We address the individualized treatment effect (ITE) estimation problem, focusing on continuous, multidimensional, and time-dependent treatments for precision medicine. The central challenge lies in modeling these complex treatment scenarios while capturing dynamic patient responses and minimizing reliance on control data. We propose the Gaussian Mixture Counterfactual Generator (GMCG), a generative model that transforms the Gaussian mixture model—traditionally a tool for clustering and density estimation—into a new tool explicitly geared toward causal inference. This approach generates robust counterfactuals by effectively handling continuous and multidimensional treatment spaces. We evaluate GMCG on synthetic crossover trial data and simulated datasets, demonstrating its superior performance over existing methods, particularly in scenarios with limited control data. GMCG derives its effectiveness from modeling the joint distribution of covariates, treatments, and outcomes using a latent state vector while employing a conditional distribution of the state vector to suppress confounding and isolate treatment-outcome relationships.

Estimating individual treatment effects in clinical data is essential for understanding how different patients uniquely respond to treatments and identifying the most effective interventions for specific patient subgroups, thereby enhancing the precision and personalization of healthcare. However, counterfactual data are not accessible, and the true calculation of causal effects cannot be performed at the individual level. This paper proposes a linear algebraic framework to generate counterfactual longitudinal data that exactly matches pre-treatment factual data. Because causation travels forward in time, not in reverse, counterfactual predictability is further strengthened by blocking causal effects from flowing back to the past, thus limiting counterfactual dependence on the future. Using simulated LDL cholesterol datasets, we show that our method significantly outperforms the most cited methods of counterfactual generation. We also provide a formula that can estimate the time-varying variance of individual treatment effects, interpreted as a confidence level in the generated counterfactuals compared to true values.

Radial basis function (RBF) kernels for SVM have been routinely used in a wide range of classification problems, delivering consistently good performance for those problems where the kernel computations are numerically feasible (high-dimensional problems typically use linear kernels). One of the drawbacks of RBF kernels is the necessity of selecting the proper value of the hyperparameter γ in addition to the standard SVM penalty parameter C — this process can lead to overfitting. Another (more obscure) drawback of RBF is its inherent non-optimality as an approximation function. In order to address these issues, we propose to extend the concept of polynomial splines (designed explicitly for approximation purposes) to multidimensional normalized splines with infinite number of knots and use the resulting hyperparameter-free kernel SVMs instead of RBF kernel SVMs. We tested our approach for a number of standard classification datasets used in the literature. The results suggest that new kernels deliver mostly better classification performance than RBF kernel (for problems of moderately large dimensions), but allow faster computation (if measured on large cross-validation grids), with less chance of overfitting.

In secure data management the inference problem occurs when data classified at a high security level becomes inferrible from data classified at lower levels. We present a model-theoretic approach to this problem that captures the epistemic state of the database user as a set of possible worlds or models. Privacy is enforced by requiring the existence of k > 1 models assigning distinct values to sensitive attributes, and implemented via model counting. We provide an algorithm mechanizing this process and show that it is sound and complete for a large class of queries.

Due to its obvious importance, fault detection and localization is a well-studied problem in communication networks, as attested by the many techniques designed to address this problem. The inherent variability, limited component reliability, and constrained resources of MANETs (Mobile Ad hoc Networks) make the problem not just more important, but also critical. Practical development and deployment considerations imply that fault detection and localization methods must i) avoid relying on overly detailed models of network protocols and traffic assumptions and instead rely on actual cross-layer measurements/observations, and ii) be applicable across different network scales and topologies with minimum adjustments. This paper demonstrates the feasibility of such goals, and proposes an important and as yet unexplored approach to fault management in MANETs: network-invariant fault detection, localization and diagnosis with limited knowledge of the underlying network and traffic models. We show how fault management methods can be derived by observing statistical network/traffic measurements in one network, and subsequently applied to other networks with satisfactory performance. We demonstrate that a carefully designed but widely applicable set of local and weak global indicators of faults can be efficiently aggregated to produce highly sensitive and specific methods that perform well when applied to MANETs with varying sizes, topologies, and traffic matrices.

Computer networks exist to provide a communication medium for social networks, and information from social networks can help in estimating their communication needs. Despite this, current network management ignores the information from social networks. On the other hand, due to their limited and fluctuating bandwidth, mobile ad hoc networks are inherently resource-constrained. As traffic load increases, we need to decide when and how to throttle the traffic to maximize user satisfaction while keeping the network operational. The state-of-the-art for making these decisions is based on network measurements and so employs a reactive approach to deteriorating network state by reducing the amount of traffic admitted into the network. However, a better approach is to avoid congestion before it occurs, by (a) monitoring the network for early onset signals of congestive phase transition, and (b) predicting future network traffic using user and application information from the overlaying social network. We use machine learning methods to predict the amount of traffic load that can be admitted without transitioning the network to a congestive phase and to predict the source and destination of near future traffic load. These two predictions when fed into an admission control component ensure better management of constrained network resources while maximizing the quality of user experience.

Due to their limited and fluctuating bandwidth, mobile ad hoc networks (MANETs) are inherently resource-constrained. As traffic load increases, we need to decide when to throttle the traffic to maximize user satisfaction while keeping the network operational. The state-of-the-art for making these decisions is based on network measurements and so employs a reactive approach to deteriorating network state by reducing the amount of traffic admitted into the network. However, a better approach is to avoid congestion before it occurs by predicting future network traffic using user and application information from the overlaying social network. We use machine learning methods to predict the source and destination of near future traffic load.

In this paper we consider a new paradigm of learning: learning using hidden information. The classical paradigm of the supervised learning is to learn a decision rule from labeled data (xi, yi), xiisin X, yiisin {-1, 1}, i = 1,hellip, lscr. In this paper we consider a new setting: given training vectors in space X along with labels and description of this data in another space X*, find in space X a decision rule better than the one found in the classical paradigm.

We present a method for automatically extracting groups of orthologous genes from a large set of genomes by a new clustering algorithm on a weighted multipartite graph. The method assigns a score to an arbitrary subset of genes from multiple genomes to assess the orthologous relationships between genes in the subset. This score is computed using sequence similarities between the member genes and the phylogenetic relationship between the corresponding genomes. An ortholog cluster is found as the subset with the highest score, so ortholog clustering is formulated as a combinatorial optimization problem. The algorithm for finding an ortholog cluster runs in time O(absolute value(E) + absolute value(V) log absolute value(V)), where V and E are the sets of vertices and edges, respectively, in the graph. However, if we discretize the similarity scores into a constant number of bins, the runtime improves to O(absolute value(E) + absolute value(V)). The proposed method was applied to seven complete eukaryote genomes on which the manually curated database of eukaryotic ortholog clusters, KOG, is constructed. A comparison of our results with the manually curated ortholog clusters shows that our clusters are well correlated with the existing clusters.