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.
The paper proves convergence of graph Laplacian with kNN self-tuned kernels.
Self-Tuning Actor-Critic improves reinforcement learning performance.
GIST adapts HMC by tuning parameters based on position and momentum.
In this paper, a new descriptor selection method for selecting an optimal combination of important descriptors of sulfonamide derivatives data, named self tuned reweighted sampling (STRS), is developed. descriptors are defined as the descriptors with large absolute coefficients in a multivariate linear regression model…
Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters …
AlgoPerf competition evaluates neural network training speed-ups.
A new algorithm avoids worst-case outcomes in risky contexts.
Improved SGD with AdaGrad stepsizes adapts to unknown parameters and unbounded gradients.
A new policy for contextual bandits adapts to reward vector shifts.
Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings,…
Stochastic Gradient Descent (SGD) has played a central role in machine learning. However, it requires a carefully hand-picked stepsize for fast convergence, which is notoriously tedious and time-consuming to tune. Over the last several years, a plethora of adaptive gradient-based algorithms have emerged to ameliorate t…
Estimates model performance under distribution shift using domain-invariant predictors.
GAIL is a recent successful imitation learning architecture that exploits the adversarial training procedure introduced in GANs. Albeit successful at generating behaviours similar to those demonstrated to the agent, GAIL suffers from a high sample complexity in the number of interactions it has to carry out in the envi…
New method schedules learning rate without stopping time, outperforming existing methods.
New method reduces regret in nonparametric bandits with unknown covariate shifts.
Improved hypernetwork for efficient neural network hyperparameter tuning.
New hyperparameter ensembles boost neural network performance and uncertainty.
AdaGrad-Norm achieves optimal convergence rates for non-convex objectives without tuning.
The inference of correlated signal fields with unknown correlation structures is of high scientific and technological relevance, but poses significant conceptual and numerical challenges. To address these, we develop the correlated signal inference (CSI) algorithm within information field theory (IFT) and discuss its n…
Improved convergence rate for kNN graph Laplacians with adaptive bandwidth.
Software and hardware co-design and optimization of HPC systems has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design and optimization choices, complex interactions between all software and hardware components, and multiple strict requirements placed on perfor…