TSC improves causal effect estimation in panel data.
problem Estimating causal effects in panel data with a single treated unit.
method Targeted synthetic control method that refines initial weights through a one-dimensional targeted update.
result TSC consistently improves estimation accuracy over state-of-the-art SCM baselines.
Develops a method to control model behavior with synthetic data.
problem Controlling language model behavior via synthetic training data.
method Dataset Policy Gradient (DPG) to optimize synthetic data generators.
result Can cause target models to embed specific patterns or rephrase inputs.
Differentially private synthetic control estimates treatment effects while protecting privacy.
problem Estimating treatment effects on sensitive data without revealing individual information.
method Combines non-private synthetic control and differentially private empirical risk minimization.
result Private synthetic control produces accurate predictions with minimal privacy cost.
New method constructs synthetic treatment groups without mean exchangeability assumption.
problem Violations of mean exchangeability assumption in randomized controlled trials.
method Weighted mixture of treatment groups from source populations, minimizing conditional maximum mean discrepancy.
result Asymptotic normality of synthetic treatment group estimator established.
Paper proposes SiSTA for single-shot domain adaptation using target-aware generative augmentation.
problem Adapting models from source to target domains with limited target data.
method Fine-tunes a generative model on a single-shot target and uses novel sampling strategies for synthetic data.
result Improves performance by up to 20% over existing baselines in face attribute detection.
Efficient synthetic data generation improves model performance on tabular data.
problem Improving model robustness and performance with scarce or low-quality data.
method Hardness characterization to identify high-value training points, generating synthetic data only from these points.
result Synthetic data generated from hardest points outperforms non-targeted methods on tabular datasets.
The paper addresses the selection of synthetic data for improving classifier performance, focusing on the role of covariance shift.
problem The effectiveness of synthetic data in improving classifier performance is questioned, and the specific properties affecting this performance are unclear.
method The paper uses high-dimensional regression to analyze synthetic data selection, focusing on the covariance shift between synthetic and target distributions.
result The covariance shift between synthetic and target distributions affects the generalization error of classifiers, but the mean shift does not.
Paper creates fair synthetic data ensuring equal predictions across sensitive attributes.
problem Ensuring fair predictions across sensitive attributes in synthetic data.
method Equalizing target probability distributions across sensitive attributes in synthetic data generation.
result Synthetic data provides strong fair predictions, equal across all thresholds.
New strategy debiases synthetic data generated by DGMs for improved statistical inference.
problem Bias and imprecision in synthetic data generated by DGMs impede statistical convergence and inference.
method Debiasing strategy based on debiased and targeted machine learning.
result Enhanced convergence rates and accurate estimators with easily approximated variances.
The paper analyzes how synthetic data training degrades diffusion models, providing bounds and characterizing different drift regimes.
problem The degradation of performance in diffusion models trained on synthetic data.
method Theoretical analysis of score-based diffusion models, focusing on the accumulated divergence between generated and target distributions.
result Upper and lower bounds on the accumulated divergence, providing the first lower bound for diffusion models.
A novel framework synthesizes treatment data across sites using optimal transport.
problem Estimating treatment effects across different sites with varying conditions.
method Distributional causal inference, Optimal Transport for alignment of control group distributions.
result Synthetic treatment group data aligns with true target distribution under general conditions.
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses, RF systems are susceptible to attacks masking true target characteristics from accurate identification. In this work, we evaluate differen…
GnIES recovers causal structure from unknown interventions.
problem Recovering causal structure from unknown interventions.
method Characterization of equivalence classes, greedy learning algorithm GnIES.
result GnIES recovers the equivalence class of the data-generating model.
Bayesian algorithm discovers synthetic routes from target molecules.
problem Identifying synthetic routes from desired products.
method Bayesian inference and combinatorial optimization.
result Algorithm rediscovered 80.3% and 50.0% of known synthetic routes.
The paper shows how training with synthetic data can lead to model improvement, not degradation, under certain conditions.
problem Model collapse in iterative training on contaminated sources.
method Statistical analysis of iterative training on a mixture of true and synthetic data.
result Training with synthetic data can lead to model improvement, not degradation, under specific conditions.
Proposes a method to correct for covariate shift in meta-analysis of randomized trials.
problem Invalidation of standard IPD meta-analysis due to covariate shift across studies.
method Placebo-anchored transport framework that treats source-trial outcomes as proxy signals and target-trial placebo outcomes as gold labels.
result Yields target-identified effect estimates in connected targets and a principled screen--then--transport procedure in disconnected targets.
Copula-based method generates synthetic populations from marginal distributions.
problem Generating realistic synthetic populations from limited data.
method Copula-based framework for population synthesis.
result Copula framework enhances transferability and realism of synthetic populations.
GenFormer uses deep learning to generate complex stochastic data.
problem Creating synthetic stochastic data that matches real-world statistical properties.
method Transformer-based deep learning model that maps Markov state sequences to time series values.
result GenFormer preserves target marginal distributions and other statistical properties in multivariate spatio-temporal data.
MAZE attacks a model without data, using synthetic inputs.
problem Model Stealing attacks that require data to replicate a target model.
method MAZE uses zeroth-order gradient estimation to create synthetic inputs.
result MAZE achieves high clone accuracy without data.
GraphTEE estimates treatment effects on graph-structured targets, mitigating bias.
problem Understanding treatment effects on graph-structured targets with observational bias.
method GraphTEE framework focusing on confounding variable sets and new regularization.
result GraphTEE mitigates bias better than previous methods.
New method for valid prediction intervals in counterfactual outcomes with runtime confounding.
problem Valid prediction intervals for counterfactual outcomes under runtime confounding.
method Debiased machine learning framework grounded in semiparametric efficiency theory.
result Prediction intervals achieve desired coverage rates with faster convergence compared to standard methods.
Proposes a new method to improve target annotation in ATR.
problem Challenges in annotating automatic target recognition due to lack of labeled data.
method Hybrid contrastive learning and cycle-consistency-based transductive transfer learning (C3TTL) framework.
result Significantly lower Fréchet Inception Distance (FID) score and improved performance in annotating civilian and military vehicles, as well as ship targets.
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
problem Designing efficient drugs for novel viral proteins.
method End-to-end framework combining VAE, controlled sampling, and predictors.
result Highly selective and affinity molecules for SARS-CoV-2 targets.
Insurance firms use RL to optimize customer offers for desired target portfolios.
problem Optimizing insurance offers to achieve a desired customer portfolio.
method Developed a novel reinforcement learning algorithm.
result The RL algorithm outperforms traditional methods in a synthetic market.
A novel approach using graph learning and synthetic long positions for statistical arbitrage in options markets.
problem Exploiting statistical arbitrage opportunities in options markets using machine learning.
method Two-stage graph learning approach: first stage defines a novel prediction target isolating pure arbitrages via synthetic bonds; second stage proposes SLSA positions.
result Statistically significant outperformance of GL baselines and consistent positive returns with an average P&L-contract information ratio of 0.1627.
A new flow optimizes synthetic data alignment for generative modeling.
problem Optimizing synthetic data to match target distributions.
method Score difference (SD) flow to reduce KL divergence.
result SD flow provides a theoretical link between generative models.
Paper tackles missing data shifts in domains, showing how to adapt models.
problem Missing data mechanisms differ between source and target domains.
method Introduced DAMS, showing covariate shift under certain conditions, and providing adjustments for missingness.
result Simple adjustments can improve target model performance even when missingness rates vary.
Framework generates precise synthetic populations for scalable modeling.
problem Generating accurate synthetic populations without personal data.
method Constraint-programming framework encoding aggregated statistics and structural relations.
result Exact control of demographic profiles without requiring microdata.
ClusterSC improves synthetic control by selecting relevant donor groups.
problem The curse of dimensionality in synthetic control with individual-level data.
method ClusterSC incorporates clustering to select relevant donor groups.
result ClusterSC consistently outperforms classical SC approaches.
A model learns causal representations from high-dimensional data.
problem Challenges in learning causal representations from high-dimensional data.
method Formulated a latent variable decoder model, Decoder BCD, for Bayesian causal discovery.
result Shows that using known intervention targets as labels helps in unsupervised Bayesian inference over structure and parameters.
Synthetic tabular data synthesis models balance utility and risk.
problem Generating synthetic tabular data for regulated domains.
method Latent flow models with various learning targets, paths, and sampling methods.
result Velocity and posterior matching objectives yield higher utility, while score and noise matching achieve lower risk.
We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards i…
Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose D…
New method predicts model performance under selection bias in healthcare.
problem Selection bias limits model generalizability in healthcare.
method Proposes a novel upper bound method for estimating model performance.
result Validates and demonstrates the practical utility of the method.
Analyzes factors affecting flow VI performance.
problem Consistent performance of flow VI across studies.
method Step-by-step analysis of capacity, objectives, batchsize, estimators, and step-sizes.
result Specific recommendations and a flow VI recipe.
ReTaSA tackles continuous target shift in regression problems.
problem Continuous target shift in regression settings.
method Nonparametric regularized approach to estimate importance weight function.
result The method provides theoretical justification for the estimated importance weight function.
We propose kernel sequential Monte Carlo (KSMC), a framework for sampling from static target densities. KSMC is a family of sequential Monte Carlo algorithms that are based on building emulator models of the current particle system in a reproducing kernel Hilbert space. We here focus on modelling nonlinear covariance s…
AIS algorithm improves heavy-tailed distribution estimation.
problem Inconsistent estimators and slow convergence in AIS for heavy-tailed distributions.
method Adapts Student-t proposal distributions by matching escort moments and minimizing α-divergence.
result Improves estimation accuracy for heavy-tailed distributions.
The paper presents a framework to quantify the trade-off between synthetic and real data.
problem Improving generalization with synthetic data when real data is scarce.
method Learning-theoretic framework leveraging algorithmic stability to derive generalization error bounds.
result Optimal synthetic-to-real data ratio minimizing expected test error as a function of Wasserstein distance.
ICYM2I corrects missingness bias in multimodal learning.
problem Missingness patterns between source and target environments affect multimodal learning performance.
method ICYM2I uses inverse probability weighting to correct missingness bias in predictive performance and information gain.
result ICYM2I improves multimodal learning performance by accounting for missingness.
DAIS minimizes symmetrized KL divergence between initial and target distributions.
problem Optimizing over initial distributions in importance sampling.
method Differentiable annealed importance sampling (DAIS) minimizing symmetrized KL divergence.
result DAIS minimizes symmetrized KL divergence between initial and target distributions.
Two synthetic likelihood methods learn EBM of likelihood from simulator data for SBI.
problem Conduct inference from experimental observations using high-fidelity simulators.
method Learn conditional EBM of likelihood using synthetic data conditioned on parameters.
result Learned likelihood combined with prior yields posterior estimate for sampling.
Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. However, none of these methods outperforms the others for all problems. This motivates the development of automatic approachesto recommend the mos…
Breaks circular dependency in synthetic option pricing with a novel model.
problem Circular dependency in implied volatility limits synthetic data for machine learning and risk analysis.
method Uses a Jump-Hidden Markov Model to generate price paths and a modified Heston process to convert paths into implied volatility.
result Framework generates realistic synthetic American option prices without external calibration.
Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challenging machine learning task. Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase lo…
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution. While absent assumptions, domain adaptation is impossible, strict conditions, e.g. covariate or label shift, enable principled algorithms. Recently-proposed domain-adversa…
TVineSynth generates synthetic data to balance privacy and utility.
problem Balancing privacy and utility in synthetic data generation.
method Uses vine copula with truncation to control privacy and utility trade-off.
result Achieves superior privacy-utility balance compared to competitors.
Improved sampling efficiency for inverse problems using variance-reduced diffusion methods.
problem Efficiently estimating noisy scores in inverse problems.
method Developed a nonparametric self-normalized importance sampling estimator and a state-dependent blending rule.
result Improved sample quality for fixed simulation budgets in synthetic targets and PDE-governed inverse problems.