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.
Framework scores DeFi users based on liquidity and trading behavior.
Two novel algorithms improve distributed machine learning in the presence of Byzantine adversaries.
Well-defined formal definitions for sentiment and opinion are extended to incorporate the necessary elements to provide a formal quantitative definition of reputation. This definition takes the form of a time-based index, in which each element is a function of a collection of opinions mined during a given time period. …
Model analyzes OTC market making with reputation feedback.
Model analyzes how reputation feedback affects OTC market making.
Study shows social media impacts shareholder returns on ESG risks.
A dealer manages quotes and rejection rules to control slippage risk in FX markets.
A new federated learning framework ensures fairness and robustness.
Reducing barriers to entry in large-scale ML markets, study shows multi-objective learning can lower data requirements.
We propose a continuum model for the description of buyer and seller dynamics in an Internet market. The relevant variables are the research effort of buyers and the sellers' reputation building process. We show that, if a commercial web-site gives consumers the possibility to rate credibly sellers they bargained with,…
New approach simulates reputation dynamics using information compression.
Model analyzes how firms balance full disclosure with selective disclosure to maintain a good reputation.
Reproducibility of modeling is a problem that exists for any machine learning practitioner, whether in industry or academia. The consequences of an irreproducible model can include significant financial costs, lost time, and even loss of personal reputation (if results prove unable to be replicated). This paper will fi…
Automated prediction of public speaking performance enables novel systems for tutoring public speaking skills. We use the largest open repository---TED Talks---to predict the ratings provided by the online viewers. The dataset contains over 2200 talk transcripts and the associated meta information including over 5.5 mi…
TadGAN detects anomalies in time series data using GANs and LSTM.
As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project. Existing solutions either rely on a user reputation system or consist of a highly specialized predictor that is tailored to …
The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisio…
CFFL framework improves fairness in FL without sacrificing accuracy.
Machine learning methods have gained a great deal of popularity in recent years among public administration scholars and practitioners. These techniques open the door to the analysis of text, image and other types of data that allow us to test foundational theories of public administration and to develop new theories. …
Android and Facebook provide third-party applications with access to users' private data and the ability to perform potentially sensitive operations (e.g., post to a user's wall or place phone calls). As a security measure, these platforms restrict applications' privileges with permission systems: users must approve th…
The paper proposes a fair and private decentralized deep learning framework.
Research shows higher damages may encourage more disclosure in corporate disputes.
Paper uses queue theory to model financial signals with relativistic delay.
Social networks offer a ready channel for fake and misleading news to spread and exert influence. This paper examines the performance of different reputation algorithms when applied to a large and statistically significant portion of the news that are spread via Twitter. Our main result is that simple crowdsourcing-bas…
Neural network models have a reputation for being black boxes. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. This helps us better understand …
New algorithm reduces regret in strategic prediction problem.
The paper assesses fairness in AI for financial services, using statistical methods.
This paper clarifies Bitcoin's volatility and predictability across daily, weekly, and monthly scales.
Clarifies model-based RL's theoretical issues and counterexamples for popular losses.
The study explores when it's best to remove a real estate broker from the process.
Delegated votes in Uniswap DAO favor parties with less self-owned votes and a16z-affiliated entities.
A technique to quickly fix mistakes in neural networks.
Both generative adversarial networks (GAN) in unsupervised learning and actor-critic methods in reinforcement learning (RL) have gained a reputation for being difficult to optimize. Practitioners in both fields have amassed a large number of strategies to mitigate these instabilities and improve training. Here we show …
Nostradamus links climate and stock market performance.
Detecting faults and SLA violations in a timely manner is critical for telecom providers, in order to avoid loss in business, revenue and reputation. At the same time predicting SLA violations for user services in telecom environments is difficult, due to time-varying user demands and infrastructure load conditions. In…
The problem of searching for experts in a given academic field is hugely important in both industry and academia. We study exactly this issue with respect to a database of authors and their publications. The idea is to use Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform topic modelling i…
Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote the…
Investors usually resort to financial advisors to improve their investment process until the point of complete delegation on investment decisions. Surely, financial advice is potentially a correcting factor in investment decisions but, in the past, the media and regulators blamed biased advisors for manipulating the ex…
Machine learning impacts computational math, offering new functions approximations.
In 1970, E. M. Andreev published a classification of all three-dimensional compact hyperbolic polyhedra having non-obtuse dihedral angles. Given a combinatorial description of a polyhedron, , Andreev's Theorem provides five classes of linear inequalities, depending on , for the dihedral angles, which are necessar…
Generative models' evaluation scores can be misleading, leading to inflated grades.
Improves score estimation for noised targets using known clean scores.
This work improves likelihood of score-based diffusion ODEs using high-order denoising score matching.
Model for open, decentralized network with task load balancing.
Nonnegative matrix factorization (NMF) has an established reputation as a useful data analysis technique in numerous applications. However, its usage in practical situations is undergoing challenges in recent years. The fundamental factor to this is the increasingly growing size of the datasets available and needed in …
Study compares multivariate scoring rules for distribution forecasts.
A new method improves data generation quality by correcting score mismatches.
New scoring rules improve probabilistic classification model evaluation.