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…
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48 results for “Indirect Observations”
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
problem Challenges in evaluating indirect effects due to unmeasured confounding and unethical exposures.
method Developed a unified identification and estimation framework using proximal causal inference.
result Unified identification and estimation of PIIE and causal effect of an intervening variable in settings with pervasive unmeasured confounding.
This paper learns prior models from indirect data efficiently.
problem Learning prior models from indirect data in Bayesian inversion.
method Generative model of prior as pushforward of Gaussian in latent space, learned by minimizing loss function.
result Efficient residual-based neural operator approximation for forward model learning.
Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learningecon.EM
Estimates causal effects using machine learning for binary treatment and mediator.
problem Estimating direct and indirect quantile treatment effects under selection-on-observables.
method Double/debiased machine learning estimators based on efficient score functions.
result Uniform consistency and asymptotic normality of effect estimators.
This paper tackles structure learning in indirect observations of Gaussian and non-Gaussian random vectors.
problem Learning the graphical structure of random vectors indirectly observed through a sensing matrix and corrupted noise.
method Parametric and non-parametric approaches for Gaussian and non-Gaussian distributions, respectively.
result Correct graphical structure can be recovered under indefinite sensing systems with insufficient samples.
Develops exact and invariant study-based decompositions for network meta-analysis.
problem Lack of exact contribution decompositions in network meta-analysis.
method Contrast-space projection formulation of NMA, study-based definition of direct and indirect evidence.
result Exact covariance-aware decompositions of NMA estimator into direct and indirect contributions.
This paper studies the problem of {\em learning} the probability distribution of a discrete random variable using indirect and sequential samples. At each time step, we choose one of the possible functions, and observe the corresponding sample . The goal is to estimate the proba…
New method handles indirect mediators in CMA for complex scenarios.
problem Handling indirect and multi-dimensional mediators in causal mediation analysis.
method Identifiable Variational Autoencoder (iVAE) architecture for multi-dimensional, indirectly observed mediators.
result Accurate estimation of direct and mediated effects in synthetic and semi-synthetic experiments.
This paper studies communication efficiency in federated learning by optimizing the sum-rate-distortion function for indirect multiterminal source coding.
problem Indirect multiterminal source coding in federated learning where edge devices send noisy gradients to the server.
method Analyzes the rate region for the quadratic vector Gaussian CEO problem under unbiased estimator and derives an explicit formula for the sum-rate-distortion function.
result Derives an explicit formula for the sum-rate-distortion function in the special case of identical gradients over edge devices.
We investigate the dynamic stability of the indirect utility process associated with a (possibly suboptimal) trading strategy under perturbations of the market. Establishing the reverse conjugacy characterizations first, we prove continuity and first-order convergence of the indirect-utility process under simultaneous …
Paper tackles RUL prediction with scarce data using indirect supervision.
problem Predicting RUL with indirect supervision and scarce time series data.
method Unified framework called parameterized static regression, handling data scarcity without interpolation.
result Competitive performance in prediction accuracy with simulated data scarcity.
Extends causal inference to hidden mediators with proxies.
problem Identifying causal effects with hidden mediators and error-prone proxies.
method Established causal hidden mediation analysis and hidden front-door criterion.
result Identification of population intervention indirect effect possible with hidden mediators.
Validity problems in clinical machine learning by indirect data labeling using consensus definitionscs.LG
Machine learning models trained on indirect data labels can fail on real-world examples.
problem Validity issues in machine learning when target labels are indirectly defined.
method Identification of problematic datasets and models using a general procedure.
result Machine learning models trained on indirect data labels will fail on real-world examples.
Unified framework for learning with indirect supervision signals.
problem Learning from indirect supervision signals when gold labels are missing or costly.
method Developed a unified theoretical framework for multi-class classification with variable supervision.
result Introduced the concept of separation to characterize learnability and generalization bounds.
PLRM synthesizes labels from mismatched sources for better training sets.
problem Creating labeled training sets is a major challenge in machine learning.
method PLRM uses probabilistic modeling to synthesize labels from indirect supervision sources with different output spaces.
result PLRM outperforms baselines by 2%-9% on various tasks.
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…
New method recovers signals from noisy indirect data, even when noise is uncertain.
problem Recovering signals from indirect observations with uncertain noise.
method Polyhedral estimates, incorporating convex optimization.
result Presumably good estimates can be constructed for ellitope signal sets.
Study shows cooperation can improve everyone's market efficiency.
problem Understanding when collective cooperation improves market efficiency.
method General semimartingale framework, deriving necessary and sufficient conditions.
result Strict improvement in each agent's indirect utility when cooperation is beneficial.
New methods resolve conflicting treatment effect estimates in health tech assessments.
problem Conflicting conclusions from different sponsors analyzing the same data.
method Arbitrated indirect treatment comparisons (ArMAIC) targeting a common target population.
result Estimates treatment effects in a common target population, resolving the MAIC paradox.
This study compares direct and indirect methods for estimating own funds in life insurance, finding indirect methods more effective under realistic asset-liability coupling.
problem Computing own funds for life insurers using direct and indirect methods in a risk-neutral pricing framework.
method Introduced a novel family of mixed estimators including both direct and indirect methods, integrated into a control variate framework for variance reduction.
result The indirect method is more effective under realistic asset-liability coupling, but neither method is universally superior.
In nonlinear state-space models, sequential learning about the hidden state can proceed by particle filtering when the density of the observation conditional on the state is available analytically (e.g. Gordon et al., 1993). This condition need not hold in complex environments, such as the incomplete-information equili…
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…
Indirect Active Learningmath.ST
New method for learning indirectly through control variables.
problem Learning relationships when direct manipulation of variables is impossible.
method Study of indirect active learning under nonparametric models with fixed budget.
result Minimax rates for estimating relationships between variables.
Study sparse function recovery from indirect noisy observations using -regularization.
problem Recovering sparse functions from indirect, noisy observations.
method Proposes an -regularized empirical risk minimizer and analyzes its statistical properties.
result Established almost-sure consistency and derived high-probability convergence rates in prediction and norms.
Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direct…
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…
Study short-term wind power and speed predictions using machine learning.
problem Accurate short-term wind power and speed predictions for energy systems.
method Combining numerical weather prediction models with local observations, using machine learning for variable selection and forecasting.
result Improved wind power and speed predictions for 4-hour ahead using machine learning.
The goal of personalized decision making is to map a unit's characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pat…
AI detects 38% NFT trades likely manipulated, improving on indirect methods.
problem Detecting crypto wash trading using indirect methods and leaked data.
method Public NFT data analysis, direct estimation, AI-based estimator.
result AI reduces estimation errors in NFT markets, improving on indirect methods.
The study tackles indirect discrimination in insurance pricing models.
problem Indirect discrimination in insurance pricing models.
method Presented a statistical model free of proxy discrimination.
result The canonical price in the model does not satisfy group fairness axioms.
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…
New method identifies causes in time series with latent variables.
problem Identifying direct and indirect causes in time series data with hidden variables.
method Proves necessary and sufficient conditions for causal feature selection using graph constraints and conditional independence tests.
result Method outperforms Granger causality in identifying causes with low false positives and false negatives.
We develop a general theory for the goodness-of-fit test to non-linear models. In particular, we assume that the observations are noisy samples of a submanifold defined by a \yao{sufficiently smooth non-linear map}. The observation noise is additive Gaussian. Our main result shows that the "residual" of the model fit, …
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…
A new approach to unsupervised learning using recognition-parametrised models.
problem Discovering meaningful latent structure in observational data.
method Recognition-Parametrised Model (RPM) combining parametric and non-parametric components.
result Effective learning of latent structure without explicit generative models.
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…
Paper proposes an algorithm to learn DAGs with indirect dependencies.
problem Learning DAGs misses indirect dependencies in local variables.
method Two-phase algorithm using high-order HSIC for local optimization.
result OT algorithm outperforms existing methods in structure estimation.
New benchmark PVR tests neural network reasoning about indirection.
problem Understanding neural network generalization limits.
method Introducing Pointer Value Retrieval (PVR) benchmark.
result Large variations in performance across different conditions.
This paper explores how to choose scoring rules for estimating properties with parametric assumptions.
problem Indirect elicitation of properties with parametric assumptions.
method Developed a framework for choosing proper scoring rules for indirect elicitation, considering constraints and optimal solutions.
result The optimal estimation of the target property changes monotonically with the increase of each weight, and often setting some weights as zero yields the best configuration.
New method estimates corporate default probabilities using indirect data.
problem Lack of direct default rate data for corporate companies.
method Modeling default probability dynamics using Bank of Russia overdue debt data.
result Validated method produces trustworthy default probability series.
Study relaxes identification assumptions for natural direct effects in non-randomized settings.
problem Identifying causal direct effects under unmeasured confounding.
method Developed relaxed conditions for identifying natural direct effects in non-randomized settings.
result Identified natural direct effect under unmeasured confounding conditions.
In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective and intractability of even a single gradient computation. In this paper, we bypass both obstacles for a class of what we call linear indirectl…
In classical contagion models, default systems are Markovian conditionally on the observation of their stochastic environment, with interacting intensities. This necessitates that the environment evolves autonomously and is not influenced by the history of the default events. We extend the classical literature and allo…
Researchers show how to secretly train models with hidden data, detect usage with high confidence.
problem Protecting training data from traceability in large language models.
method Gradient-based optimization to learn secret sequences absent from training data.
result Secret sequences can be learned by models without performance degradation, detectable with high confidence.
Networks capture our intuition about relationships in the world. They describe the friendships between Facebook users, interactions in financial markets, and synapses connecting neurons in the brain. These networks are richly structured with cliques of friends, sectors of stocks, and a smorgasbord of cell types that go…
A model predicts influential nodes in complex networks by considering indirect interactions.
problem Identifying influential nodes in complex networks using indirect interactions.
method Proposes MOGen, a multi-order generative model that considers all indirect influences up to a maximum distance.
result MOGen consistently outperforms network models and path-based approaches in predicting influential nodes.