arXiv research

Unified framework for estimating indirect effects in observational studies with unmeasured confounding.

Paper tackles RUL prediction with scarce data using indirect supervision.

Estimates causal effects using machine learning for binary treatment and mediator.

Extends causal inference to hidden mediators with proxies.

Researchers show how to secretly train models with hidden data, detect usage with high confidence.

The study tackles indirect discrimination in insurance pricing models.

Study stability of trading strategy under market perturbations.

Study relaxes identification assumptions for natural direct effects in non-randomized settings.

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness…

Machine learning models trained on indirect data labels can fail on real-world examples.

Unified framework for learning with indirect supervision signals.

We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of…

Proposes a new approach to approximate maximum likelihood for complex models.

PLRM synthesizes labels from mismatched sources for better training sets.

We address the problem of gauging the influence exerted by a given country on the global trade market from the viewpoint of complex networks. In particular, we apply the PWP method for computing indirect influences on the world trade network.

Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. In this paper, we classify RL into direct and indirect RL according to how they seek the optimal policy of the Markov decision process problem. The former solves the optimal poli…

Study shows cooperation can improve everyone's market efficiency.

This paper studies the problem of {\em learning} the probability distribution $P_X$ of a discrete random variable $X$ using indirect and sequential samples. At each time step, we choose one of the possible $K$ functions, $g_1, \ldots, g_K$ and observe the corresponding sample $g_i(X)$. The goal is to estimate the proba…

New methods resolve conflicting treatment effect estimates in health tech assessments.

This study compares direct and indirect methods for estimating own funds in life insurance, finding indirect methods more effective under realistic asset-liability coupling.

The relationship between international trade and foreign direct investment (FDI) is one of the main features of globalization. In this paper we investigate the effects of FDI on trade from a network perspective, since FDI takes not only direct but also indirect channels from origin to destination countries because of f…

Weakly-supervised learning is a paradigm for alleviating the scarcity of labeled data by leveraging lower-quality but larger-scale supervision signals. While existing work mainly focuses on utilizing a certain type of weak supervision, we present a probabilistic framework, learning from indirect observations, for learn…

New method for learning indirectly through control variables.

Indirect attacks can fool graph classifiers even with poisoned neighbors.

A semi-parametric, non-linear regression model in the presence of latent variables is applied towards learning network graph structure. These latent variables can correspond to unmodeled phenomena or unmeasured agents in a complex system of interacting entities. This formulation jointly estimates non-linearities in the…

A multi-task network avoids indirect discrimination in insurance pricing.

Develops exact and invariant study-based decompositions for network meta-analysis.

Financial markets are exposed to systemic risk, the risk that a substantial fraction of the system ceases to function and collapses. Systemic risk can propagate through different mechanisms and channels of contagion. One important form of financial contagion arises from indirect interconnections between financial insti…

This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, the mapping from the continuous latent factors to the observed signal is parametrized by a convolutional neural network. The alternating bac…

New methods estimate interventional effects with multiple mediators using machine learning.

This paper learns prior models from indirect data efficiently.

AI detects 38% NFT trades likely manipulated, improving on indirect methods.

Proposes a new estimator for causal mediation with continuous treatments.

In this paper we develop a new form of agent-based model for limit order books based on heterogeneous trading agents, whose motivations are liquidity driven. These agents are abstractions of real market participants, expressed in a stochastic model framework. We develop an efficient way to perform statistical calibrati…

New method quantifies intrinsic causal contributions in neural networks.

Transformer pretraining yields strong EB performance without explicit adaptation.

We study the ever more integrated and ever more unbalanced trade relationships between European countries. To better capture the complexity of economic networks, we propose two global measures that assess the trade integration and the trade imbalances of the European countries. These measures are the network (or indire…

Driven by the goal to enable sleep apnea monitoring and machine learning-based detection at home with small mobile devices, we investigate whether interpretation-based indirect knowledge transfer can be used to create classifiers with acceptable performance. Interpretation-based indirect knowledge transfer means that a…

This paper tackles structure learning in indirect observations of Gaussian and non-Gaussian random vectors.

Many questions of fundamental interest in todays science can be formulated as inference problems: Some partial, or noisy, observations are performed over a set of variables and the goal is to recover, or infer, the values of the variables based on the indirect information contained in the measurements. For such problem…

Paper proposes an algorithm to learn DAGs with indirect dependencies.

New benchmark PVR tests neural network reasoning about indirection.

This paper studies communication efficiency in federated learning by optimizing the sum-rate-distortion function for indirect multiterminal source coding.

Proposes a graph dynamics prior for more accurate relational inference.

Unobserved confounding is a major hurdle for causal inference from observational data. Confounders---the variables that affect both the causes and the outcome---induce spurious non-causal correlations between the two. Wang & Blei (2018) lower this hurdle with "the blessings of multiple causes," where the correlation st…

New method estimates corporate default probabilities using indirect data.

Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are necessary to infer the true underlying spiking activity from fluorescence measurements. Bayesian model inversion can be used to solve this prob…

A method to assess sensitivity to unmeasured confounding with sharp bounds.