Discriminative Adversarial Unlearning
We introduce a novel machine unlearning framework founded upon the established principles of the min-max optimization paradigm. We capitalize on the capabilities of strong Membership Inference Attacks...
View ArticleLiRank: Industrial Large Scale Ranking Models at LinkedIn
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods. We unveil several modeling improvements,...
View ArticleFor Better or For Worse? Learning Minimum Variance Features With Label...
Data augmentation has been pivotal in successfully training deep learning models on classification tasks over the past decade. An important subclass of data augmentation techniques - which includes...
View ArticleRAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations
There is considerable work on improving robustness against adversarial attacks bounded by a single $l_p$ norm using adversarial training (AT). However, the multiple-norm robustness (union accuracy) of...
View ArticleForecasting Events in Soccer Matches Through Language
This paper introduces an approach to predicting the next event in a soccer match, a challenge bearing remarkable similarities to the problem faced by Large Language Models (LLMs). Unlike other methods...
View ArticleMonitored Markov Decision Processes
In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assumption that rewards...
View ArticleTowards a Systematic Approach to Design New Ensemble Learning Algorithms
Ensemble learning has been a focal point of machine learning research due to its potential to improve predictive performance. This study revisits the foundational work on ensemble error decomposition,...
View ArticleEstimating Player Performance in Different Contexts Using Fine-tuned Large...
This paper introduces an innovative application of Large Event Models (LEMs), akin to Large Language Models, to the domain of soccer analytics. By learning the "language" of soccer - predicting...
View ArticleA Kalman Filter Based Framework for Monitoring the Performance of In-Hospital...
Unlike in a clinical trial, where researchers get to determine the least number of positive and negative samples required, or in a machine learning study where the size and the class distribution of...
View ArticleExplain Variance of Prediction in Variational Time Series Models for Clinical...
In healthcare, thanks to many model agnostic methods, explainability of the prediction scores made by deep learning applications has improved. However, we note that for daily or hourly risk of...
View ArticleGenerative Nowcasting of Marine Fog Visibility in the Grand Banks area and...
This study presents the application of generative deep learning techniques to evaluate marine fog visibility nowcasting using the FATIMA (Fog and turbulence interactions in the marine atmosphere)...
View ArticleScalable Kernel Logistic Regression with Nystr\"om Approximation: Theoretical...
The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of...
View ArticleEmbedding Compression for Teacher-to-Student Knowledge Transfer
Common knowledge distillation methods require the teacher model and the student model to be trained on the same task. However, the usage of embeddings as teachers has also been proposed for different...
View ArticleConvergence of Gradient Descent with Small Initialization for Unregularized...
We study the problem of symmetric matrix completion, where the goal is to reconstruct a positive semidefinite matrix $\rm{X}^\star \in \mathbb{R}^{d\times d}$ of rank-$r$, parameterized by...
View ArticleLow-Rank Learning by Design: the Role of Network Architecture and Activation...
Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete. Recent research has begun to uncover the mathematical principles underlying these networks, including the...
View ArticleExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation...
Self-supervised Learning (SSL) has emerged as a powerful technique in pre-training deep learning models without relying on expensive annotated labels, instead leveraging embedded signals in unlabeled...
View ArticleCorruption Robust Offline Reinforcement Learning with Human Feedback
We study data corruption robustness for reinforcement learning with human feedback (RLHF) in an offline setting. Given an offline dataset of pairs of trajectories along with feedback about human...
View ArticleDynamic Graph Information Bottleneck
Dynamic Graphs widely exist in the real world, which carry complicated spatial and temporal feature patterns, challenging their representation learning. Dynamic Graph Neural Networks (DGNNs) have shown...
View ArticleElectricity Price Forecasting in the Irish Balancing Market
Short-term electricity markets are becoming more relevant due to less-predictable renewable energy sources, attracting considerable attention from the industry. The balancing market is the closest to...
View ArticleMulti-class real-time crash risk forecasting using convolutional neural...
The performance of an artificial neural network (ANN) in forecasting crash risk is shown in this paper. To begin, some traffic and weather data are acquired as raw data. This data is then analyzed, and...
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