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.
Guarantees for tuning step size using meta-gradient descent.
Meta-gradient RL learns to learn from experience.
Meta-gradient D4PG optimizes performance and constraint adherence in RL.
Evolutionary Strategies optimize hyper-parameters for off-policy learning.
Proposes a meta-learning algorithm to improve semi-supervised learning.
Synthetic data helps prevent forgetting when learning sequentially.
Accelerates policy optimization in RL with optimistic and adaptive updates.
Representations are fundamental to artificial intelligence. The performance of a learning system depends on the type of representation used for representing the data. Typically, these representations are hand-engineered using domain knowledge. More recently, the trend is to learn these representations through stochasti…
AdvImmune improves certifiable robustness of GNNs against adversarial attacks.
A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an effective approach for few-shot learning. In this formulation, meta-parameters are learned in the outer loop, while task-specific m…
Reinforcement learning (RL) has had many successes in both "deep" and "shallow" settings. In both cases, significant hyperparameter tuning is often required to achieve good performance. Furthermore, when nonlinear function approximation is used, non-stationarity in the state representation can lead to learning instabil…
Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradi…
In few-shot learning, typically, the loss function which is applied at test time is the one we are ultimately interested in minimising, such as the mean-squared-error loss for a regression problem. However, given that we have few samples at test time, we argue that the loss function that we are interested in minimising…
Improves meta-learning efficiency with mixed-mode differentiation.
DataRater learns which data points are most valuable for training models.
The goal of reinforcement learning algorithms is to estimate and/or optimise the value function. However, unlike supervised learning, no teacher or oracle is available to provide the true value function. Instead, the majority of reinforcement learning algorithms estimate and/or optimise a proxy for the value function. …
MetAL improves graph classification models with fewer labeled data.
New algorithm improves dataset distillation with 108% improvement on ImageNet.
MSLG generates soft labels to improve DNN performance on noisy datasets.
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. In addition to various regularizers, example reweighting algorithms are popular solutions to these p…
This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad, RMSProp, and AMSGra…
New method optimises learning via surrogate PAC-Bayes bounds.
New framework guarantees convergence of multi-step MAML.
Self-Tuning Actor-Critic improves reinforcement learning performance.
Improved learning to reweight using deep interactions between student and teacher models.
G-Meta learns graph meta-learning from local subgraphs.
Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning. Typically, previous works have approached this issue either by attempting to train a neural network that directly produces updates or by attempting to learn better i…
This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in order to help AI agents rapidly learn. We show that such algorithms are possible via Generative Teaching Networks (GTNs), a general approach tha…