A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Machine learning models trained on indirect data labels can fail on real-world examples.
Unified framework for learning with indirect supervision signals.
New method for learning indirectly through control variables.
Estimates causal effects using machine learning for binary treatment and mediator.
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…
PLRM synthesizes labels from mismatched sources for better training sets.
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…
This paper learns prior models from indirect data efficiently.
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 …
AI detects 38% NFT trades likely manipulated, improving on indirect methods.
Paper tackles RUL prediction with scarce data using indirect supervision.
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…
The study tackles indirect discrimination in insurance pricing models.
Paper proposes an algorithm to learn DAGs with indirect dependencies.
This paper studies communication efficiency in federated learning by optimizing the sum-rate-distortion function for indirect multiterminal source coding.
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.
This paper tackles structure learning in indirect observations of Gaussian and non-Gaussian random vectors.
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…
Study shows cooperation can improve everyone's market efficiency.
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
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…
Researchers show how to secretly train models with hidden data, detect usage with high confidence.
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…
The objective optimization of medical imaging systems requires full characterization of all sources of randomness in the measured data, which includes the variability within the ensemble of objects to-be-imaged. This can be accomplished by establishing a stochastic object model (SOM) that describes the variability in t…
End-to-end algorithm for controlling bilinear systems with probabilistic noise.
Graph convolutional neural networks, which learn aggregations over neighbor nodes, have achieved great performance in node classification tasks. However, recent studies reported that such graph convolutional node classifier can be deceived by adversarial perturbations on graphs. Abusing graph convolutions, a node's cla…
We present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to …
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…
New benchmark PVR tests neural network reasoning about indirection.
New method estimates corporate default probabilities using indirect data.
MediEncoder learns nonlinear representations for causal mediation analysis.
Study relaxes identification assumptions for natural direct effects in non-randomized settings.
Study short-term wind power and speed predictions using machine learning.
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…
Efficient algorithm for learning from indirect feedback in complex decision-making scenarios.
Direct neural network calibration outperforms indirect method for rough volatility models.
Optimal reinsurance contracts designed for a continuum of risk types.
This study measures price risk aversion using indirect utility functions in a lab experiment.
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…
This paper studies how to capture dependency graph structures from real data which may not be Gaussian. Starting from marginal loss functions not necessarily derived from probability distributions, we utilize an additive over-parametrization with shrinkage to incorporate variable dependencies into the criterion. An ite…
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…
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…
A simple banking network model is proposed which features multiple waves of bank defaults and is analytically solvable in the limiting case of an infinitely large homogeneous network. The model is a collection of nodes representing individual banks; associated with each node is a balance sheet consisting of assets and …
A multi-task network avoids indirect discrimination in insurance pricing.
Paper develops models to forecast private equity fund cash flows.