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 AI driving competition challenges with mixed simulation and real-world data.
This work shows how to use simulators to learn efficient exploration in real-world RL.
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, smal…
CausalMan simulates complex causality for fair benchmarking.
This work studies reinforcement learning in the Sim-to-Real setting, in which an agent is first trained on a number of simulators before being deployed in the real world, with the aim of decreasing the real-world sample complexity requirement. Using a dynamic model known as a rich observation Markov decision process (R…
RoPE framework calibrates misspecified simulators for reliable inference.
The paper provides guidelines for choosing between SBI methods in complex biological models.
Discovery of causal relations from observational data is essential for many disciplines of science and real-world applications. However, unlike other machine learning algorithms, whose development has been greatly fostered by a large amount of available benchmark datasets, causal discovery algorithms are notoriously di…
Mobile network that millions of people use every day is one of the most complex systems in the world. Optimization of mobile network to meet exploding customer demand and reduce capital/operation expenditures poses great challenges. Despite recent progress, application of deep reinforcement learning (DRL) to complex re…
Study reveals gaps between simulated and real-world treatment effect evaluation metrics.
Paper proposes hybrid modeling to improve surrogate accuracy using multiple data sources.
StockAgent uses AI to simulate real-world stock trading, analyzing external factors and profitability.
Paper tackles sim-to-real transfer in continuous domains with partial observations.
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notoriously costly to collect, many recent state-of-the-art disentanglement models have heavily relied on synthetic toy data-sets. In this paper, …
The paper proposes a method to learn from both simulation and real-world data.
Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time. Given that it is impractical …
Improved inference efficiency for complex simulations.
Unified method for balancing simulation and data collection.
Study evaluates scalability and real-world impact of disentangled representations.
Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on app…
A method to automatically and symbolically detect and resolve degenerate parameter combinations from parameter-data pairs.
Improves reinforcement learning policies for robustness.
Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized …
We describe a new public-domain open-source simulator of an electronic financial exchange, and of the traders that interact with the exchange, which is a truly distributed and cloud-native system that been designed to run on widely available commercial cloud-computing services, and in which various components can be pl…
New approach reduces simulator exploitation by improving strategic robustness.
New approach reduces simulator exploitation by learning robust models.
Paper uses GANs to simulate consumer transactions with SKU constraints.
Validates composite systems using discrepancy propagation.
Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simula…
Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model-free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real-world ro…
Recently, reinforcement learning (RL) algorithms have demonstrated remarkable success in learning complicated behaviors from minimally processed input. However, most of this success is limited to simulation. While there are promising successes in applying RL algorithms directly on real systems, their performance on mor…
We present the use of the fitted Q iteration in algorithmic trading. We show that the fitted Q iteration helps alleviate the dimension problem that the basic Q-learning algorithm faces in application to trading. Furthermore, we introduce a procedure including model fitting and data simulation to enrich training data as…
The interpretability of machine learning, particularly for deep neural networks, is crucial for decision making in real-world applications. One approach is replacing the un-interpretable machine learning model with a surrogate model, which has a simple structure for interpretation. Another approach is understanding the…
Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domain of robotic locomotion, deep RL could enable learning locomotion skills with minimal engineering and without an explicit model of the robot…
Frengression models causal data flexibly and faithfully.
This paper compares and evaluates methods for evaluating statistical models using benchmarking data and simulations.
This paper introduces NPR, a technique to improve Bayesian inference for multi-modal, high-dimensional simulations.
Study uses RL to simulate realistic market behavior.
CODA simulates future data to generalize models across different datasets.
We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data set…
Bayesian optimization adapts domain parameters for more robust robot policies.
Convolutional neural networks are commonly used to control the steering angle for autonomous cars. Most of the time, multiple long range cameras are used to generate lateral failure cases. In this paper we present a novel model to generate this data and label augmentation using only one short range fisheye camera. We p…
fintech-kMC simulates financial platforms for AI/ML model validation.
Survey examines challenges and solutions in sim-to-real transfer for robotics.
Efficiently estimates marginal posteriors for complex simulations.
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthe…
CausalRegNet generates accurate data for gene perturbation experiments, improving CSL methods.
Unified framework for simulation-based inference learns a single model for multiple tasks.