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Found 8 papers, 1 papers with code
Cryptographic Hardness of Score Estimation
We show that $L^2$-accurate score estimation, in the absence of strong assumptions on the data distribution, is computationally hard even when sample complexity is polynomial in the relevant problem parameters.
Learning Single-Index Models with Shallow Neural Networks
Single-index models are a class of functions given by an unknown univariate ``link'' function applied to an unknown one-dimensional projection of the input.
Lattice-Based Methods Surpass Sum-of-Squares in Clustering
Prior work on many similar inference tasks portends that such lower bounds strongly suggest the presence of an inherent statistical-to-computational gap for clustering, that is, a parameter regime where the clustering task is statistically possible but no polynomial-time algorithm succeeds.
On the Cryptographic Hardness of Learning Single Periodic Neurons
More precisely, our reduction shows that any polynomial-time algorithm (not necessarily gradient-based) for learning such functions under small noise implies a polynomial-time quantum algorithm for solving worst-case lattice problems, whose hardness form the foundation of lattice-based cryptography.
Self-Supervised Motion Retargeting with Safety Guarantee
In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos.
Impact-driven Exploration with Contrastive Unsupervised Representations
That is, we train the embedding with respect to cosine similarity, where we define two observations to be similar if the agent can reach one observation from the other within a few steps, and define impact in terms of this similarity measure.
Evaluating representations by the complexity of learning low-loss predictors
We consider the problem of evaluating representations of data for use in solving a downstream task.
Continuous LWE
We introduce a continuous analogue of the Learning with Errors (LWE) problem, which we name CLWE.
