arXiv research

New method for causal inference with complex treatment compositions.

There is tremendous interest in precision medicine as a means to improve patient outcomes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it max…

New method estimates effects of multiple nutrients on blood glucose.

The paper introduces a privacy-preserving method for estimating treatment effects that maintains accuracy.

Develops methods for causal inference in compositional data using instrumental variables.

New geometric approach for analyzing compositional data like gut microbiomes.

Paper models treatment effects by clustering patients with distinct survival characteristics.

Estimates the effect of time-varying treatments using machine learning.

Optimizes treatment duration to maximize quality-adjusted lifetime.

ML predicts alloy properties considering chemistry, processing, and data transformations.

The paper explores using historical data to improve clinical trial analysis by optimizing covariate weights.

DEBIAS learns causal effects from psychiatric longitudinal data by optimizing outcome weights.

LUQ-Learning adapts Q-learning for healthcare decisions considering patient preferences.

We develop a model to predict effects of sequential interventions, clarifying their combined impact.

Differentially private method for estimating individualized treatment rules.

Simplified calculus for semimartingales makes complex transformations easier.

Study predicts internet-based treatment effects for GPPPD based on dyadic coping.

Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifices some of its usability by adopting a block syntax, which lacks compositionality and flexible user-defined functions. Moreover, the semantic…

New method estimates hazard ratios without bias in observational studies.

Study causal effects on humans in mixed human-AI systems with unobserved unit types.

Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…

The paper extends Lie bracket to noncommutative geometry using differential operators.

Gen AI improves document understanding but not data analysis in public sector tasks.

This research improves forecasting and testing of risk contributions using Expected Shortfall.

Many complex ecosystems, such as those formed by multiple microbial taxa, involve intricate interactions amongst various sub-communities. The most basic relationships are frequently modeled as co-occurrence networks in which the nodes represent the various players in the community and the weighted edges encode levels o…

This work is an analytical and numerical study of the composition of several fractals into one and of the relation between the composite dimension and the dimensions of the component fractals. In the case of composition of standard IFS with segments of equal size, the composite dimension can be expressed as a function …

We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (…

Study on deep neural networks using branching processes and Mehler's formula.

We establish conditions for compositional generalization in machine learning.

This paper proves hyperbolicity of virtual knot compositions.

In classical field theory, the composite fibred manifolds Y -> Z -> X provides the adequate mathematical formulation of gauge models with broken symmetries, e.g., the gauge gravitation theory. This work is devoted to connections on composite fibred manifolds. In particular, we get the horizontal splitting of the vertic…

The p-index improves investment performance for NYSE stocks but not for SSE stocks.

Model predicts composite structures assembly quality with input uncertainty.

This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.

Study challenges neural models in compositional learning tasks.

SCL discovers compositional structures in analogical reasoning tasks.

This paper presents an analytical treatment of economic systems with an arbitrary number of agents that keeps track of the systems' interactions and agents' complexity. This formalism does not seek to aggregate agents. It rather replaces the standard optimization approach by a probabilistic description of both the enti…

New filters match advanced composition for adaptive privacy, with practical constants.

Paper develops momentum schemes with variance reduction for non-convex composition optimization.

Extends knockoff filter for composite null hypotheses in variable selection.

Model estimates foreign exchange reserve compositions of undisclosed central banks.

In this paper we study n-composition series of affine manifolds. One composition series are classified using gerbe theory. It is natural to think that n-composition series must be classified using n-gerbe theory. In the last section of this, we propose a notion of abelian n-gerbe theory

Study on when RLVR can learn compositional problems.

GraphITE estimates individual effects of graph-structured treatments.

A new geometry-preserving method for interpreting compositional data.

M3E2 neural network estimates multiple treatment effects.

Using quilted Floer cohomology and relative quilt invariants, we define a composition functor for categories of Lagrangian correspondences in monotone and exact symplectic Floer theory. We show that this functor agrees with geometric composition in the case that the composition is smooth and embedded. As a consequence …

Proposes a new method to estimate continuous treatment policies and match treatments effectively.