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Found 13 papers, 10 papers with code
Ask, and it shall be given: On the Turing completeness of prompting
In this work, we show that prompting is in fact Turing-complete: there exists a finite-size Transformer such that for any computable function, there exists a corresponding prompt following which the Transformer computes the function.
BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting
Multivariate Time Series (MTS) forecasting is a fundamental task with numerous real-world applications, such as transportation, climate, and epidemiology.
Fair Anomaly Detection For Imbalanced Groups
Due to the imbalanced nature between protected and unprotected groups and the imbalanced distributions of normal examples and anomalies, the learning objectives of most existing anomaly detection methods tend to solely concentrate on the dominating unprotected group.
AIM: Attributing, Interpreting, Mitigating Data Unfairness
Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals.
How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark
Secondly, to set a high-standard for efficiency evaluation, we employ a human expert to design best algorithms and implementations as our reference solutions of efficiency, many of which are much more efficient than existing canonical solutions in HumanEval and HumanEval+.
Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization
In this paper, we propose *Gradient Compressed Sensing* (GraCe), a query-efficient and accurate estimator for sparse gradients that uses only $O\big(s\log\log\frac ds\big)$ queries per step and still achieves $O\big(\frac1T\big)$ rate of convergence.
Discrete-state Continuous-time Diffusion for Graph Generation
Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design.
Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Through the utilization of both knowledge graph reasoning and LLMs, it successfully derives answers for each subquestion.
Ensuring User-side Fairness in Dynamic Recommender Systems
In the ever-evolving landscape of user-item interactions, continual adaptation to newly collected data is crucial for recommender systems to stay aligned with the latest user preferences.
Class-Imbalanced Graph Learning without Class Rebalancing
In this work, we approach the root cause of class-imbalance bias from an topological paradigm.
Reconstructing Graph Diffusion History from a Single Snapshot
They are exclusively based on the maximum likelihood estimation (MLE) formulation and require to know true diffusion parameters.
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures.
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
Recently, deep reinforcement learning (DRL) models have shown promising results in solving NP-hard Combinatorial Optimization (CO) problems.
