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.
Unified view on selective credit assignment for reinforcement learning.
Eigenoptions improve credit assignment in reinforcement learning.
Introduces expected eligibility traces for more efficient credit assignment in reinforcement learning.
New framework for modular reinforcement learning reduces sample complexity.
This paper proposes a new algorithm for learning guidance rewards in RL.
Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most common method for training recurrent neural networks, back-propagation through time (BPTT), requires credit information to be propagated backwards through every single step of the forward c…
Improved GFlowNets learn more efficiently with trajectory balance.
New methods improve deep reinforcement learning by accelerating credit assignment.
Meta-learning adjusts TD learning's eligibility trace parameter for more efficient reinforcement learning.
COCOA improves credit assignment in reinforcement learning by measuring contributions to rewards.
Recent advances in deep reinforcement learning algorithms have shown great potential and success for solving many challenging real-world problems, including Go game and robotic applications. Usually, these algorithms need a carefully designed reward function to guide training in each time step. However, in real world, …
We consider the problem of efficient credit assignment in reinforcement learning. In order to efficiently and meaningfully utilize new data, we propose to explicitly assign credit to past decisions based on the likelihood of them having led to the observed outcome. This approach uses new information in hindsight, rathe…
Deep reinforcement learning approaches have shown impressive results in a variety of different domains, however, more complex heterogeneous architectures such as world models require the different neural components to be trained separately instead of end-to-end. While a simple genetic algorithm recently showed end-to-e…
Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically suffer from three core difficulties: temporal credit assignment with sparse rewards, lack of effective exploration, and brittle convergence properties that are extremely …
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively. This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies. This paper provide…
In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, …
LICA learns credit assignment for cooperative agents without explicit formulation.
The paper explores how planning with models improves credit assignment in reinforcement learning.
Survive method improves model-based RL by avoiding terminal states, reducing sample complexity.
Model improves mortgage credit risk prediction with spatio-temporal machine learning.
This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is t…
MRC improves credit assignment in multi-agent LLM systems, achieving high returns and transparency.
Meta-learning framework for credit risk assessment of SMEs, aligning financial statement dates with evaluation dates.
Credit risk analysis improved with a joint model for spatial and temporal effects.
This project proposes using reinforcement learning to train spiking neural networks.
We present a new approach to understanding credit relationships between commercial banks and quoted firms, and with this approach, examine the temporal change in the structure of the Japanese credit network from 1980 to 2005. At each year, the credit network is regarded as a weighted bipartite graph where edges corresp…
Shapley Flow interprets model predictions using a graph-based approach to feature importance.
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Buil…
Deep learning has achieved impressive prediction accuracies in a variety of scientific and industrial domains. However, the nested non-linear feature of deep learning makes the learning highly non-transparent, i.e., it is still unknown how the learning coordinates a huge number of parameters to achieve a decision makin…
MACC learns communication protocols by adapting counterfactual reasoning.
PRISM integrates diverse rewards in MORL, improving sample efficiency and Pareto coverage.
Deep learning improves credit risk assessment without new data.
This work is attached to the BRICS 2013 competition. We propose a two-stage model for dealing with the temporal degradation of credit scoring models. This methodology produced motivating results in a 1-year horizon. We anticipate that it can be extended to other applications of risk assessment with great success. Futur…
We present a recurrent neural network memory that uses sparse coding to create a combinatoric encoding of sequential inputs. Using several examples, we show that the network can associate distant causes and effects in a discrete stochastic process, predict partially-observable higher-order sequences, and enable a DQN a…
CaT-GNN improves credit card fraud detection by integrating causal reasoning into GNNs.
Delta-AI speeds up inference in sparse PGMs by local credit assignment.
Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation. However, the spike generation function is non-differentiable and therefore not directly compatible with the standard error backpropagation algorithm. In this paper, we introduce a new general back…
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient explo…
HED Score improves temporal evaluation of detection accuracy.
Small Medium-sized Enterprises (SMEs) face many obstacles when they try to access credit market. These obstacles are increased if the SMEs are innovative. In this case, financial data are insufficient or even not reliable. Thus, when building a judgemental rating model, mainly based on qualitative criteria (soft inform…
Study finds implicit government guarantee improves municipal investment bond ratings.
The study develops a machine learning system for credit scoring and default prediction.
CREDIT learns to master pair trading with risk-aware RL, outperforming existing methods.
Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning. Previous work has shown that an unbiased estimator of the gradient of the expected loss of SCGs can be derived from a…
The article compares neural networks and logistic regression for credit scoring and introduces a new probability calibration technique.
Credit scoring models support loan approval decisions in the financial services industry. Lenders train these models on data from previously granted credit applications, where the borrowers' repayment behavior has been observed. This approach creates sample bias. The scoring model (i.e., classifier) is trained on accep…
Study evaluates neural networks for corporate credit rating assessment.
Machine learning struggles with temporal data in finance, leading to inaccurate models.