Aditya Krishna Menon

Sparse Robust Classification via the Kernel Mean

2015 · arXiv (Cornell University)

Many leading classification algorithms output a classifier that is a weighted average of kernel evaluations. Optimizing these weights is a nontrivial problem that still attracts much research effort. Furthermore, explaining these methods to the uninitiated …

Low-Rank Linear Cold-Start Recommendation from Social Data

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

The cold-start problem involves recommendation of content to new users of a system, for whom there is no historical preference information available. This proves a challenge for collaborative filtering algorithms that inherently rely on such …

f-GANs in an Information Geometric Nutshell

2017 · arXiv (Cornell University)

Nowozin \textit{et al} showed last year how to extend the GAN \textit{principle} to all $f$-divergences. The approach is elegant but falls short of a full description of the supervised game, and says little about the …

Monge blunts Bayes: Hardness Results for Adversarial Training

2018 · arXiv (Cornell University)

The last few years have seen a staggering number of empirical studies of the robustness of neural networks in a model of adversarial perturbations of their inputs. Most rely on an adversary which carries out …

On the Reproducibility of Neural Network Predictions

2021 · arXiv (Cornell University)

Standard training techniques for neural networks involve multiple sources of randomness, e.g., initialization, mini-batch ordering and in some cases data augmentation. Given that neural networks are heavily over-parameterized in practice, such randomness can cause {\em …

AutoRec

2015

This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec's compact and efficiently trainable model outperforms state-of-the-art CF techniques (biased matrix factorization, RBM-CF and LLORMA) on the Movielens and Netflix datasets.