high dimensional searches, including K-nearest neighbors (KNN), approximate nearest neighbors (ANN), and exact r-neighbor lookup in Hamming space. At scale, these searches are enabled by indexes on binary hashes, such as locality-sensitive hashing (LSH) and multi-indexing [1]. Recent research has flourished on these topics due to enormous growth in data volume and industry applications [2]. We present a powerful new approach to a fundamental challenge in these tasks: learning a good binary hash function. We demonstrate the effectiveness of our method by applying it to the task of automated scene matching (ASM) with a multi-index system. We call our model convolutional hashing for automated scene matching (CHASM). To the best of our knowledge, it is the first neural network to outperform state-of-the-art hash functions like Haar wavelets and color layout descriptors at ASM across the board.

Machine learning has become an important component for many systems and applications including computer vision, spam filtering, malware and network intrusion detection, among others. Despite the capabilities of machine learning algorithms to extract valuable information from data and produce accurate predictions, it has been shown that these algorithms are vulnerable to attacks. Data poisoning is one of the most relevant security threats against machine learning systems, where attackers can subvert the learning process by injecting malicious samples in the training data. Recent work in adversarial machine learning has shown that the so-called optimal attack strategies can successfully poison linear classifiers, degrading the performance of the system dramatically after compromising a small fraction of the training dataset. In this paper we propose a defence mechanism to mitigate the effect of these optimal poisoning attacks based on outlier detection. We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack. Hence, they can be detected with an appropriate pre-filtering of the training dataset.

There is a lot of new technology, a huge demand for our skills, and a bright future that promises only more work for us. Yet, this excitement is a two-edged blade. We often hear from peers about how hard it is to hire good security folks. My email box gets at least one mention a week from a peer looking for folks to bolster their team. But beyond anecdotes, we have data, as well.