Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation

• URL: http://arxiv.org/abs/2608.03647v1
• Date: Tue, 04 Aug 2026 13:30:51 GMT
• Title: Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation
• Abstract summary: Out-of-distribution (OOD) recommendation is vulnerable to preference shifts induced by a latent environment.<n>We formulate this task as conditionally identifiable risk-aware recommendation (CI-RR)<n>We propose Conditionally Identifiable Latent-Environment Recommendation (CILER)
• Score: 17.941409894004636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Out-of-distribution (OOD) recommendation is vulnerable to preference shifts induced by a latent environment. Existing methods can infer latent states from logged interactions, yet the statistical meaning of the latent environment and its effect on preference remain underdetermined. We formulate this task as conditionally identifiable risk-aware recommendation (CI-RR) and propose Conditionally Identifiable Latent-Environment Recommendation (CILER). CILER uses a user-conditioned exponential family to model the latent environment and a feature-indexed polynomial to specify how it changes preference. It predicts by marginalizing item probabilities over the inferred environment distribution. Under sufficient variation, correct specification, and decoder regularity, CILER identifies the environment-sensitive representation up to the stated equivalence class. We further bound excess deployment log-risk by environment-inference error. Controlled studies test the observable consequences of sufficient variation and model specification. Experiments on three datasets show that CILER improves all twelve OOD ranking metrics under feature, temporal, and geographical shifts within shared support.

Related papers

• Robust Regression with Adaptive Contamination in Response: Optimal Rates and Computational Barriers [65.58071581164043]
  We study robust regression under a contamination model in which covariates are clean while the responses may be corrupted in an adaptive manner.<n>We show that the additional information can be carefully exploited to construct an estimator that a better estimation rate than achieves that attainable under Huber contamination.<n>Despite the improvement over the Huber model from an information-theoretic perspective, we provide formal evidence that the problem exhibits strong information-computation gaps.
  arXiv  Detail & Related papers  (2026-04-05T19:07:24Z)
• Distributed Dynamic Invariant Causal Prediction in Environmental Time Series [9.779350203438485]
  We propose Distributed Dynamic Invariant Causal Prediction in Time-series (DisDy-ICPT)<n>DisDy-ICPT learns dynamic causal relationships over time while mitigating spatial confounding variables without requiring data communication.<n> Empirical evaluations on synthetic benchmarks and environment-segmented real-world datasets show that DisDy-ICPT achieves superior predictive stability and accuracy compared to baseline methods A and B.
  arXiv  Detail & Related papers  (2026-03-03T11:52:35Z)
• Environment-Adaptive Covariate Selection: Learning When to Use Spurious Correlations for Out-of-Distribution Prediction [33.69413571438309]
  We show that out-of-distribution prediction fails when only a subset of the true causes of the outcome is observed.<n>We propose an environment-adaptive covariate selection algorithm.<n>EACS consistently outperforms static causal, invariant, and ERM-based predictors under diverse distribution shifts.
  arXiv  Detail & Related papers  (2026-01-05T18:13:02Z)
• Certifiably Robust Policies for Uncertain Parametric Environments [57.2416302384766]
  We propose a framework based on parametric Markov decision processes (MDPs) with unknown distributions over parameters.<n>We learn and analyse IMDPs for a set of unknown sample environments induced by parameters.<n>We show that our approach produces tight bounds on a policy's performance with high confidence.
  arXiv  Detail & Related papers  (2024-08-06T10:48:15Z)
• Nonstationary Time Series Forecasting via Unknown Distribution Adaptation [22.915008205203886]
  As environments evolve, temporal distribution shifts can degrade time series forecasting performance.<n>Some methods disentangle stationary and nonstationary components by assuming uniform distribution shifts, but it is impractical since when the distribution changes is unknown.<n>We propose the textbfUnknown textbfDistribution textbfAdaptation (textbfUDA) model for nonstationary time series forecasting.
  arXiv  Detail & Related papers  (2024-02-20T07:16:12Z)
• Prediction under Latent Subgroup Shifts with High-Dimensional Observations [30.433078066683848]
  We introduce a new approach to prediction in graphical models with latent-shift adaptation. Our novel form of RPM identifies causal latent structure in the source environment, and adapts properly to predict in the target.
  arXiv  Detail & Related papers  (2023-06-23T12:26:24Z)
• Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification [29.827191184889898]
  This paper proposes a deep generative model (DGM) for simultaneous range error mitigation and environment identification. Experiments on a general Ultra-wideband dataset demonstrate the superior performance on range error mitigation, scalability to different environments, and novel capability on simultaneous environment identification.
  arXiv  Detail & Related papers  (2023-05-23T10:16:22Z)
• Free Lunch for Domain Adversarial Training: Environment Label Smoothing [82.85757548355566]
  We propose Environment Label Smoothing (ELS) to improve training stability, local convergence, and robustness to noisy environment labels. We yield state-of-art results on a wide range of domain generalization/adaptation tasks, particularly when the environment labels are highly noisy.
  arXiv  Detail & Related papers  (2023-02-01T02:55:26Z)
• Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling [77.15766509677348]
  Conditional generative models often inherit spurious correlations from the training dataset. This can result in label-conditional distributions that are imbalanced with respect to another latent attribute. We propose a general two-step strategy to mitigate this issue.
  arXiv  Detail & Related papers  (2022-12-05T08:09:33Z)
• Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift [50.98086766507025]
  We propose a simple-yet-effective data augmentation strategy, Adversarial Invariant Augmentation (AIA) AIA aims to extrapolate and generate new environments, while concurrently preserving the original stable features during the augmentation process.
  arXiv  Detail & Related papers  (2022-11-05T07:55:55Z)
• Differentiable Invariant Causal Discovery [106.87950048845308]
  Learning causal structure from observational data is a fundamental challenge in machine learning. This paper proposes Differentiable Invariant Causal Discovery (DICD) to avoid learning spurious edges and wrong causal directions. Extensive experiments on synthetic and real-world datasets verify that DICD outperforms state-of-the-art causal discovery methods up to 36% in SHD.
  arXiv  Detail & Related papers  (2022-05-31T09:29:07Z)
• Learning Conditional Invariance through Cycle Consistency [60.85059977904014]
  We propose a novel approach to identify meaningful and independent factors of variation in a dataset. Our method involves two separate latent subspaces for the target property and the remaining input information. We demonstrate on synthetic and molecular data that our approach identifies more meaningful factors which lead to sparser and more interpretable models.
  arXiv  Detail & Related papers  (2021-11-25T17:33:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.