A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This paper uses bandit algorithms to reduce the cost of user interface experimentation in online retail.
Comparison Lift uses bandit algorithms to optimize online ad testing.
New method optimizes experiments under constraints.
Greedy policy maximizes information in unknown linear systems.
New BED method handles online inference for partially observed dynamical systems.
Two methods estimate effect size for online experiments, improving accuracy and efficiency.
New method minimizes experiment cost while maintaining accuracy.
New method learns low-dimensional representations of AI-generated treatments.
Automated method selects best model from many for production systems.
Bayesian model predicts online activity participation.
Online-iForest detects anomalies in streaming data efficiently.
Fast algorithm for online optimization on transport polytopes.
We provide a new online learning algorithm which utilizes online passive-aggressive learning (PA) and total-error-rate minimization (TER) for binary classification. The PA learning establishes not only large margin training but also the capacity to handle non-separable data. The TER learning on the other hand minimizes…
While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the stat…
We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine. More challenging, the data coming from the wild may contain malicious outliers. To address the scalability and robustness issues, we present an online robust learning (ORL) approac…
One of the current challenges in machine learning is how to deal with data coming at increasing rates in data streams. New predictive learning strategies are needed to cope with the high throughput data and concept drift. One of the data stream mining tasks where new learning strategies are needed is multi-target regre…
New model tackles interference in online experiments.
We propose algorithms for online principal component analysis (PCA) and variance minimization for adaptive settings. Previous literature has focused on upper bounding the static adversarial regret, whose comparator is the optimal fixed action in hindsight. However, static regret is not an appropriate metric when the un…
Proposes online learning for Hawkes processes with network structure and event interaction.
This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems. Existing projection-free methods either achieve suboptimal regret bounds or have high per-iteration computational costs. To fill this gap, two efficient projection-free online methods called ORGFW and MORGFW are …
The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.
Designs an online selective sampling approach for choosing which model to use.
We consider online detection strategies for identifying a change point in a stream of quantum particles allegedly prepared in identical states. We show that the identification of the change point can be done without error via sequential local measurements while attaining the optimal performance bound set by quantum mec…
Online Normalization is a new technique for normalizing the hidden activations of a neural network. Like Batch Normalization, it normalizes the sample dimension. While Online Normalization does not use batches, it is as accurate as Batch Normalization. We resolve a theoretical limitation of Batch Normalization by intro…
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, porta…
Study estimates long-term effects of online advertising mechanisms on user behavior and revenue.
Nucleosome positioning is an important process required for proper genome packing and its accessibility to execute the genetic program in a cell-specific, timely manner. In the recent years hundreds of papers have been devoted to the bioinformatics, physics and biology of nucleosome positioning. The purpose of this rev…
A popular approach to selling online advertising is by a waterfall, where a publisher makes sequential price offers to ad networks for an inventory, and chooses the winner in that order. The publisher picks the order and prices to maximize her revenue. A traditional solution is to learn the demand model and then subseq…
New approach improves convergence of online learning for ARIMA models.
As more data are produced each day, and faster, data stream mining is growing in importance, making clear the need for algorithms able to fast process these data. Data stream mining algorithms are meant to be solutions to extract knowledge online, specially tailored from continuous data problem. Many of the current alg…
A new algorithm speeds up CP decomposition for large tensors.
Bubblewrap predicts neural dynamics online, scaling to thousands of neurons.
Ahpatron improves online kernel learning with tighter mistake bounds.
We present an efficient second-order algorithm with regret for the bandit online multiclass problem. The regret bound holds simultaneously with respect to a family of loss functions parameterized by , for a range of restricted by the norm of the competitor. The family of loss funct…
A new framework for real-time multi-speaker diarization without prior registration.
With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem i…
New algorithms control FDX while achieving more power in online multiple testing.
New online imputation method for mixed data improves accuracy and speed.
New framework for online influencer selection considering cost constraints.
New model for high rank matrix completion with online and batch methods.
Bayesian nonparametric model predicts user activity and intervention success.
AR design reduces multi-armed bandit experiment cost.
COP improves online conformal prediction by incorporating data patterns, leading to tighter prediction sets.
Global convergence of an online (stochastic) limited memory version of the Broyden-Fletcher- Goldfarb-Shanno (BFGS) quasi-Newton method for solving optimization problems with stochastic objectives that arise in large scale machine learning is established. Lower and upper bounds on the Hessian eigenvalues of the sample …
Online media provides opportunities for marketers through which they can deliver effective brand messages to a wide range of audiences. Advertising technology platforms enable advertisers to reach their target audience by delivering ad impressions to online users in real time. In order to identify the best marketing me…
Batch Thompson Sampling reduces exploration-exploitation trade-off in online decision making.
NeSGD efficiently updates tensor-based features for online model learning in multi-way data.
A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adapt…