Statistical Learning
Efficient Training of Energy-Based Models Using Jarzynski Equality
Their performance is well measured by the cross-entropy (CE) of the model distribution relative to the data distribution.
Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
Some related lower bounds include the work of Backurs et al. [2017] that solving kernel Support V ector Machines (SVM), ridge regression, or Principal Component Analysis (PCA) problems to high accuracy or approximating kernel density estimates up to a constant factor for kernels with
Accelerating ERM for data-driven algorithm design using output-sensitive techniques
Data-driven algorithm design is a promising, learning-based approach for beyond worst-case analysis of algorithms with tunable parameters. An important open problem is the design of computationally efficient data-driven algorithms for combinatorial algorithm families with multiple parameters.
84ca3f2d9d9bfca13f69b48ea63eb4a5-Paper-Conference.pdf
Event cameras are neuromorphic sensors that summarize the evolving world as a stream of events . Each event describes the pixel coordinates, time, and polarity of an intensity change.
Automatically Learning Hybrid Digital Twins of Dynamical Systems Samuel Holt
However, existing approaches to DTs often struggle to generalize to unseen conditions in data-scarce settings, a crucial requirement for such models. To address these limitations, our work begins by establishing the essential desiderata for effective DTs.
TabEBM: AT abular Data Augmentation Method with Distinct Class-Specific Energy-Based Models
Figure 1: Evaluation of T abEBM and other state-of-the-art tabular generative methods across six key metrics (larger area indicates better performance).