Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature. We demonstrate that SVM can be used to balance covariates and estimate average causal effects under the unconfoundedness assumption. Specifically, we adapt the SVM classifier as a kernel-based weighting procedure that minimizes the maximum mean discrepancy between the treatment and control groups while simultaneously maximizing effective sample size. We also show that SVM is a continuous relaxation of the quadratic integer program for computing the largest balanced subset, establishing its direct relation to the cardinality matching method. Another important feature of SVM is that the regularization parameter controls the trade-off between covariate balance and effective sample size. As a result, the existing SVM path algorithm can be used to compute the balance-sample size frontier. We characterize the bias of causal effect estimation arising from this trade-off, connecting the proposed SVM procedure to the existing kernel balancing methods. Finally, we conduct simulation and empirical studies to evaluate the performance of the proposed methodology and find that SVM is competitive with the state-of-the-art covariate balancing methods.

When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This paper presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations.

Quantum machine learning is an emerging field at the intersection of machine learning and quantum computing. Classical cross entropy plays a central role in machine learning. We define its quantum generalization, the quantum cross entropy, and investigate its relations with the quantum fidelity and the maximum likelihood principle. We also discuss its physical implications on quantum measurements.

Determining the dynamics of the expectation values for operators acting on a quantum many-body (QMB) system is a challenging task. Matrix product states (MPS) have traditionally been the "go-to" models for these systems because calculating expectation values in this representation can be done with relative simplicity and high accuracy. However, such calculations can become computationally costly when extended to long times. Here, we present a solution for efficiently extending the computation of expectation values to long time intervals. We utilize a multi-layer perceptron (MLP) model as a tool for regression on MPS expectation values calculated within the regime of short time intervals. With this model, the computational cost of generating long-time dynamics is significantly reduced, while maintaining a high accuracy. These results are demonstrated with operators relevant to quantum spin models in one spatial dimension.

We show that a single softmax neural net with minimal changes can beat the uncertainty predictions of Deep Ensembles and other more complex single-forward-pass uncertainty approaches. Softmax neural nets cannot capture epistemic uncertainty reliably because for OoD points they extrapolate arbitrarily and suffer from feature collapse. This results in arbitrary softmax entropies for OoD points which can have high entropy, low, or anything in between. We study why, and show that with the right inductive biases, softmax neural nets trained with maximum likelihood reliably capture epistemic uncertainty through the feature-space density. This density is obtained using Gaussian Discriminant Analysis, but it cannot disentangle uncertainties. We show that it is necessary to combine this density with the softmax entropy to disentangle aleatoric and epistemic uncertainty -- crucial e.g. for active learning. We examine the quality of epistemic uncertainty on active learning and OoD detection, where we obtain SOTA ~0.98 AUROC on CIFAR-10 vs SVHN.

Reinforcement learning in large-scale environments is challenging due to the many possible actions that can be taken in specific situations. We have previously developed a means of constraining, and hence speeding up, the search process through the use of motion primitives; motion primitives are sequences of pre-specified actions taken across a state series. As a byproduct of this work, we have found that if the motion primitives' motions and actions are labeled, then the search can be sped up further. Since motion primitives may initially lack such details, we propose a theoretically viewpoint-insensitive and speed-insensitive means of automatically annotating the underlying motions and actions. We do this through a differential-geometric, spatio-temporal kinematics descriptor, which analyzes how the poses of entities in two motion sequences change over time. We use this descriptor in conjunction with a weighted-nearest-neighbor classifier to label the primitives using a limited set of training examples. In our experiments, we achieve high motion and action annotation rates for human-action-derived primitives with as few as one training sample. We also demonstrate that reinforcement learning using accurately labeled trajectories leads to high-performing policies more quickly than standard reinforcement learning techniques. This is partly because motion primitives encode prior domain knowledge and preempt the need to re-discover that knowledge during training. It is also because agents can leverage the labels to systematically ignore action classes that do not facilitate task objectives, thereby reducing the action space.

In this paper, we formulate the problem of selecting a set of individuals from a candidate population as a utility maximisation problem. From the decision maker's perspective, it is equivalent to finding a selection policy that maximises expected utility. Our framework leads to the notion of expected marginal contribution (EMC) of an individual with respect to a selection policy as a measure of deviation from meritocracy. In order to solve the maximisation problem, we propose to use a policy gradient algorithm. For certain policy structures, the policy gradients are proportional to EMCs of individuals. Consequently, the policy gradient algorithm leads to a locally optimal solution that has zero EMC, and satisfies meritocracy. For uniform policies, EMC reduces to the Shapley value. EMC also generalises the fair selection properties of Shapley value for general selection policies. We experimentally analyse the effect of different policy structures in a simulated college admission setting and compare with ranking and greedy algorithms. Our results verify that separable linear policies achieve high utility while minimising EMCs. We also show that we can design utility functions that successfully promote notions of group fairness, such as diversity.

Authorship analysis is an important subject in the field of natural language processing. It allows the detection of the most likely writer of articles, news, books, or messages. This technique has multiple uses in tasks related to authorship attribution, detection of plagiarism, style analysis, sources of misinformation, etc. The focus of this paper is to explore the limitations and sensitiveness of established approaches to adversarial manipulations of inputs. To this end, and using those established techniques, we first developed an experimental frame-work for author detection and input perturbations. Next, we experimentally evaluated the performance of the authorship detection model to a collection of semantic-preserving adversarial perturbations of input narratives. Finally, we compare and analyze the effects of different perturbation strategies, input and model configurations, and the effects of these on the author detection model.

In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier. Previous methods for such combined clustering and classification either are classifier-specific and not generic or independently perform clustering and classifier training, which may not form clusters that can potentially benefit classifier performance. The question of how to perform clustering to improve the performance of classifiers trained on the clusters has received scant attention in previous literature despite its importance in several real-world applications. In this paper, we theoretically analyze when and how clustering may help in obtaining accurate classifiers. We design a simple, efficient, and generic framework called Classification Aware Clustering (CAC), to find clusters that are well suited for being used as training datasets by classifiers for each underlying subpopulation. Our experiments on synthetic and real benchmark datasets demonstrate the efficacy of CAC over previous methods for combined clustering and classification.

This paper introduces a procedure for testing the identifiability of Bayesian models for causal inference. Although the do-calculus is sound and complete given a causal graph, many practical assumptions cannot be expressed in terms of graph structure alone, such as the assumptions required by instrumental variable designs, regression discontinuity designs, and within-subjects designs. We present simulation-based identifiability (SBI), a fully automated identification test based on a particle optimization scheme with simulated observations. This approach expresses causal assumptions as priors over functions in a structural causal model, including flexible priors using Gaussian processes. We prove that SBI is asymptotically sound and complete, and produces practical finite-sample bounds. We also show empirically that SBI agrees with known results in graph-based identification as well as with widely-held intuitions for designs in which graph-based methods are inconclusive.