Statistical Learning
Unsupervised Discovery of Formulas for Mathematical Constants
These metrics enable the first automated clustering of mathematical formulas.
Matching the Statistical Query Lower Bound for k-Sparse Parity Problems with Sign Stochastic Gradient Descent
's in this subset is even or odd.
Symmetry Discovery Beyond Affine Transformations
Symmetry detection can improve various machine learning tasks.
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
However, enabling human trust and control over FL systems requires understanding the evolving behaviour of clients, whether beneficial or detrimental for the training, which still represents a key challenge in the current literature.
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions
We propose a method for metric-scale monocular depth estimation. Inferring depth from a single image is an ill-posed problem due to the loss of scale from perspective projection during the image formation process.
Causal Dependence Plots
To use artificial intelligence and machine learning models wisely we must understand how they interact with the world, including how they depend causally on data inputs. In this work we develop Causal Dependence Plots (CDPs) to visualize how a model's predicted outcome depends on changes in a given predictor
Learning segmentation from point trajectories
Segmentation, the task of delineating and isolating distinct objects, is a fundamental problem in computer vision. Much of the current approaches are supervised, relying on expensive manual annotations.
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models
The predominant de facto paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on different subgroups.