The study evaluates 62 classifiers for detecting online toxic comments.
problem Identifying and classifying toxic online commentary.
method Systematic evaluation of 62 classifiers representing 19 algorithmic families on the Jigsaw dataset.
result Simple bad word lists are most predictive of offensive commentary.
The paper analyzes sports commentary to automatically recognize events and extract insights.
problem Automatically recognizing and categorizing major actions in sports events from commentary.
method Used multiple Natural Language Processing techniques for classification and sentiment analysis.
result Identified insights from analyzing live sport commentaries and classifying major actions.
Commentary on Cheng's fairness comparison between tests and AI.
problem Distinction between equality and equity in fairness.
method Systematic comparison of test fairness and algorithmic fairness.
result Importance of causality in fairness research.
We give some background and biographical commentary on the postumous article that appears in this [journal issue | ArXiv] by Robert Riley on his part of the early history of hyperbolic structures on some compact 3-manifolds. A complete list of Riley's publications appears at the end of the article.
We provide a commentary on Teichm{ü}ller's paper "Extremale quasikonforme Abbildungen und quadratische Differentiale" (Extremal quasiconformal mappings of closed oriented Riemann surfaces), Abh. Preuss. Akad. Wiss., Math.-Naturw. Kl. 1940, No.22, 1-197 (1940). The paper is quoted in several works, although it was read …
This is a commentary on Teichm{ü}ller's paper Ein Verschiebungssatz der quasikonformen Abbildung (A displacement theorem of quasiconformal mapping), published in 1944. We explain in detail how Teichm{ü}ller solves the problem of finding the quasiconformal mapping from the unit disc to itself, sending 0 to a strictly ne…
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice.
problem Lack of consistent advisor expertise and difficulty in encoding it in LLM systems.
method Grounds financial advisor personas in fund disclosures, market context, and manager commentary through an agentic actor--scorer--patcher loop.
result Personas better recover portfolio decisions and manager interpretation than generic baselines.
This is a short commentary piece that discusses how the methods used in the natural sciences can apply to economics in general and financial markets specifically.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.
problem Lack of consistent and specific financial advisor expertise in personalized investment advice.
method Grounds financial advisor personas in fund disclosures, holdings transitions, market context, and manager commentary through an agentic actor--scorer--patcher loop.
result Personas better recover portfolio decisions and grounded manager interpretation than generic baselines.
We show that power-law analyses of financial commentaries from newspaper web-sites can be used to identify stock market bubbles, supplementing traditional volatility analyses. Using a four-year corpus of 17,713 online, finance-related articles (10M+ words) from the Financial Times, the New York Times, and the BBC, we s…
Commentary on Teichmüller's 1938 paper on conformal and quasiconformal mappings.
problem Investigations into conformal and quasiconformal mappings and their applications.
method Detailed development of conformal invariants and applications in value distribution theory.
result Insures the almost circularity of certain loci and the circularity near infinity of quasiconformal maps.
This paper is a commentary and a reading guide to three papers by Herbert Busemann, Über die Geometrien, in denen die "Kreise mit unendlichem Radius" die kürzesten Linien sind." (On the geometries where circles of infinite radius are the shortest lines) (1932), "Paschsches Axiom und Zweidimensionalität," (Pasch's Axiom…
Comments on deconfounder method's pros and cons.
problem Addressing confounding in causal inference.
method Critically reviews deconfounder method and suggests improvements.
result Points out advantages and limitations of deconfounder method.
This is a mathematical commentary on Teichm{ü}ller's paper ``Bestimmung der extremalen quasikonformen Abbildungen bei geschlossenen orientierten Riemannschen Fl{ä}chen'' (Determination of extremal quasiconformal maps of closed oriented Riemann surfaces). This paper is among the last (and may be the last one) that Teich…
We comment on the paper Über Extremalprobleme der konformen Geometrie (On extremal problems in conformal geometry) by Teichmüller, published in 1941. This paper contains ideas on a wide generalization of his previous work on the solution of extremal problems in conformal geometry. The generalization concerns at the sam…
The paper reviews Hankel low-rank methods for time series analysis and forecasting.
problem Developing efficient methods for time series analysis and forecasting.
method Hankel low-rank approximation and completion techniques.
result Discussion of methods and challenges in obtaining optimal solutions.
This is a commentary on Teichmüllers' paper "Veränderliche Riemannsche Flächen" (Variable Riemann Surfaces), published in 1944. This paper is the last one that Teichmüller wrote on the problem of moduli. At most places the paper contains ideas and no technical details. The author presents a completely new approach to T…
We comment on Teichm{ü}ller 's paper ''Vollst{ä}ndige L{ö}sung einer Extremalaufgabe der quasikonformen Abbildung'' (Complete solution of an ex-tremal problem of the quasiconformal mapping),, published in 1941. In this paper, Teichm{ü}ller gives a proof of the existence of extremal quasiconformal mappings in the case o…
Paper summarizes unsupervised learning challenges for disentangled representations.
problem Unsupervised learning of disentangled representations without inductive biases.
method Theoretical and practical analysis of existing approaches.
result Unsupervised disentanglement is fundamentally impossible without inductive biases.
Commentary on Rashomon Effect complicating model selection.
problem Many models equally predict data; hard to draw conclusions.
method Connections to recent ML literature exploring implications.
result Grasping Rashomon Effect can foster collaboration.
This paper reviews PU learning evaluation methods and provides practical recommendations.
problem Evaluating PU learning methods when only positive and unlabelled data are available.
method Critical review of 51 articles proposing PU classifiers and alternative predictive accuracy measures.
result Practical recommendations for improving PU learning evaluation.
This handbook translates lead time analysis into R code.
problem Tracking divergence in booking lead times over time.
method Translated original article's methodology into R code.
result Demonstrated reproducibility and error bounds in lead time forecasts.
Some optimization problems coming from the Differential Geometry, as for example, the minimal submanifolds problem and the harmonic maps problem are solved here via interior solutions of appropriate multitime optimal control problems. Section 1 underlines some science domains where appear multitime optimal control prob…
This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what…
This study examines investor sentiment's impact on stock market liquidity and volatility using deep learning and TVP-VAR models.
problem Investor sentiment's impact on stock market liquidity and volatility.
method Deep learning BERT model for sentiment extraction and TVP-VAR model for time-varying analysis.
result Investor sentiment has a stronger impact on stock market liquidity and volatility, with more pronounced effects in short-term shocks.
The study examines Euclid's Book I, focusing on area applications and construction methods.
problem Exploring Euclid's geometric constructions and proofs, particularly those involving area calculations.
method Summarizing medieval editions and ancient commentaries, comparing constructions and proofs.
result Medieval editions often avoid Euclid's use of superposition in area proofs, offering alternative constructions.
Investigates optimal consumption and investment using alternative data sources.
problem Optimal consumption and investment decisions under hidden economic regimes.
method Develops a novel duality theory for a jump-diffusion process with alternative data.
result Provides conditions for using control approach based on dynamic programming.
This paper explores crypto, blockchain, and Metaverse risks and opportunities.
problem Understanding crypto crashes and blockchain technologies.
method Interdisciplinary approach combining fintech, machine learning, and risk assessment.
result Blockchain technologies will continue to dominate, but discerning genuine projects is crucial.
Online boosting method improves weak to strong learner.
problem Online learning of weak to strong learner.
method Extends batch GentleAdaBoost to online approach with line search.
result Online boosting performs better than other methods.
Paper tackles online optimization with memory and competitive control.
problem Minimizing hitting and switching costs in online optimization problems.
method Optimistic Regularized Online Balanced Descent algorithm.
result Achieves a constant, dimension-free competitive ratio.
Study on computable online learning with new conditions and complexities.
problem Characterizing optimal online learning under varying optimality requirements.
method Introduced anytime optimal (a-optimal) online learning and explored computational separations.
result Found a computational separation between a-optimal and optimal online learning.
We study the task of online boosting--combining online weak learners into an online strong learner. While batch boosting has a sound theoretical foundation, online boosting deserves more study from the theoretical perspective. In this paper, we carefully compare the differences between online and batch boosting, and pr…
Proposes an online method for high-dimensional streaming data.
problem Increasing variable dimensions with sample size in online kernel sliced inverse regression.
method Introduces approximate linear dependence condition and dictionary variable sets to address the problem. Transforms into online generalized eigen-decomposition problem and uses stochastic optimization for updates.
result Achieves close performance to batch processing kernel sliced inverse regression.
Enhances RL for better stock market trading decisions.
problem Lack of practical RL evidence in finance.
method Advanced RL framework using financial indicators.
result Improved differentiation between buy/sell actions.
Boosts weak online learners to strong ones with sublinear regret.
problem Online learning agnostic setting without strong guarantees.
method Reduction to online convex optimization, boosting via marginally-better-than-trivial regret guarantees.
result First agnostic online boosting algorithm with sublinear regret.
Improved online classification with accurate predictions.
problem Online classification challenges with limited data.
method Designing an online learner that uses predictions to reduce regret.
result Expected regret is better than worst-case analysis, especially with accurate predictions.
Continuous-time algorithms improve online learning performance.
problem Online learning with sequential data and minimizing overall regret.
method Extending discrete-time algorithms to continuous-time models for online linear optimization, adversarial bandit, and adversarial linear bandit.
result Optimal regret bounds are proven for continuous-time settings.
Extends boosting to multiclass online agnostic classification.
problem Online multiclass classification with weak learners.
method Reduces multiclass online agnostic boosting to online convex optimization.
result First boosting algorithm for online agnostic multiclass classification.
Proximal online gradient minimizes dynamic regret in evolving environments.
problem Optimizing dynamic regret in online learning where the optimal solution changes over time.
method Proximal online gradient method, showing it is optimal for dynamic regret.
result Proximal online gradient matches the lower bound for dynamic regret, proving its optimality.
Online learning improves big data accuracy quickly.
problem Heterogeneity in big data analysis.
method Online machine learning for big data.
result Online learning converges quickly to batch accuracy.
New private algorithms for online learning improve regret in high privacy regimes.
problem Private online learning from experts and convex optimization.
method Transformed lazy algorithms for differential privacy.
result Improved regret bounds for DP-OPE and DP-OCO.
Online-iForest detects anomalies in streaming data efficiently.
problem Offline anomaly detection methods are impractical for streaming contexts.
method Online-iForest tracks evolving data processes in real-time without periodic retraining.
result Online-iForest outperforms all competitors in efficiency.
Study online learning with set-valued feedback, showing differences between deterministic and randomized approaches.
problem Online learning with set-valued feedback, where labels are sets rather than single labels.
method Introduced new combinatorial dimensions (Set Littlestone and Measure Shattering) to characterize learnability.
result Characterized deterministic and randomized online learnability, and established bounds for various learning settings.
Transforms offline algorithms to online with low regret in random order model.
problem Developing online algorithms with low approximate regret from offline approximation algorithms.
method General reduction theorem and coreset construction method.
result Achieves polylogarithmic ε-approximate regret for various online problems.
New setup for continuous online learning improves understanding of imitation learning.
problem Challenges in capturing regularity in online problems.
method Continuous Online Learning (COL) setup, focusing on continuous gradient changes.
result Fundamental equivalence between sublinear dynamic regret and solving certain EPs.
Paper proposes an online transfer learning framework using online bagging.
problem Difficulty in obtaining sufficient labeled data in the target domain.
method Ensemble approach with online bagging for anytime transfer learning.
result Effectiveness of the proposed algorithms demonstrated on real data sets.
Private learning can be used to efficiently solve online learning problems.
problem The relationship between differentially private learning and online learning efficiency.
method Derive an efficient black-box reduction from differentially private learning to online learning from expert advice.
result An efficient differentially private learner implies an efficient online learner.
Predicts student performance in interactive online question pools using GNNs.
problem Predicting student performance in interactive online question pools with evolving knowledge.
method Proposes R^2GCN, a GNN model for heterogeneous networks to predict student performance.
result Achieves higher accuracy in student performance prediction than traditional methods.