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Found 10 papers, 6 papers with code
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions.
How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-Oriented (CGO) problems.
Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework
Large language models (LLMs) can easily generate biased and discriminative responses.
Hindsight Learning for MDPs with Exogenous Inputs
Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker.
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix.
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How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?
Our framework measures a network's robustness via the predictive power in its representations -- the test performance of a linear model trained on the learned representations using a small set of clean labels.
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e. g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features.
Understanding Generalization in Deep Learning via Tensor Methods
Recently proposed complexity measures have provided insights to understanding the generalizability in neural networks from perspectives of PAC-Bayes, robustness, overparametrization, compression and so on.
Tensorial Neural Networks: Generalization of Neural Networks and Application to Model Compression
We propose tensorial neural networks (TNNs), a generalization of existing neural networks by extending tensor operations on low order operands to those on high order ones.
