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Found 18 papers, 8 papers with code
StablePCA: Learning Shared Representations across Multiple Sources via Minimax Optimization
To address this challenge, we employ the Fantope relaxation, reformulating the problem as a convex minimax optimization, with the objective defined as the maximum loss across sources.
Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
Pursuing invariant prediction from heterogeneous environments opens the door to learning causality in a purely data-driven way and has several applications in causal discovery and robust transfer learning.
Causal Invariance Learning via Efficient Optimization of a Nonconvex Objective
To address both challenges, we focus on the additive intervention regime and propose nearly necessary and sufficient conditions for ensuring that the invariant prediction model matches the causal outcome model.
STT: Stateful Tracking with Transformers for Autonomous Driving
In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately.
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset.
Inference for Nonlinear Endogenous Treatment Effects Accounting for High-Dimensional Covariate Complexity
Using the control function approach for identification, we implement a regularized nonparametric estimation to obtain an initial estimator of the model.
Distributionally Robust Transfer Learning
Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data.
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
To address such potential distribution shifts, we develop an unsupervised domain adaptation approach that leverages labeled data from multiple source domains and unlabeled data from the target domain.
Federated Learning
Multi-Source Unsupervised Domain Adaptation
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Datasets and Benchmarks for Offline Safe Reinforcement Learning
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.
TSCI: two stage curvature identification for causal inference with invalid instruments
TSCI implements a two-stage algorithm.
Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE
Videos depict the change of complex dynamical systems over time in the form of discrete image sequences.
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment.
On the Robustness of Safe Reinforcement Learning under Observational Perturbations
One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward.
A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
The novelty of the proposed test is that it allows the number of covariates and instruments to be larger than the sample size.
Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
The success of TSCI requires the instrumental variable's effect on treatment to differ from its violation form.
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction
Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors.
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies
Integrative analysis of data from multiple sources is critical to making generalizable discoveries.
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding
Inferring causal relationships or related associations from observational data can be invalidated by the existence of hidden confounding.
Methodology Statistics Theory Statistics Theory
