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Found 13 papers, 2 papers with code
Low-Cost Privacy-Aware Decentralized Learning
This paper introduces ZIP-DL, a novel privacy-aware decentralized learning (DL) algorithm that relies on adding correlated noise to each model update during the model training process.
Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection
Among these methods, Kernel SHAP is widely used due to its model-agnostic nature and its well-founded theoretical framework.
UniRank: Unimodal Bandit Algorithm for Online Ranking
The semi-bandit version, where a full matching is sampled at each iteration, has been addressed by \cite{ADMA}, creating an algorithm with an expected regret matching $O(\frac{L\log(L)}{\Delta}\log(T))$ with $2L$ players, $T$ iterations and a minimum reward gap $\Delta$.
Unimodal Mono-Partite Matching in a Bandit Setting
The semi-bandit version, where a full matching is sampled at each iteration, has been addressed by \cite{ADMA}, creating an algorithm with an expected regret matching $O(\frac{L\log(L)}{\Delta}\log(T))$ with $2L$ players, $T$ iterations and a minimum reward gap $\Delta$.
s-LIME: Reconciling Locality and Fidelity in Linear Explanations
The benefit of locality is one of the major premises of LIME, one of the most prominent methods to explain black-box machine learning models.
Position-Based Multiple-Play Bandits with Thompson Sampling
Multiple-play bandits aim at displaying relevant items at relevant positions on a web page.
Hybrid Recommender System based on Autoencoders
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings.
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Recommendation Systems
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Hybrid Collaborative Filtering with Autoencoders
Such algorithms look for latent variables in a large sparse matrix of ratings.
AUC Optimisation and Collaborative Filtering
With this in mind, we propose a class of objective functions over matrix factorisations which primarily represent a smooth surrogate for the real AUC, and in a special case we show how to prioritise the top of the list.
Bandits Warm-up Cold Recommender Systems
Finally, experimental evidence confirm that our algorithm is effective in dealing with the cold start problem on publicly available datasets.
Online Matrix Completion Through Nuclear Norm Regularisation
It is the main goal of this paper to propose a novel method to perform matrix completion on-line.
Efficient Eigen-updating for Spectral Graph Clustering
Namely, the method promoted in this article can be viewed as an incremental eigenvalue solution for the spectral clustering method described by Ng.
Feature Selection as a One-Player Game
This paper formalizes Feature Selection as a Reinforcement Learning problem, leading to a provably optimal though intractable selection policy.
