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 discusses the gambling contest introduced in Seel & Strack (Gambling in contests, Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 375, Mar 2012.) and considers the impact of adding a penalty associated with failure to follow a winning strategy. The Seel & Strack model c…
New contest evaluates machine learning robustness against unrestricted adversarial examples.
New framework for contesting algorithmic decisions, not just explaining them.
Study optimal reward schemes for inducing desired player performance in risky contests.
Proposes a method for neural networks to learn causal relationships and humans to contest and modify them.
The Wikimedia Foundation has recently observed that newly joining editors on Wikipedia are increasingly failing to integrate into the Wikipedia editors' community, i.e. the community is becoming increasingly harder to penetrate. To sustain healthy growth of the community, the Wikimedia Foundation aims to quantitatively…
Improved prediction accuracy in matrix factorization using graph-based priors.
Recently, sentiment analysis has received a lot of attention due to the interest in mining opinions of social media users. Sentiment analysis consists in determining the polarity of a given text, i.e., its degree of positiveness or negativeness. Traditionally, Sentiment Analysis algorithms have been tailored to a speci…
Alchemy dataset benchmarks AI models in chemistry.
This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear part…
DisCoveR efficiently discovers declarative process models from event logs.
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provi…
Paper improves financial trading models using GPU parallelism.
The study analyzes games and social hierarchies, incorporating luck and depth of competition.
New algorithm selects variables from large datasets.
This paper presents a general framework for studying diverse beliefs in dynamic economies. Within this general framework, the characterization of a central-planner general equilbrium turns out to be very easy to derive, and leads to a range of interesting applications. We show how for an economy with log investors hold…
Study predicts individual treatment effects in ride-sharing competitions.
Winning solution for predicting player churn in a video game.
We propose an original model for inferring team strengths using a Markov Random Field, which can be used to generate historical estimates of the offensive and defensive strengths of a team over time. This model was designed to be applied to sports such as soccer or hockey, in which contest outcomes take value in a limi…
Framework fuses satellite and OSM data for local climate zone classification.
Study analyzes neural network models to understand generalization performance.
In this paper we study some aspects of knots and links in lens spaces. Namely, if we consider lens spaces as quotient of the unit ball with suitable identification of boundary points, then we can project the links on the equatorial disk of , obtaining a regular diagram for them. In this contest, we obtai…
The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, hum…
Bitcoin fails to prove safe haven status during pandemic.
Bayesian rating system for large competitions improves prediction and efficiency.
Develops a framework to test excessive influence of small data subsets.
Improved ranking method for scarce data with feature info.
ML4CO uses machine learning to improve combinatorial optimization solvers.
FinRLlama wins FinRL Challenge 2024 by fine-tuning LLMs with market data.
Top 8 robotic vision systems tackled lifelong object recognition challenges.
We present a new modeling technique for solving the problem of ecological inference, in which individual-level associations are inferred from labeled data available only at the aggregate level. We model aggregate count data as arising from the Poisson binomial, the distribution of the sum of independent but not identic…
ARFs generate plausible counterfactuals for models, improving model understanding.
Proposes a new method to explain model predictions for consumer recourse.
Box Thirding identifies the best arm efficiently under limited samples.
In this paper we construct an -category associated to a Legendrian submanifold of jet spaces. Objects of the category are augmentations of the Chekanov algebra and the homology of the morphism spaces forms a new set of invariants of Legendrian submanifolds called the bilinearised Le…
Missing data is an expected issue when large amounts of data is collected, and several imputation techniques have been proposed to tackle this problem. Beneath classical approaches such as MICE, the application of Machine Learning techniques is tempting. Here, the recently proposed missForest imputation method has show…
Machine learning enhances rock facies classification with physics-inspired features.
EHR-MPC optimizes sepsis treatment using digital twins and inference-time control.
This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitr…
We present an adversarial active exploration for inverse dynamics model learning, a simple yet effective learning scheme that incentivizes exploration in an environment without any human intervention. Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each …
This paper investigates Shampoo's heuristics and decouples preconditioner updates.
Multinational corporations use highly complex structures of parents and subsidiaries to organize their operations and ownership. Offshore Financial Centers (OFCs) facilitate these structures through low taxation and lenient regulation, but are increasingly under scrutiny, for instance for enabling tax avoidance. Theref…
New algorithms find all ε-good arms in stochastic bandits.
Cardinal scores (numeric ratings) collected from people are well known to suffer from miscalibrations. A popular approach to address this issue is to assume simplistic models of miscalibration (such as linear biases) to de-bias the scores. This approach, however, often fares poorly because people's miscalibrations are …
ContestTrade uses competitive teams to improve LLM trading performance.
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and transformed into sparse representations via one-hot encoding. Due to the spars…
We propose an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g. when pharmaceutical companies test new treatment options against control pills for different diseases, or when internet companies test the…
In this paper we study iterative procedures for stationary equilibria in games with large number of players. Most of learning algorithms for games with continuous action spaces are limited to strict contraction best reply maps in which the Banach-Picard iteration converges with geometrical convergence rate. When the be…