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.
Paper tackles unpredictable feature evolution in learning.
The study compares DLS method with machine learning for cricket match result prediction.
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For insta…
New method for predicting paths of unpredictable objects with high confidence.
Self-balancing sampler improves sampling efficiency and unpredictability.
Gradient descent with large steps leads to chaotic parameter space and unpredictable outcomes.
We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations. A sufficiently powerful temporal model should separate predictable elements of the sequence from unpredictable elements, exp…
Spectral dimensionality reduction algorithms are widely used in numerous domains, including for recognition, segmentation, tracking and visualization. However, despite their popularity, these algorithms suffer from a major limitation known as the "repeated Eigen-directions" phenomenon. That is, many of the embedding co…
The Sornette-Ide differential equation of herding and rational trader behaviour together with very small random noise is shown to lead to crashes or bubbles where the price change goes to infinity after an unpredictable time. About 100 time steps before this singularity, a few predictable roughly log-periodic oscillati…
PredictaBoard benchmarks LLM score predictors to assess their ability to anticipate errors.
Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of havi…
In the real world, a learning system could receive an input that is unlike anything it has seen during training. Unfortunately, out-of-distribution samples can lead to unpredictable behaviour. We need to know whether any given input belongs to the population distribution of the training/evaluation data to prevent unpre…
Vroom optimizes in unpredictable conditions without derivatives.
Study curvatures of diffeomorphisms on non-orientable surfaces.
Optimizes trading in markets with unpredictable price impacts.
The variability of the clusters generated by clustering techniques in the domain of latitude and longitude variables of fatal crash data are significantly unpredictable. This unpredictability, caused by the randomness of fatal crash incidents, reduces the accuracy of crash frequency (i.e., counts of fatal crashes per c…
Study identifies a Strategic Gap in market efficiency due to AI-driven timing and complexity in disclosure.
Predictability enables efficient parallelization of nonlinear models.
We prove results on bounded solutions to backward stochastic equations driven by random measures. Those bounded BSDE solutions are then applied to solve different stochastic optimization problems with exponential utility in models where the underlying filtration is noncontinuous. This includes results on portfolio opti…
Modeling financial chaos with market makers' risk appetite.
This dissertation uses deep reinforcement learning to improve drone flight control.
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable. Our method, ExpO, is a hybridization of these approaches that regu…
Algorithmic stablecoins optimize monetary policy to balance price stability.
Paper predicts stock prices using ML and human intelligence.
Paper develops a model-based RL framework for portfolio optimization in financial markets.
Deep RL agent improves lane changing in unpredictable traffic.
Many complex systems exhibit extreme events far more often than expected for a normal distribution. This work examines how self-similar bursts of activity across several orders of magnitude can emerge from first principles in systems that adapt to information. Surprising connections are found between two apparently unr…
Paper tackles RL for power grid topology optimization.
Graph conformal prediction predicts future power outages with high confidence.
New framework ensures valid uncertainty estimates for any data stream changes.
Stock prices predicted using a Transformer model.
Asynchronous cooperative learning rules ensure all agents converge to correct hypothesis.
New CDC scheme avoids intergenerational subsidies, offering better outcomes.
In complex systems, crucial parameters are often subject to unpredictable changes in time. Climate, biological evolution and networks provide numerous examples for such non-stationarities. In many cases, improved statistical models are urgently called for. In a general setting, we study systems of correlated quantities…
Improved cover song detection with neural networks.
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such an adaptation property relies heavily on cellular neuromodulation,…
This work optimizes DNN inference for energy-harvesting devices by compressing and selectively executing neural network exits.
Study bank salvage model with stochastic impulse controls to minimize costs.
We study the market impact of a meta-order in the framework of the Minority Game. This amounts to studying the response of the market when introducing a trader who buys or sells a fixed amount h for a finite time T. This perturbation introduces statistical arbitrages that traders exploit by adapting their trading strat…
Study optimizes investment strategies in volatile markets using machine learning and Bayesian techniques.
Stock return forecasting is of utmost importance in the business world. This has been the favourite topic of research for many academicians since decades. Recently, regularization techniques have reported to tremendously increase the forecast accuracy of the simple regression model. Still, this model cannot incorporate…
We consider a model in which a trader aims to maximize expected risk-adjusted profit while trading a single security. In our model, each price change is a linear combination of observed factors, impact resulting from the trader's current and prior activity, and unpredictable random effects. The trader must learn coeffi…
We investigate the dynamics of a trust game on a mixed population where individuals with the role of buyers are forced to play against a predetermined number of sellers, whom they choose dynamically. Agents with the role of sellers are also allowed to adapt the level of value for money of their products, based on payof…
MPC outperforms reactive budgeting in non-stationary return environments.
We identify spectral conditions for reliable neural probe interpretation.
The financial crisis clearly illustrated the importance of characterizing the level of 'systemic' risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly inter…
Bayesian deep learning uses function-space priors to improve model uncertainty and robustness.
Tuning machine learning models, particularly deep learning architectures, is a complex process. Automated hyperparameter tuning algorithms often depend on specific optimization metrics. However, in many situations, a developer trades one metric against another: accuracy versus overfitting, precision versus recall, smal…