Search Results for author:
Found 19 papers, 8 papers with code
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
We propose a novel Invariant Neighborhood Pattern Learning (INPL) to alleviate the distribution shifts problem on non-homophilous graphs.
The FinBen: An Holistic Financial Benchmark for Large Language Models
This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs.
Learning to Reweight for Graph Neural Network
In this paper, we study the problem of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings.
Simple and Asymmetric Graph Contrastive Learning without Augmentations
Experimental results show that the simple GraphACL significantly outperforms state-of-the-art graph contrastive learning and self-supervised learning methods on homophilic and heterophilic graphs.
Let Models Speak Ciphers: Multiagent Debate through Embeddings
Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary.
Learning How to Propagate Messages in Graph Neural Networks
To compensate for this, in this paper, we present learning to propagate, a general learning framework that not only learns the GNN parameters for prediction but more importantly, can explicitly learn the interpretable and personalized propagate strategies for different nodes and various types of graphs.
Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models
We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
PIE: Simulating Disease Progression via Progressive Image Editing
Disease progression simulation is a crucial area of research that has significant implications for clinical diagnosis, prognosis, and treatment.
On the Tool Manipulation Capability of Open-source Large Language Models
In this paper, we ask can we enhance open-source LLMs to be competitive to leading closed LLM APIs in tool manipulation, with practical amount of human supervision.
IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System
The aforementioned two trends enable the device-cloud collaborative and dynamic recommendation, which deeply exploits the recommendation pattern among cloud-device data and efficiently characterizes different instances with different underlying distributions based on the cost of frequent device-cloud communication.
MAP: Towards Balanced Generalization of IID and OOD through Model-Agnostic Adapters
To achieve this goal, we apply a bilevel optimization to explicitly model and optimize the coupling relationship between the OOD model and auxiliary adapter layers.
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization
DUET is deployed on a powerful cloud server that only requires the low cost of forwarding propagation and low time delay of data transmission between the device and the cloud.
Knowledge Distillation of Transformer-based Language Models Revisited
In the past few years, transformer-based pre-trained language models have achieved astounding success in both industry and academia.
Decoupled Self-supervised Learning for Non-Homophilous Graphs
This paper studies the problem of conducting self-supervised learning for node representation learning on graphs.
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders
In this paper, we propose a theoretical understanding of why existing unbiased learning objectives work for unbiased recommendation.
Minimizing Memorization in Meta-learning: A Causal Perspective
Meta-learning has emerged as a potent paradigm for quick learning of few-shot tasks, by leveraging the meta-knowledge learned from meta-training tasks.
Adaptive Adversarial Training for Meta Reinforcement Learning
Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task.
Pareto Self-Supervised Training for Few-Shot Learning
While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data.
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning
It is of significance for an agent to learn a widely applicable and general-purpose policy that can achieve diverse goals including images and text descriptions.
