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Detecting and Measuring Confounding Using Causal Mechanism Shifts
Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables.
The Fine-Grained Complexity of Gradient Computation for Training Large Language Models
To train an LLM, one needs to alternatingly run'forward' computations and'backward' computations.
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
This paradigm has been challenged by practical deployments, which are more dynamic in nature.
A Canonicalization Perspective on Invariant and Equivariant Learning George Ma
In many applications, we desire neural networks to exhibit invariance or equivari-ance to certain groups due to symmetries inherent in the data. Recently, frame-averaging methods emerged to be a unified framework for attaining symmetries efficiently by averaging over input-dependent subsets of the group, i.e., frames. What we currently lack is a principled understanding of the design of frames.
LibAMM: Empirical Insights into Approximate Computing for Accelerating Matrix Multiplication
To support and inspire ongoing and future research, we introduce LibAMM, a framework within the PyTorch ecosystem.