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Found 15 papers, 6 papers with code
Collective Certified Robustness against Graph Injection Attacks
We investigate certified robustness for GNNs under graph injection attacks.
Revisiting Demonstration Selection Strategies in In-Context Learning
In this work, we first revisit the factors contributing to this variance from both data and model aspects, and find that the choice of demonstration is both data- and model-dependent.
XAI for In-hospital Mortality Prediction via Multimodal ICU Data
To address this issue, this paper proposes an eXplainable Multimodal Mortality Predictor (X-MMP) approaching an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data.
LLaRA: Large Language-Recommendation Assistant
Treating the "sequential behaviors of users" as a distinct modality beyond texts, we employ a projector to align the traditional recommender's ID embeddings with the LLM's input space.
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence.
Large Language Model Can Interpret Latent Space of Sequential Recommender
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items.
Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees
In this paper, we propose a randomly projected convex clustering model for clustering a collection of $n$ high dimensional data points in $\mathbb{R}^d$ with $K$ hidden clusters.
Invariant Collaborative Filtering to Popularity Distribution Shift
Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios.
Understanding Deep Learning via Decision Boundary
From the theoretical view, two lower bounds based on algorithm DB variability are proposed and do not explicitly depend on the sample size.
Differentiable Invariant Causal Discovery
This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.
Decision boundary variability and generalization in neural networks
Two new notions, algorithm DB variability and $(\epsilon, \eta)$-data DB variability, are proposed to measure the decision boundary variability from the algorithm and data perspectives.
Learning Intents behind Interactions with Knowledge Graph for Recommendation
In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).
A dual Newton based preconditioned proximal point algorithm for exclusive lasso models
In addition, we derive the corresponding HS-Jacobian to the proximal mapping and analyze its structure --- which plays an essential role in the efficient computation of the PPA subproblem via applying a semismooth Newton method on its dual.
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
The perfect recovery properties of the convex clustering model with uniformly weighted all pairwise-differences regularization have been proved by Zhu et al. (2014) and Panahi et al. (2017).
An Efficient Semismooth Newton Based Algorithm for Convex Clustering
Clustering may be the most fundamental problem in unsupervised learning which is still active in machine learning research because its importance in many applications.
