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.
Active data collection improves convergence rates in operator learning.
Study shows small groups can influence machine learning algorithms.
This research improves debt collection strategies using advanced machine learning.
Paper proposes efficient sample collection strategy for RL.
Approach collects missing outcomes to improve fairness in classification.
Study on collaboration vs. independent data collection in sensor networks.
This paper acts as a collection of various trading strategies and useful pieces of market information that might help to implement such strategies. This list is meant to be comprehensive (though by no means exhaustive) and hence we only provide pointers and give further sources to explore each strategy further. To set …
Optimizes investment strategies for retirees with longevity risk.
We document a mechanism operating in complex adaptive systems leading to dynamical pockets of predictability (``prediction days''), in which agents collectively take predetermined courses of action, transiently decoupled from past history. We demonstrate and test it out-of-sample on synthetic minority and majority game…
We present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and generalise over its experience to achieve new outcomes. The robot explores its environment both via interactive learning and goal…
Agglomerative hierarchical clustering can be implemented with several strategies that differ in the way elements of a collection are grouped together to build a hierarchy of clusters. Here we introduce versatile linkage, a new infinite system of agglomerative hierarchical clustering strategies based on generalized mean…
Post-ADC inference corrects bias in statistical inference after active data collection.
Generative model learns conditional distributions on collective variable levels.
Study uses RNN for real-time crypto price prediction and trading optimization.
Presents SPEED, an algorithm for optimal policy evaluation in linear bandits with heteroscedastic noise.
Paper proposes a new framework for combining investment strategies without market-specific assumptions.
Study experiment planning with function approximation in contextual bandit problems.
Unions of subspaces provide a powerful generalization to linear subspace models for collections of high-dimensional data. To learn a union of subspaces from a collection of data, sets of signals in the collection that belong to the same subspace must be identified in order to obtain accurate estimates of the subspace s…
Evolution Strategies (ES) emerged as a scalable alternative to popular Reinforcement Learning (RL) techniques, providing an almost perfect speedup when distributed across hundreds of CPU cores thanks to a reduced communication overhead. Despite providing large improvements in wall-clock time, ES is data inefficient whe…
Study mutual insurance market dynamics using mean field games.
Generalization of the minority game to more than one market is considered. At each time step every agent chooses one of its strategies and acts on the market related to this strategy. If the payoff function allows for strong fluctuation of utility then market occupancies become inhomogeneous with preference given to th…
In a collectivised pension fund, investors agree that any money remaining in the fund when they die can be shared among the survivors. We compute analytically the optimal investment-consumption strategy for a fund of identical investors with homogeneous Epstein--Zin preferences, investing in the Black--Scholes mark…
The paper analyzes cryptocurrency and equity markets using advanced statistical methods.
Assessment of mental workload in real-world conditions is key to ensure the performance of workers executing tasks that demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end despite having observed that EEG correlates of mental workload vary across subjects and physical s…
Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.
This paper proposes a novel policy for a group of agents to, individually as well as collectively, solve a multi armed bandit (MAB) problem. The policy relies solely on the information that an agent has obtained through sampling of the options on its own and through communication with neighbors. The option selection po…
Adaptive robust strategy improves online portfolio selection by managing market trends and costs.
New method improves active statistical inference by reducing noise.
Paper proposes a deep RL method for hedging variable annuities, outperforming misspecified models.
We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples. The aim o…
In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch. Recognizers in decision either reject or categorize them to some known class using empirically-set threshold. Thus the decision threshold plays a…
Paper develops a robust federated recommendation system against poisoning attacks.
Embedded ensembles improve neural network performance efficiently.
A new active learning strategy for real-time data in production.
In lowest unique bid auctions, players bid for an item. The winner is whoever places the \emph{lowest} bid, provided that it is also unique. We use a grand canonical approach to derive an analytical expression for the equilibrium distribution of strategies. We then study the properties of the solution as a function…
Bayesian optimization agent learns user preferences from pairwise comparisons.
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for building and combining asynchronous optimization strategies, designed for global optimization of expensive functions where concurrent function …
Continuous collection of physiological data from wearable sensors enables temporal characterization of individual behaviors. Understanding the relation between an individual's behavioral patterns and psychological states can help identify strategies to improve quality of life. One challenge in analyzing physiological d…
We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regr…
Study optimizes data collection from biased, costly sources to minimize risk.
We present a sparse estimation and dictionary learning framework for compressed fiber sensing based on a probabilistic hierarchical sparse model. To handle severe dictionary coherence, selective shrinkage is achieved using a Weibull prior, which can be related to non-convex optimization with -norm constraints for $0…
A decentralized approach for agents to learn and optimize collectively.
This paper proposes a geometry-aware active learning framework for spatiotemporal dynamic systems.
Study compares federated learning and coreset approaches for privacy in distributed machine learning.
This article is motivated by soccer positional passing networks collected across multiple games. We refer to these data as replicated spatial passing networks---to accurately model such data it is necessary to take into account the spatial positions of the passer and receiver for each passing event. This spatial regist…
This paper presents Natural Evolution Strategies (NES), a recent family of algorithms that constitute a more principled approach to black-box optimization than established evolutionary algorithms. NES maintains a parameterized distribution on the set of solution candidates, and the natural gradient is used to update th…
Decentralised fund framework allocates capital via tokenised vaults.
A reinforcement learning approach prepares quantum squeezed states in open spin systems.