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We propose a general framework for studying adaptive regret bounds in the online learning framework, including model selection bounds and data-dependent bounds. Given a data- or model-dependent bound we ask, "Does there exist some algorithm…

Machine Learning · Computer Science 2020-02-14 Dylan J. Foster , Alexander Rakhlin , Karthik Sridharan

We consider the problem of online active learning to collect data for regression modeling. Specifically, we consider a decision maker with a limited experimentation budget who must efficiently learn an underlying linear population model.…

Machine Learning · Statistics 2016-12-22 Carlos Riquelme , Ramesh Johari , Baosen Zhang

Recommender systems support decisions in various domains ranging from simple items such as books and movies to more complex items such as financial services, telecommunication equipment, and software systems. In this context,…

Information Retrieval · Computer Science 2021-02-15 Alexander Felfernig , Viet-Man Le , Andrei Popescu , Mathias Uta , Thi Ngoc Trang Tran , Müslüum Atas

Predicting high-fidelity future human poses, from a historically observed sequence, is decisive for intelligent robots to interact with humans. Deep end-to-end learning approaches, which typically train a generic pre-trained model on…

Computer Vision and Pattern Recognition · Computer Science 2023-04-14 Qiongjie Cui , Huaijiang Sun , Jianfeng Lu , Bin Li , Weiqing Li

There is considerable interest in developing techniques for predicting human behavior, for instance to enable emerging contentious situations to be forecast or the nature of ongoing but hidden activities to be inferred. A promising approach…

Social and Information Networks · Computer Science 2013-01-01 Richard Colbaugh , Kristin Glass , Travis Bauer

In supervised learning, acquiring labeled training data for a predictive model can be very costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is a method of obtaining predictive models with high…

Machine Learning · Computer Science 2020-12-17 Hideitsu Hino

Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning. In this paper, we present a novel framework for learning generative models…

Machine Learning · Computer Science 2020-10-05 Ruixiang Zhang , Masanori Koyama , Katsuhiko Ishiguro

Recent applications of machine learning (ML) reveal a noticeable shift from its use for predictive modeling in the sense of a data-driven construction of models mainly used for the purpose of prediction (of ground-truth facts) to its use…

Machine Learning · Computer Science 2021-12-16 Eyke Hüllermeier

We develop a qualitative model of decision making with two aims: to describe how people make simple decisions and to enable computer programs to do the same. Current approaches based on Planning or Decisions Theory either ignore uncertainty…

Artificial Intelligence · Computer Science 2013-02-18 Blai Bonet , Hector Geffner

The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hyperparameters for a given…

Machine Learning · Computer Science 2014-07-09 Michael R. Smith , Logan Mitchell , Christophe Giraud-Carrier , Tony Martinez

A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known…

Artificial Intelligence · Computer Science 2020-09-01 Zhenyu A. Liao , Charupriya Sharma , James Cussens , Peter van Beek

Deep learning models are widely used across computer vision and other domains. When working on the model induction, selecting the right architecture for a given dataset often relies on repetitive trial-and-error procedures. This procedure…

Machine Learning · Computer Science 2026-01-06 Yen-Chia Chen , Hsing-Kuo Pao , Hanjuan Huang

We present a neural network for predicting purchasing intent in an Ecommerce setting. Our main contribution is to address the significant investment in feature engineering that is usually associated with state-of-the-art methods such as…

Machine Learning · Computer Science 2018-07-24 Humphrey Sheil , Omer Rana , Ronan Reilly

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed…

Information Retrieval · Computer Science 2022-02-17 Le Wu , Xiangnan He , Xiang Wang , Kun Zhang , Meng Wang

Deep learning has revolutionized many industries by enabling models to automatically learn complex patterns from raw data, reducing dependence on manual feature engineering. However, deep learning algorithms are sensitive to input data, and…

Machine Learning · Computer Science 2025-07-21 Mert Sehri , Zehui Hua , Francisco de Assis Boldt , Patrick Dumond

Critical points separate distinct dynamical regimes of complex systems, often delimiting functional or macroscopic phases in which the system operates. However, the long-term prediction of critical regimes and behaviors is challenging given…

Physics and Society · Physics 2025-04-15 Xiangrong Wang , Dan Lu , Zongze Wu , Weina Xu , Hongru Hou , Yanqing Hu , Yamir Moreno

Computer systems are full of heuristic rules which drive the decisions they make. These rules of thumb are designed to work well on average, but ignore specific information about the available context, and are thus sub-optimal. The emerging…

Databases · Computer Science 2020-09-22 Max Halford , Philippe Saint-Pierre , Franck Morvan

Large language models based on self-attention mechanisms have achieved astonishing performances not only in natural language itself, but also in a variety of tasks of different nature. However, regarding processing language, our human brain…

Computation and Language · Computer Science 2024-04-18 Chan Li , Junbin Qiu , Haiping Huang

The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, we present a method to provide people with clinical expertise…

Machine Learning · Computer Science 2021-03-05 Aniruddh Raghu , John Guttag , Katherine Young , Eugene Pomerantsev , Adrian V. Dalca , Collin M. Stultz

Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our…

Econometrics · Economics 2023-08-17 S. Van Cranenburgh , S. Wang , A. Vij , F. Pereira , J. Walker