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.
Online PaLD extends PaLD for semi-supervised online applications.
Most traditional online learning algorithms are based on variants of mirror descent or follow-the-leader. In this paper, we present an online algorithm based on a completely different approach, tailored for transductive settings, which combines "random playout" and randomized rounding of loss subgradients. As an applic…
Online boosting method improves weak to strong learner.
Proposes online learning for Hawkes processes with network structure and event interaction.
A scalable online method for Gaussian processes that improves decision-making in various applications.
Transforms offline greedy algorithms to online algorithms for combinatorial problems.
This thesis improves OCO algorithms for dynamic data environments.
R package for online forecasting in various fields.
The paper tackles online resource allocation with uncertain coefficients and chance constraints.
Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identify changepoints in complex systems. In this work, we introduce a Stein variational online changepoint detection (SVOCD) method to provide a computationally tractable generalization of BOCPD beyond the exponent…
Online-iForest detects anomalies in streaming data efficiently.
GAIF enhances online multiple testing with feedback, improving statistical power.
Transforms offline algorithms to online with low regret in random order model.
In online learning, the dynamic regret metric chooses the reference (optimal) solution that may change over time, while the typical (static) regret metric assumes the reference solution to be constant over the whole time horizon. The dynamic regret metric is particularly interesting for applications such as online reco…
New method uses offline data to improve online bandit learning, even when distributions differ.
Online method selects candidates from data streams, ensuring irreversible decisions.
Paper tackles online facility location with user requests and provides a quasi-linear time algorithm.
Paper tackles online label shift in real-world applications.
Feature Selection (FS) plays an important role in learning and classification tasks. The object of FS is to select the relevant and non-redundant features. Considering the huge amount number of features in real-world applications, FS methods using batch learning technique can't resolve big data problem especially when …
As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by providing a consistency result for online random forests.
New framework captures long-term decision dependence in online learning.
POLA adapts learning rates for online time series prediction.
Current online learning methods suffer issues such as lower convergence rates and limited capability to select important features compared to their offline counterparts. In this paper, a novel framework for online learning based on running averages is proposed. Many popular offline regularized methods such as Lasso, El…
Optimizes sampling from target distributions with applications to online learning.
New online method for statistical inference with matrix context in decision-making.
Proposes methods for online conformal prediction with nested prediction sets across multiple confidence levels.
New framework for fair online allocation in continuous time with deadlines.
This work explains why online imitation learning improves faster than theory predicts.
Online learning is a powerful tool for analyzing iterative algorithms. However, the classic adversarial setup sometimes fails to capture certain regularity in online problems in practice. Motivated by this, we establish a new setup, called Continuous Online Learning (COL), where the gradient of online loss function cha…
New algorithm reduces online decision-making regret with efficient LP re-solving and parallel first-order method.
We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners. We consider an algorithmic framework to model the relationship of these tasks via a set of convex constraints. To exploit this relationship, we design a novel algori…
Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem …
In this paper, we design and analyze a new zeroth-order online algorithm, namely, the zeroth-order online alternating direction method of multipliers (ZOO-ADMM), which enjoys dual advantages of being gradient-free operation and employing the ADMM to accommodate complex structured regularizers. Compared to the first-ord…
Nowadays, online learning is an appealing learning paradigm, which is of great interest in practice due to the recent emergence of large scale applications such as online advertising placement and online web ranking. Standard online learning assumes a finite number of samples while in practice data is streamed infinite…
FLeet improves online FL for mobile apps with better performance and privacy.
Principal component analysis (PCA) has been a prominent tool for high-dimensional data analysis. Online algorithms that estimate the principal component by processing streaming data are of tremendous practical and theoretical interests. Despite its rich applications, theoretical convergence analysis remains largely ope…
Paper proposes a novel online transfer learning method to reduce domain discrepancy.
Regularized online learning is widely used in machine learning applications. In online learning, performing exact minimization ( implicit update) is known to be beneficial to the numerical stability and structure of solution. In this paper we study a class of regularized online algorithms without linearizing the…
Study on online regression with noise, achieving near-optimal regret bounds.
The information-theoretic analysis by Russo and Van Roy (2014) in combination with minimax duality has proved a powerful tool for the analysis of online learning algorithms in full and partial information settings. In most applications there is a tantalising similarity to the classical analysis based on mirror descent.…
Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from the observations of its behavior on a task. While this problem has been well investigated, the related problem of {\em online} IRL---where the observations are incrementally accrued, yet the demands of the application often…
Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next cha…
Unified analysis of online optimization with self-concordant barriers, improving regret bounds.
New method optimizes on curved manifolds without curvature dependence.
Online algorithm for probabilistic forecasting of conditional moments.
Unified analysis of tree-based methods for online reinforcement learning.
High-velocity streams of high-dimensional data pose significant "big data" analysis challenges across a range of applications and settings. Online learning and online convex programming play a significant role in the rapid recovery of important or anomalous information from these large datastreams. While recent advance…
Study online monotone density estimation with expert aggregation and log-optimal calibration.