arXiv research

OFTER predicts multivariate time series online, outperforming baselines.

Modular pipeline improves stock portfolio prediction robustness under regime changes.

This paper introduces Zap, a generic machine learning pipeline for making predictions based on online user behavior. Zap combines well known techniques for processing sequential data with more obscure techniques such as Bloom filters, bucketing, and model calibration into an end-to-end solution. The pipeline creates we…

Clapping reduces memory usage in distributed optimization by reusing data samples.

AgensFlow learns multi-agent coordination policies from experience.

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is prop…

Estimates mode from partial feedback, improving AI learning pipelines.

New algorithm learns and unlearns from streaming data efficiently.

New machine learning pipeline solves dynamic vehicle routing problems efficiently.

AttViz offers online visualizations of neural language model attention mechanisms.

This paper introduces a novel approach for dengue fever classification based on online learning paradigms. The proposed approach is suitable for practical implementation as it enables learning using only a few training samples. With time, the proposed approach is capable of learning incrementally from the data collecte…

A scalable system detects price anomalies in online marketplaces to improve customer experience.

Efficiently factorize tensors in streaming data with coreset selection.

Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time. In this paper, …

AVATAR uses a surrogate model to quickly evaluate ML pipelines, saving time and resources.

New methods for skill rating in sports using state-space models.

A new metric, Weighted Regret, unifies FDR and power evaluation in online multiple testing.

While the purpose of most fake news is misinformation and political propaganda, our team sees it as a new type of myth that is created by people in the age of internet identities and artificial intelligence. Seeking insights on the fear and desire hidden underneath these modified or generated stories, we use machine le…

STVNN models spatiotemporal data using covariance matrices.

FavMac maximizes value while controlling cost in multi-label prediction.

New online learning algorithms improve cyberattack detection in industrial control systems.

Hyperparameter tuning of multi-stage pipelines introduces a significant computational burden. Motivated by the observation that work can be reused across pipelines if the intermediate computations are the same, we propose a pipeline-aware approach to hyperparameter tuning. Our approach optimizes both the design and exe…

Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network models, layers composing the neural models are simultaneously trained using backpropagation. We argue that the isolated training scheme of ML…

Study proposes a statistical testing framework for evaluating clustering pipelines.

Unified approach to RLHF tackles uncertainty in reward function.

Investigates fairness in pipeline models where individuals may drop out.

We review a method for click-through rate prediction based on the work of Menon et al. [11], which combines collaborative filtering and matrix factorization with a side-information model and fuses the outputs to proper probabilities in [0,1]. In addition we provide details, both for the modeling as well as the experime…

Bridges uplift modeling and sequential decision-making with online budget allocation.

Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole …

Automates supervised learning pipeline design with matrix and tensor factorization.

Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole …

KVCOMM optimizes multi-agent LLM systems by reusing KV-caches, reducing redundant processing.

Algorithm learns optimal coordination for strategic agents in uncertain settings.

New model optimizes oil product distribution via pipelines.

Pipeline decomposes portfolio optimization problems into smaller, solvable subproblems.

fintech-kMC simulates financial platforms for AI/ML model validation.

Paper proposes a statistical test for feature selection pipelines using selective inference.

New approach identifies and explains errors in machine learning pipelines.

Pipelined Backpropagation trains large models without batches efficiently.

Study reveals similarities in knowledge flows between pharmaceutical and AI industries.

Modyn automates continuous ML model training on growing datasets.

TODS automates time series outlier detection with customizable pipelines.

Hungarian text processing improved with efficient, accurate NLP pipelines.

Study forecasts vegetable prices in Nepal using a novel index and ensemble model.

Techniques such as ensembling and distillation promise model quality improvements when paired with almost any base model. However, due to increased test-time cost (for ensembles) and increased complexity of the training pipeline (for distillation), these techniques are challenging to use in industrial settings. In this…

Enhances content moderation with culturally-aware models.

A rigorous ML pipeline for binary classification in biomedical studies, focusing on pancreatic cancer.

Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware utilization. Unfortunately, to preserve the statistical efficiency of sequential training, existing PP techniques sacrifice hardware efficien…