On-device research index

arXiv research

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.

168,695 papers · 148 categories

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48 results for “soft causal learning”

Scalable Contrastive Causal Discovery under Unknown Soft Interventionsstat.ML

Model identifies causal structure from paired observational and interventional data with unknown soft interventions.

problem Identifying causal structure from observational and interventional data with unknown soft interventions.

method Proposes a scalable causal discovery model that aggregates subset-level PDAGs and applies contrastive cross-regime orientation rules.

result The model asymptotically recovers the identifiable PDAG and can orient additional edges compared to non-contrastive subset-restricted methods.

causal discovery soft interventions Markov equivalence contrastive learning scalable methods

2026-03-03abs ↗pdf ↗

Graph Agnostic Causal Bayesian Optimisationcs.LG

GACBO optimizes unknown causal graphs with interventions.

problem Optimizing a target variable on an unknown causal graph with interventions.

method Graph Agnostic Causal Bayesian Optimisation (GACBO) seeks to balance exploitation and exploration of causal structures and functions.

result GACBO outperforms baselines in simulated and real-world applications.

causal Bayesian optimization cumulative regret unknown causal graph soft and hard interventions graph discovery

2024-11-05abs ↗pdf ↗

Soft causal learning for generalized molecule property prediction: An environment perspectivecs.LG

Framework tackles OOD challenges in molecule property prediction by modeling environments.

problem Challenges in modeling OOD samples for molecule property prediction.

method Soft causal learning framework incorporating chemistry theories and cross-attention mechanisms.

result Demonstrates well generalization ability on seven datasets.

molecule property prediction OOD challenges soft causal learning graph neural networks environment modeling

2025-05-07abs ↗pdf ↗

Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applicationsstat.ML

Bayesian approach learns causal concepts from diverse social surveys.

problem Inferring causal concepts from heterogeneous data with sparse changes.

method Hierarchical Bayesian model with sequential Monte Carlo sampling.

result Model infers meaningful causal concepts and plausible relations.

Bayesian inference causal representation learning heterogeneous data social surveys soft interventions

2026-06-04abs ↗pdf ↗

Linear Causal Bandits: Unknown Graph and Soft Interventionsstat.ML

This paper tackles unknown causal graphs and soft interventions, establishing regret bounds and an efficient algorithm.

problem Designing causal bandit algorithms with unknown causal graphs and stochastic intervention models.

method Establishes novel regret bounds and presents a computationally efficient algorithm for unknown graph and soft interventions.

result Regret bounds for unknown graph and soft interventions, with a universal minimax lower bound.

causal bandits unknown graph soft interventions regret bounds minimax lower bound

2024-11-04abs ↗pdf ↗

Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learningcs.LG

In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyz…

2017-09-19abs ↗pdf ↗

Linear Causal Representation Learning from Unknown Multi-node Interventionscs.LG

This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.

problem CRL under unknown multi-node interventions, focusing on single-node assumptions.

method Establishes identifiability results for general latent causal models under stochastic interventions.

result Identifiability up to ancestors using soft interventions, perfect identifiability using hard interventions.

Causal Representation Learning Multi-node Interventions Identifiability Linear Transformation Score Functions

2024-06-09abs ↗pdf ↗

Differentiable Constraint-Based Causal Discoverycs.LG

New method combines gradient optimization with constraint-based techniques for causal discovery.

problem Causal discovery from observational data, especially with small sample sizes.

method Differentiable

d

-separation scores using percolation theory and soft logic for gradient-based optimization of conditional independence constraints.

result Empirical evaluations show robust performance in low-sample regimes, surpassing traditional methods.

causal discovery gradient optimization percolation theory conditional independence soft logic

2025-10-24abs ↗pdf ↗

Causal Deep Reinforcement Learning Using Observational Datacs.LG

Paper proposes methods to use observational data for reinforcement learning, addressing confounding issues.

problem Using observational data for reinforcement learning can lead to misleading outcomes due to unobserved confounders.

method The paper introduces two deconfounding methods in deep reinforcement learning to adjust for confounders.

result The proposed deconfounding methods improve the accuracy of reinforcement learning models using observational data.

reinforcement learning observational data deconfounding causal inference deep Q-learning

2022-11-28abs ↗pdf ↗

The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionsstat.ME

Bayesian method for inferring causal relations from mixed observational and interventional data.

problem Challenges in causal inference from heterogeneous study conditions with unknown soft interventions.

method Interventional Bayesian Gaussian equivalent score for a mixture of observational and interventional data.

result Effective characterisation of posterior distributions of causal structures and intervention effects.

Bayesian causal inference soft interventions mixed data posterior distributions experimental studies

2022-05-05abs ↗pdf ↗

Causal Bandits with General Causal Models and Interventionsstat.ML

The paper tackles causal bandits with unknown SCMs and soft interventions, providing upper and lower bounds on regret.

problem Optimizing interventions in a causal system with unknown SCMs and soft interventions.

method Assumes unknown SCMs from a general class, allows infinite interventions, and provides upper and lower bounds on regret.

result General upper and lower bounds on cumulative achievable regret for various SCMs.

causal bandits general SCMs soft interventions regret bounds graph parameters

2024-03-01abs ↗pdf ↗

Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approachstat.ML

New framework optimizes decisions under uncertainty considering causal and continuous data.

problem Optimizing decisions under uncertain distributions with causal and continuous data structures.

method Developed a framework using Causal Sinkhorn DRO with Soft Regression Forest decision rules.

result Framework provides interpretable and tractable decision rules for optimizing under uncertainty.

distributionally robust optimization causal inference continuous data interpretability soft regression forest

2026-01-16abs ↗pdf ↗

Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguitycs.LG

Study identifies latent variables and causal relationships from multiple environments.

problem Identify latent variables and causal relationships from multiple environments.

method Proposes algorithm LiNGCReL for identifying causal graph up to surrounded-node ambiguity.

result Identifies latent variables up to surrounded-node ambiguity (SNA) in linear causal models.

causal representation learning identifiability general environments surrounded-node ambiguity linear causal models

2023-11-21abs ↗pdf ↗

Learning to Defer for Causal Discovery with Imperfect Expertscs.LG

L2D-CD learns to defer expert recommendations in causal discovery.

problem Combining expert knowledge with data-driven results in causal discovery when expert recommendations may contradict data.

method Adapting learning-to-defer algorithms for pairwise causal discovery, L2D-CD learns a deferral function to select between expert recommendations and data-driven methods.

result L2D-CD outperforms both causal discovery methods and the expert used in isolation, identifying domains where the expert's performance is strong or weak.

causal discovery expert recommendations learning to defer pairwise causal discovery superior performance

2025-02-18abs ↗pdf ↗

PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discoverystat.ML

PRCD-MAP learns to trust imperfect priors in causal discovery, improving accuracy and robustness.

problem Tackles the brittle trade-off between blind trust and rejection of external priors in causal discovery.

method Proposes PRCD-MAP, a soft prior-consumption layer that assigns per-edge trust to imperfect priors and modulates regularization in a MAP objective.

result Enjoys a population-level safety guarantee and outperforms existing methods on real-world causal discovery tasks.

causal discovery prior consumption soft priors trust calibration regularization

2026-05-03abs ↗pdf ↗

Inverse Reinforcement Learning with Explicit Policy Estimatescs.LG

This work connects IRL methods from ML and economics.

problem Solving the inverse reinforcement learning problem.

method Shows connections and differences between various IRL methods.

result Identifies key computational and algorithmic differences.

Inverse Reinforcement Learning Maximum Causal Entropy Nested Fixed Point Algorithm Conditional Choice Probability Soft Value Function

2021-03-04abs ↗pdf ↗

Score-based Causal Representation Learning with Interventionsstat.ML

Paper recovers latent causal structure and linear transformation from indirect observations.

problem Recovering latent causal structure and linear transformation from indirect observations.

method Established sufficient conditions for DAG recovery, leveraged score function properties, and used soft/hard interventions.

result Perfect recovery of latent DAG structure and linear transformation up to scaling using soft interventions, hard interventions with additional hypothesis testing.

causal representation learning latent transformation interventional score DAG recovery two-layer neural networks

2023-01-19abs ↗pdf ↗

Representation of Context-Specific Causal Models with Observational and Interventional Datamath.ST

Introduces CStrees for modeling context-specific causal models from observational and interventional data.

problem Modeling context-specific causal relationships from mixed data types.

method Introduces CStrees with a novel factorization criterion and graphical characterization for context-specific conditional independence models.

result Derives a graphical characterization of model equivalence for observational CStrees and extends it to CStree models under context-specific interventions.

Causal Models Context-Specific Conditional Independence Interventional Data Observational Data

2021-01-22abs ↗pdf ↗

Identifying Best Fair Interventioncs.CY

Study fairness in intervention to maximize outcomes.

problem Fairness in intervention on a given node.

method Counterfactual estimation with partial causal model knowledge.

result Theoretical guarantees on error probability and effectiveness of algorithm.

fairness intervention causal model counterfactual marketplace

2021-11-08abs ↗pdf ↗

Linear causal disentanglement via higher-order cumulantsstat.ML

New method identifies latent variables with causal dependencies from observed data.

problem Identify latent variables with causal relationships from observed data.

method Linear causal disentanglement via higher-order cumulants, with perfect and soft interventions.

result Recovery of parameters via coupled tensor decomposition and polynomial equations.

causal inference latent variable models higher-order cumulants tensor decomposition polynomial equations

2024-07-05abs ↗pdf ↗

Conditional independences and causal relations implied by sets of equationscs.AI

New approach reveals causal and probabilistic relationships from equations.

problem Understanding causal and probabilistic relationships from sets of equations.

method Simon's causal ordering algorithm and Markov ordering graph construction.

result Implied conditional independences and causal relations without solving equations.

equations causal ordering Markov ordering interventions conditional independences

2020-07-14abs ↗pdf ↗

Score-based Causal Representation Learning: Linear and General Transformationscs.LG

This paper tackles causal representation learning with linear and general transformations.

problem Identify and recover latent causal variables and graphs under unknown transformations.

method Score-based algorithms that use gradients of log-density functions for identifiability and achievability.

result Two stochastic hard interventions per node are sufficient for identifiability of general transformations.

causal representation learning score-based methods latent causal variables identifiability achievability

2024-02-01abs ↗pdf ↗

Cyclic Counterfactuals under Shift-Scale Interventionscs.AI

Study counterfactuals in cyclic systems with shifts and scales.

problem Counterfactual inference in cyclic systems with shifts and scales.

method Shift-scale interventions in cyclic SCMs.

result Valid inference in cyclic systems with shifts and scales.

counterfactuals cyclic SCMs shift-scale interventions structural causal models

2025-10-28abs ↗pdf ↗

Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescs.LG

It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper, we develop a framework for causal discovery from such data…

2019-03-05abs ↗pdf ↗

Identifiability Guarantees for Causal Disentanglement from Soft Interventionsstat.ML

Paper shows identifiability of causal models with unobserved variables.

problem Identify latent variables in causal models with unobserved variables.

method Developed an autoencoding variational Bayes algorithm.

result Identifiability achieved with generalized faithfulness assumptions.

causal disentanglement latent variables interventional data autoencoding variational Bayes

2023-07-12abs ↗pdf ↗

Learning Mixtures of Unknown Causal Interventionsstat.ML

New method disentangles mixed interventional and observational data in SEMs.

problem Learning causal relationships from mixed interventional and observational data.

method Developed a method to disentangle mixed interventional and observational data in linear SEMs with Gaussian noise.

result The method can identify causal graphs up to their interventional Markov Equivalence Class.

Causal Discovery Interventional Data Linear SEMs Gaussian Noise Markov Equivalence

2024-10-31abs ↗pdf ↗

Causal Bandits for Linear Structural Equation Modelsstat.ML

The paper tackles causal bandits for SEMs, proposing algorithms that avoid estimating $2^N$ reward distributions.

problem Designing an optimal sequence of interventions in causal graphical models to minimize cumulative regret.

method Proposes two algorithms for causal bandits for linear structural equation models (SEMs), avoiding the estimation of

2^N

reward distributions.

result Cumulative regrets scale as

ilde{\cal O} (d^{L+\frac{1}{2}} \sqrt{NT})

under bounded noise and parameter space.

causal bandits linear structural equation models interventions cumulative regret regression

2022-08-26abs ↗pdf ↗

Hard and Soft EM in Bayesian Network Learning from Incomplete Datastat.ML

Paper compares hard and soft EM for BN learning from incomplete data.

problem Learning BNs from incomplete data using EM algorithms.

method Investigates the impact of imputation vs. belief propagation in hard and soft EM.

result A decision tree can guide practitioners in choosing the best EM algorithm.

Bayesian networks Expectation-Maximization Incomplete data Belief propagation Imputation

2020-12-09abs ↗pdf ↗

PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstractioncs.LG

PLOT uses optimal transport to find neural site handles for causal abstraction.

problem Finding the relevant neural site for causal analysis is computationally challenging.

method PLOT employs optimal transport to localize causal variables from neural network outputs.

result PLOT efficiently finds intervention handles for causal abstraction in neural networks.

causal abstraction neural networks optimal transport localization counterfactual intervention

2026-05-07abs ↗pdf ↗

Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networkscs.LG

Soft labeling impacts OOD detection in neural networks.

problem Impact of soft labeling on OOD detection in deep neural networks.

method Empirical analysis of how soft labeling affects OOD detection performance.

result Soft labeling can either improve or deteriorate OOD detection performance.

soft labeling out-of-distribution detection deep neural networks machine learning safety

2020-07-07abs ↗pdf ↗

Scalable Intervention Target Estimation in Linear Modelsstat.ME

Paper proposes scalable algorithm to estimate intervention targets in linear models.

problem Estimating intervention targets in linear models from observational and interventional data.

method The paper proposes a scalable algorithm that estimates intervention sites from the difference between precision matrices of observational and interventional datasets.

result The algorithm consistently identifies all intervention targets and updates observational Markov equivalence classes to interventional ones.

causal inference linear models interventional data precision matrices Markov equivalence

2021-11-15abs ↗pdf ↗

Multi-task and few-shot learning in virtual flow meteringstat.ML

The paper develops a multi-unit soft sensing model for virtual flow meters that improves few-shot learning.

problem Improving soft sensor performance for new wells with limited data.

method Formulates a probabilistic, hierarchical model using a deep neural network for multi-unit soft sensing.

result Multi-unit models trained on many wells can perform well on new wells with just a few data points.

soft sensors virtual flow meters multi-unit learning few-shot learning transferability

2023-09-27abs ↗pdf ↗

Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learningcs.LG

Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe instability when training on large off-policy data or cannot scale to tasks with very high state and action dimensionality such as 3D humanoid l…

2019-09-07abs ↗pdf ↗

A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processesstat.ML

Deep model learns from labeled and unlabeled data for industrial soft sensing.

problem Lack of data limits soft sensor development in industrial processes.

method Hierarchical, generative deep latent variable model for semi-supervised multi-unit soft sensing.

result Model outperforms current methods for soft sensing in industrial processes.

soft sensing semi-supervised learning multi-unit industrial processes latent variable model

2024-07-18abs ↗pdf ↗

Stationary Reweighting Yields Local Convergence of Soft Fitted Q-Iterationstat.ML

New method improves stability of soft FQI for offline RL.

problem Stability issues in soft FQI under function approximation.

method Stationary reweighting to align operator norms.

result Local linear convergence proved under certain conditions.

reinforcement learning offline learning soft FQI stationary reweighting local convergence

2025-12-30abs ↗pdf ↗

Soft Autoencoder and Its Wavelet Adaptation Interpretationcs.LG

Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpretability. As a successful unsupervised model in deep learning, the autoencoder embraces a wide spectrum of applications, yet it suffers fro…

2018-12-31abs ↗pdf ↗

Soft Rule Ensembles for Statistical Learningstat.ML

In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are then obtained with logistic regression from the corresponding hard rules. In order to deal with the p…

2012-05-21abs ↗pdf ↗

Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networkscs.LG

LPINNs solve complex PDEs by reformulating PINNs on Lagrangian frame, reducing training complexity.

problem Complexity in training PINNs, especially for convection-diffusion equations.

method Propose LPINNs, a Lagrangian reformulation of PINNs, with two branches solving state variables and characteristics curves.

result Loss landscapes of LPINNs are less sensitive to problem complexity compared to traditional PINNs.

Physics-informed neural networks PINNs Lagrangian formulation Convection-diffusion equations Training complexity

2022-05-05abs ↗pdf ↗

Root Cause Analysis of Outliers with Missing Structural Knowledgestat.ML

Paper tackles anomaly detection with missing causal knowledge.

problem Detect anomalies with missing structural knowledge.

method Simple, efficient methods for polytree causal graphs.

result Heuristic identifies root causes based on anomaly scores.

Root Cause Analysis Anomaly Detection Polytree Causal Graph Missing Structural Knowledge Efficient Methods

2024-06-07abs ↗pdf ↗

Learning Representations for Soft Skill Matchingcs.CL

Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…

2018-07-20abs ↗pdf ↗

COLAM: Co-Learning of Deep Neural Networks and Soft Labels via Alternating Minimizationcs.LG

Improves deep neural networks using soft labels through alternating minimization.

problem Improving deep neural networks training with soft labels.

method Co-Learns DNNs and soft labels via Alternating Minimization of two objectives.

result COLAM achieves improved performance on many tasks with better testing classification accuracy.

deep neural networks soft labels alternating minimization classification accuracy

2020-04-26abs ↗pdf ↗

Logistic Regression as Soft Perceptron Learningstat.ML

We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the "soft" variant of perceptron learning.

2017-08-24abs ↗pdf ↗

Learning Soft Labels via Meta Learningcs.LG

A framework learns dynamic soft labels to improve model generalization and accuracy.

problem Models trained on one-hot labels overfit and are sensitive to noisy annotations.

method Proposes a framework where labels are treated as learnable parameters, adapting dynamically during optimization.

result Consistent gains across different datasets and architectures, improving ResNet18 by 2.1% on CIFAR100.

soft labels dynamic learning meta learning supervised classification generalization

2020-09-20abs ↗pdf ↗

Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revisionstat.ML

A method for selecting pseudo-labeled data in semi-supervised learning using generalized Bayes and soft revision.

problem Selecting pseudo-labeled data for semi-supervised learning with robustness to uncertainty.

method Using credal sets and the Gamma-Maximin method with soft revision to update priors and select pseudo-labeled data.

result The Gamma-Maximin method with soft revision can achieve promising results, especially in scenarios with low labeled data proportions.

semi-supervised learning pseudo-label selection Gamma-Maximin soft revision credal sets

2024-05-24abs ↗pdf ↗

Improving Deep Ensembles by Estimating Confusion Matricescs.LG

Proposes a new method for deep ensembles that improves accuracy and calibration.

problem Improving accuracy and calibration of deep ensembles.

method Estimates confusion matrices of ensemble members and weighs them according to their inferred performance.

result Empirically shows superiority of soft Dawid Skene over ensemble averaging.

deep learning ensemble methods crowdsourcing confusion matrices soft labels

2025-03-10abs ↗pdf ↗

Meta Soft Label Generation for Noisy Labelscs.CV

MSLG generates soft labels to improve DNN performance on noisy datasets.

problem Significant performance degradation of DNNs due to noisy labels.

method Meta-learning techniques to estimate optimal label distribution and iteratively update soft labels.

result MSLG outperforms state-of-the-art methods by a large margin on various datasets.

Meta Learning Soft Labels Deep Neural Networks Noisy Labels CIFAR10

2020-07-11abs ↗pdf ↗

Identifying Best Interventions through Online Importance Samplingstat.ML

Motivated by applications in computational advertising and systems biology, we consider the problem of identifying the best out of several possible soft interventions at a source node $V$ in an acyclic causal directed graph, to maximize the expected value of a target node $Y$ (located downstream of $V$ ). Our setting im…

2017-01-10abs ↗pdf ↗

Soft-Robust Algorithms for Batch Reinforcement Learningcs.LG

New algorithms optimize a soft-robust criterion in reinforcement learning, reducing conservatism.

problem Computing robust policies for high-stakes decisions with limited data.

method Soft-robust criterion using risk measures, two algorithms for optimization.

result Our algorithms produce less conservative solutions than existing methods.

reinforcement learning robust policies soft-robust criterion percentile criterion risk measures

2020-11-30abs ↗pdf ↗