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Found 18 papers, 7 papers with code
Optimizing Case-Based Reasoning System for Functional Test Script Generation with Large Language Models
To achieve this, we propose a case-based reasoning (CBR) system utilizing a 4R cycle (i. e., retrieve, reuse, revise, and retain), which maintains and leverages a case bank of test intent descriptions and corresponding test scripts to facilitate LLMs for test script generation.
M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery
This paper introduces M$^{3}$-20M, a large-scale Multi-Modal Molecule dataset that contains over 20 million molecules, with the data mainly being integrated from existing databases and partially generated by large language models.
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
We provide a unified framework, termed Identifiable Exchangeable Mechanisms (IEM), for representation and structure learning under the lens of exchangeability.
Do Finetti: On Causal Effects for Exchangeable Data
We study causal effect estimation in a setting where the data are not i. i. d.
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs
By contrast, certain instruction-tuning leads to similar performance changes irrespective of training on different data, suggesting a lack of domain understanding across different skills.
DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
In this work, we investigate the potential of large language models (LLMs) based agents to automate data science tasks, with the goal of comprehending task requirements, then building and training the best-fit machine learning models.
CausalCite: A Causal Formulation of Paper Citations
Thus, we propose CausalCite, a new way to measure the significance of a paper by assessing the causal impact of the paper on its follow-up papers.
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.
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.
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.
Out-of-Variable Generalization for Discriminative Models
The ability of an agent to do well in new environments is a critical aspect of intelligence.
Dataflow graphs as complete causal graphs
Component-based development is one of the core principles behind modern software engineering practices.
On the Interventional Kullback-Leibler Divergence
Modern machine learning approaches excel in static settings where a large amount of i. i. d.
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
We then present our main identifiability theorem, which shows that given data from an ICM generative process, its unique causal structure can be identified through performing conditional independence tests.
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.
