arXiv research

DCN-V2 improves deep & cross network for web-scale learning to rank systems.

Efficiently projects points onto polytopes, especially useful in web-scale applications.

Paper combines scalable BMF algorithms for web-scale datasets.

BanditLP optimizes personalized recommendations for large-scale systems.

New methods ensure fair rankings in web-scale recommender systems.

Efficiently selects nearest neighbors for labeling to speed up active learning.

HGT models heterogeneous graphs with dedicated node and edge representations.

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a challenge. Here we de…

Study on continual learning with Twitter data, developing ConGraD algorithm.

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery. However, classical methods for inferring topic models do not scale up to the massive size of today's publicly availab…

Learning from multiple-relational data which contains noise, ambiguities, or duplicate entities is essential to a wide range of applications such as statistical inference based on Web Linked Data, recommender systems, computational biology, and natural language processing. These tasks usually require working with very …

Since time immemorial, people have been looking for ways to organize scientific knowledge into some systems to facilitate search and discovery of new ideas. The problem was partially solved in the pre-Internet era using library classifications, but nowadays it is nearly impossible to classify all scientific and popular…

The paper provides convergence guarantees for multicalibration gradient boosting.

Variational inference provides approximations to the computationally intractable posterior distribution in Bayesian networks. A prominent medical application of noisy-or Bayesian network is to infer potential diseases given observed symptoms. Previous studies focus on approximating a handful of complicated pathological…

Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail inv…

We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in …

In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB). Traditional techniques from universal schema and from schema mapping fall in two extremes: either they perform instance-level…

Proposes SSL framework for improving item recommendations in large-scale systems.

In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for …

This paper improves prediction uncertainty estimation by inferring variation from neuron activation strength.

The method of random projections has become a standard tool for machine learning, data mining, and search with massive data at Web scale. The effective use of random projections requires efficient coding schemes for quantizing (real-valued) projected data into integers. In this paper, we focus on a simple 2-bit coding …

The paper studies stability of generative models trained on mixed data.

Unsupervised near-duplicate detection has many practical applications ranging from social media analysis and web-scale retrieval, to digital image forensics. It entails running a threshold-limited query on a set of descriptors extracted from the images, with the goal of identifying all possible near-duplicates, while l…

When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers. We consider this challenge in the co…

The paper shows how curated synthetic data can optimize human preferences in generative models.

New data accumulation prevents model collapse in generative models.

Meta-learning adapts models for unseen tasks across AI, robotics, and NLP.

Meta-learning improves neural networks by adapting learning algorithms.

Survey explores how transfer learning improves deep reinforcement learning.

Machine learning models adapt to motor learning but face challenges.

New method uses bi-level optimization to learn useful representations for imitation learning.

Tabular Q-Learning with learned state abstractions solves continuous control tasks.

Study Whittle index learning algorithms for restless bandits with constant stepsizes.

Paper discusses flaws in traditional RL for lifelong learning.

AI learns to learn sequentially without forgetting.

Poisson learning doesn't solve graph semi-supervised learning issues.

New unsupervised learning technique learns independent kernels for better machine learning tasks.

Meta-learning helps models learn quickly from few samples.

New self-imitation learning method improves performance in continuous control tasks.

Deep reinforcement learning finds optimal learning policies for adaptive systems.

Unified framework explains all types of learning, including brain.

Cyclical learning rates improve DRL performance without manual tuning.

Study batch reinforcement learning methods for personalized medical treatments.

A new meta-meta classification method tackles few-shot learning tasks.

The paper proposes a learning algorithm that improves adaptability and generalization.

This paper surveys meta-learning, online, and continual learning.

Relational logistic regression (RLR) is a representation of conditional probability in terms of weighted formulae for modelling multi-relational data. In this paper, we develop a learning algorithm for RLR models. Learning an RLR model from data consists of two steps: 1- learning the set of formulae to be used in the m…

Survey on curriculum learning for reinforcement learning.