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Spectral Algorithms Optimally Recover Planted Sub-structures
Spectral algorithms are an important building block in machine learning and graph algorithms.
On the Power of Differentiable Learning versus PAC and SQ Learning
With fine enough precision relative to minibatch size, namely when $b \rho$ is small enough, SGD can go beyond SQ learning and simulate any sample-based learning algorithm and thus its learning power is equivalent to that of PAC learning; this extends prior work that achieved this result for $b=1$.
Spoofing Generalization: When Can't You Trust Proprietary Models?
In this work, we study the computational complexity of determining whether a machine learning model that perfectly fits the training data will generalizes to unseen data.
Learning to Sample from Censored Markov Random Fields
These are Markov Random Fields where some of the nodes are censored (not observed).
On the universality of deep learning
This paper shows that deep learning, i. e., neural networks trained by SGD, can learn in polytime any function class that can be learned in polytime by some algorithm, including parities.
Poly-time universality and limitations of deep learning
Therefore deep learning provides a universal learning paradigm: it was known that the approximation and estimation errors could be controlled with poly-size neural nets, using ERM that is NP-hard; this new result shows that the optimization error can also be controlled with SGD in poly-time.
Provable limitations of deep learning
As the success of deep learning reaches more grounds, one would like to also envision the potential limits of deep learning.
Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation
The stochastic block model (SBM) has long been studied in machine learning and network science as a canonical model for clustering and community detection.
Detection in the stochastic block model with multiple clusters: proof of the achievability conjectures, acyclic BP, and the information-computation gap
In a paper that initiated the modern study of the stochastic block model, Decelle et al., backed by Mossel et al., made the following conjecture: Denote by $k$ the number of balanced communities, $a/n$ the probability of connecting inside communities and $b/n$ across, and set $\mathrm{SNR}=(a-b)^2/(k(a+(k-1)b)$; for any $k \geq 2$, it is possible to detect communities efficiently whenever $\mathrm{SNR}>1$ (the KS threshold), whereas for $k\geq 4$, it is possible to detect communities information-theoretically for some $\mathrm{SNR}<1$.
Recovering communities in the general stochastic block model without knowing the parameters
Most recent developments on the stochastic block model (SBM) rely on the knowledge of the model parameters, or at least on the number of communities.
