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.
ChainerRL is a deep reinforcement learning library for Python.
PettingZoo library accelerates multi-agent reinforcement learning research.
In this work, we address the problem of tuning communication libraries by using a deep reinforcement learning approach. Reinforcement learning is a machine learning technique incredibly effective in solving game-like situations. In fact, tuning a set of parameters in a communication library in order to get better perfo…
MushroomRL simplifies RL experiments for researchers.
MARLeME extracts MARL models into symbolic models for better interpretability.
PyCFRL helps ensure fair reinforcement learning policies from offline data.
Meta-reinforcement learning improves fault-adaptive control efficiency.
We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep and reinforcement learning approaches, ReLeaSE integrates two deep neural networks - generative and predictive - that are trained separately b…
SafePILCO is a Python tool for safe reinforcement learning.
This paper presents an open-source enforcement learning toolkit named CytonRL (https://github.com/arthurxlw/cytonRL). The toolkit implements four recent advanced deep Q-learning algorithms from scratch using C++ and NVIDIA's GPU-accelerated libraries. The code is simple and elegant, owing to an open-source general-purp…
This paper applies DRL to mean reversion trading problems.
Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this e…
FinRL-Meta offers market environments and benchmarks for financial reinforcement learning.
FinRL simplifies deep RL for stock trading, making it accessible to beginners.
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem. In this paper, we give a formalization of this shared pattern, which we call GIMLI, prove its general requirements, and derive…
An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks. In general, however, it is unclear how to compose skills in a principled way. We provide a "recipe" for optimal value function composition in entropy-regularised reinforcement learning (RL) and then exten…
learn2learn simplifies meta-learning research by providing a library and standardized interfaces.
Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent successes in combining deep neural networks with reinforcement learning have sparked significant new interest in this domain. However, pract…
A framework for reinforcement learning tackles CVRP with competitive results.
This paper formalizes -learning and linear TD convergence using Lean 4.
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key is…
We consider the problem of developing suitable learning representations (embeddings) for library packages that capture semantic similarity among libraries. Such representations are known to improve the performance of downstream learning tasks (e.g. classification) or applications such as contextual search and analogica…
MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript.…
MRCpy implements minimax risk classifiers with performance guarantees and distribution shift adaptability.
Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are with prescribed size, and can degenerate the …
IllinoisSL is a Java library for learning structured prediction models. It supports structured Support Vector Machines and structured Perceptron. The library consists of a core learning module and several applications, which can be executed from command-lines. Documentation is provided to guide users. In Comparison to …
Achieving faster execution with shorter compilation time can enable further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently, simulated annealing and genetic algorithms.…
The deep Q-network (DQN) and return-based reinforcement learning are two promising algorithms proposed in recent years. DQN brings advances to complex sequential decision problems, while return-based algorithms have advantages in making use of sample trajectories. In this paper, we propose a general framework to combin…
TrueLearn Python library for personalized educational recommendations.
Disentanglement-PyTorch library facilitates disentangled representation learning.
Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we g…
Python library for boosting statistical relational models.
L2SC improves spectral clustering performance by selectively transferring knowledge across tasks.
The problem-solving in automated theorem proving (ATP) can be interpreted as a search problem where the prover constructs a proof tree step by step. In this paper, we propose a deep reinforcement learning algorithm for proof search in intuitionistic propositional logic. The most significant challenge in the application…
Picasso is a new library for sparse learning problems in R and Python.
BayesDLL offers a PyTorch library for Bayesian deep learning with large models.
Kernelmethods library simplifies kernel-based ML in Python.
New metrics assess reliability in RL algorithms.
abess efficiently solves various machine learning problems quickly.
DeepRobust is a PyTorch library for adversarial attacks and defenses.
Library learns Bayesian networks from mixed data without discretization.
We introduce torchbearer, a model fitting library for pytorch aimed at researchers working on deep learning or differentiable programming. The torchbearer library provides a high level metric and callback API that can be used for a wide range of applications. We also include a series of built in callbacks that can be u…
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, porta…
GPflux simplifies deep Gaussian processes for Python.
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
FedML aims to improve FL research by providing a library and benchmark.
LibAUC optimizes X-risks for AI tasks like CID, LTR, and CLR.
GP+ is a Python library for Gaussian process learning.