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Found 13 papers, 2 papers with code
Scene-Driven Multimodal Knowledge Graph Construction for Embodied AI
Currently, knowledge base for embodied tasks is missing and most existing work use general knowledge base or pre-trained models to enhance the intelligence of an agent.
Assessing Electricity Service Unfairness with Transfer Counterfactual Learning
Energy justice is a growing area of interest in interdisciplinary energy research.
INTAGS: Interactive Agent-Guided Simulation
To adapt to any environment with interactive sequential decision making agents, INTAGS formulates the simulator as a stochastic policy in reinforcement learning.
Transfer Learning for Causal Effect Estimation
We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data.
Causal Structural Learning from Time Series: A Convex Optimization Approach
Structural learning, which aims to learn directed acyclic graphs (DAGs) from observational data, is foundational to causal reasoning and scientific discovery.
Causal Graph Discovery from Self and Mutually Exciting Time Series
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series.
Online Kernel CUSUM for Change-Point Detection
We present a computationally efficient online kernel Cumulative Sum (CUSUM) method for change-point detection that utilizes the maximum over a set of kernel statistics to account for the unknown change-point location.
Optimal Sub-sampling to Boost Power of Kernel Sequential Change-point Detection
We present a novel scheme to boost detection power for kernel maximum mean discrepancy based sequential change-point detection procedures.
Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes
Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have elucidated prodromes (i. e., event chains) that can be used to identify and intercept the adverse event early in its course.
Causal Graph Discovery from Self and Mutually Exciting Time Series
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series.
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization
Numerous empirical evidences have corroborated the importance of noise in nonconvex optimization problems.
Inferring serial correlation with dynamic backgrounds
Sequential data with serial correlation and an unknown, unstructured, and dynamic background is ubiquitous in neuroscience, psychology, and econometrics.
Statistics Theory Methodology Statistics Theory
Goodness-of-Fit Test for Mismatched Self-Exciting Processes
Recently there have been many research efforts in developing generative models for self-exciting point processes, partly due to their broad applicability for real-world applications.
