We provide theoretical convergence guarantees for score-based generative models (SGMs)
such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of …

A long-standing open question in computational learning theory is to prove NP-hardness of
learning efficient programs, the setting of which is in between proper learning and improper …

We give superpolynomial statistical query (SQ) lower bounds for learning two-hidden-layer
ReLU networks with respect to Gaussian inputs in the standard (noise-free) model. No …

We consider the problem of learning an unknown ReLU network with respect to Gaussian
inputs and obtain the first nontrivial results for networks of depth more than two. We give an …

The multi-head attention layer is one of the key components of the transformer architecture
that sets it apart from traditional feed-forward models. Given a sequence length $ k …

We show a simple reduction which demonstrates the cryptographic hardness of learning a
single periodic neuron over isotropic Gaussian distributions in the presence of noise. More …

We present polynomial time and sample efficient algorithms for learning an unknown depth-
2 feedforward neural network with general ReLU activations, under mild non-degeneracy …

Understanding when neural networks can be learned efficientlyis a fundamental question in
learning theory. Existing hardness results suggest that assumptions on both the input …

We show hardness of improperly learning halfspaces in the agnostic model, both in the
distribution-independent as well as the distribution-specific setting, based on the assumption …

Motivated by the recent empirical successes of deep generative models, we study the
computational complexity of the following unsupervised learning problem. For an unknown …