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Homotopy Analysis for Tensor PCA
Anima Anandkumar, Yuan Deng, Rong Ge +1
Developing efficient and guaranteed nonconvex algorithms has been an important challenge in modern machine learning. Algorithms with good empirical performance such as stochastic g…
Unsupervised learning of transcriptional regulatory networks via latent tree graphical models
Anthony Gitter, Furong Huang, Ragupathyraj Valluvan +2
Gene expression is a readily-observed quantification of transcriptional activity and cellular state that enables the recovery of the relationships between regulators and their targ…
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
Planning plays an important role in the broad class of decision theory. Planning has drawn much attention in recent work in the robotics and sequential decision making areas. Recen…
Online and Differentially-Private Tensor Decomposition
Yining Wang, Animashree Anandkumar
In this paper, we resolve many of the key algorithmic questions regarding robustness, memory efficiency, and differential privacy of tensor decomposition. We propose simple variant…
Tensor Contractions with Extended BLAS Kernels on CPU and GPU
Yang Shi, U. N. Niranjan, Animashree Anandkumar +1
Tensor contractions constitute a key computational ingredient of numerical multi-linear algebra. However, as the order and dimension of tensors grow, the time and space complexitie…
Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition
Furong Huang, Animashree Anandkumar
Unsupervised text embeddings extraction is crucial for text understanding in machine learning. Word2Vec and its variants have received substantial success in mapping words with sim…
Spectral Methods for Correlated Topic Models
Forough Arabshahi, Animashree Anandkumar
In this paper, we propose guaranteed spectral methods for learning a broad range of topic models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the li…
Training Input-Output Recurrent Neural Networks through Spectral Methods
Hanie Sedghi, Anima Anandkumar
We consider the problem of training input-output recurrent neural networks (RNN) for sequence labeling tasks. We propose a novel spectral approach for learning the network paramete…
Efficient approaches for escaping higher order saddle points in non-convex optimization
Anima Anandkumar, Rong Ge
Local search heuristics for non-convex optimizations are popular in applied machine learning. However, in general it is hard to guarantee that such algorithms even converge to a lo…
Reinforcement Learning of POMDPs using Spectral Methods
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have be…