6 citations · 16 across the 12 of their papers we have counts for
12 papers
Anti-causal domain generalization: Leveraging unlabeled data
Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller +3
The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing metho…
Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters
Emanuele Palumbo, Sorawit Saengkyongam, Maria R. Cervera +5
Continuous cardiovascular monitoring can play a key role in precision health. However, some fundamental cardiac biomarkers of interest, including stroke volume and cardiac output,…
Distributional Instrumental Variable Method
Anastasiia Holovchak, Sorawit Saengkyongam, Nicolai Meinshausen +1
The instrumental variable (IV) approach is commonly used to infer causal effects in the presence of unmeasured confounding. Existing methods typically aim to estimate the mean caus…
Identifying Representations for Intervention Extrapolation
Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar +2
The premise of identifiable and causal representation learning is to improve the current representation learning paradigm in terms of generalizability or robustness. Despite recent…
Model-based causal feature selection for general response types
Lucas Kook, Sorawit Saengkyongam, Anton Rask Lundborg +2
Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal featur…
Effect-Invariant Mechanisms for Policy Generalization
Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja +2
Policy learning is an important component of many real-world learning systems. A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks. Rec…
Exploiting Independent Instruments: Identification and Distribution Generalization
Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister +1
Instrumental variable models allow us to identify a causal function between covariates and a response , even in the presence of unobserved confounding. Most of the existing…
Invariant Policy Learning: A Causal Perspective
Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters +1
Contextual bandit and reinforcement learning algorithms have been successfully used in various interactive learning systems such as online advertising, recommender systems, and dyn…
Statistical Testing under Distributional Shifts
Nikolaj Thams, Sorawit Saengkyongam, Niklas Pfister +1
In this work, we introduce statistical testing under distributional shifts. We are interested in the hypothesis for a target distribution , but observe data from…
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders
Sorawit Saengkyongam, Ricardo Silva
We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders. To overcome the problem of hidden confounding, we consid…
Counterfactual Mean Embeddings
Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam +1
Counterfactual inference has become a ubiquitous tool in online advertisement, recommendation systems, medical diagnosis, and econometrics. Accurate modeling of outcome distributio…
Efficient Computation of the Stochastic Behavior of Partial Sum Processes
Sorawit Saengkyongam, Anthony Hayter, Seksan Kiatsupaibul +1
In this paper the computational aspects of probability calculations for dynamical partial sum expressions are discussed. Such dynamical partial sum expressions have many important…