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Found 11 papers, 5 papers with code
DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
In the development stage, DS-Agent follows the CBR framework to structure an automatic iteration pipeline, which can flexibly capitalize on the expert knowledge from Kaggle, and facilitate consistent performance improvement through the feedback mechanism.
Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation
Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness.
CausalCite: A Causal Formulation of Paper Citations
Evaluating the significance of a paper is pivotal yet challenging for the scientific community.
On the Interventional Kullback-Leibler Divergence
Modern machine learning approaches excel in static settings where a large amount of i. i. d.
Dataflow graphs as complete causal graphs
Component-based development is one of the core principles behind modern software engineering practices.
Out-of-Variable Generalization for Discriminative Models
The ability of an agent to do well in new environments is a critical aspect of intelligence.
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning
Recent works have shown the potential of diffusion models in computer vision and natural language processing.
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
However, constrained by the limited quality of the offline dataset, its performance is often sub-optimal.
Learning Generalizable Agents via Saliency-Guided Features Decorrelation
Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations.
Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties
Extensive experiments with two benchmark datasets QM9 and ZINC250k show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fr\'echet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models.
