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Found 9 papers, 2 papers with code
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
We study distributionally robust offline reinforcement learning (robust offline RL), which seeks to find an optimal robust policy purely from an offline dataset that can perform well in perturbed environments.
Robust Consensus Clustering and its Applications for Advertising Forecasting
Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions.
Video Background Music Generation: Dataset, Method and Evaluation
To close this gap, we introduce a dataset, benchmark model, and evaluation metric for video background music generation.
Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach
Specifically, we propose a two-stage estimator based on the instrumental variables and establish its statistical properties in the confounded MDPs with a linear structure.
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
We study offline reinforcement learning (RL) in partially observable Markov decision processes.
GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection
In this paper, we reveal and address the disadvantages of the conventional query-driven HOI detectors from the two aspects.
Ranked #6 on
Human-Object Interaction Detection
on HICO-DET
Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization
Many existing algorithms learn robust policies by modeling the disturbance and applying it to source environments during training, which usually requires prior knowledge about the disturbance and control of simulators.
Mining the Benefits of Two-stage and One-stage HOI Detection
To this end, we propose a novel one-stage framework with disentangling human-object detection and interaction classification in a cascade manner.
Ranked #5 on
Human-Object Interaction Detection
on V-COCO
Bayesian Time Series Forecasting with Change Point and Anomaly Detection
However, their performance is easily undermined by the existence of change points and anomaly points, two structures commonly observed in real data, but rarely considered in the aforementioned methods.
