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Information-theoretic metric learning
TLDR
An information-theoretic approach to learning a Mahalanobis distance function that can handle a wide variety of constraints and can optionally incorporate a prior on the distance function and derive regret bounds for the resulting algorithm. Expand
Sparse Local Embeddings for Extreme Multi-label Classification
TLDR
The SLEEC classifier is developed for learning a small ensemble of local distance preserving embeddings which can accurately predict infrequently occurring (tail) labels and can make significantly more accurate predictions then state-of-the-art methods including both embedding-based as well as tree-based methods. Expand
Low-rank matrix completion using alternating minimization
TLDR
This paper presents one of the first theoretical analyses of the performance of alternating minimization for matrix completion, and the related problem of matrix sensing, and shows that alternating minimizations guarantees faster convergence to the true matrix, while allowing a significantly simpler analysis. Expand
Large-scale Multi-label Learning with Missing Labels
TLDR
This paper studies the multi-label problem in a generic empirical risk minimization (ERM) framework and develops techniques that exploit the structure of specific loss functions - such as the squared loss function - to obtain efficient algorithms. Expand
Guaranteed Rank Minimization via Singular Value Projection
TLDR
Results show that the SVP-Newton method is significantly robust to noise and performs impressively on a more realistic power-law sampling scheme for the matrix completion problem. Expand
Phase Retrieval Using Alternating Minimization
TLDR
This work represents the first theoretical guarantee for alternating minimization (albeit with resampling) for any variant of phase retrieval problems in the non-convex setting. Expand
Non-convex Robust PCA
TLDR
A new provable method for robust PCA, where the task is to recover a low-rank matrix, which is corrupted with sparse perturbations, which represents one of the few instances of global convergence guarantees for non-convex methods. Expand
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
TLDR
This work provides the first analysis for IHT-style methods in the high dimensional statistical setting with bounds that match known minimax lower bounds and extends the analysis to the problem of low-rank matrix recovery. Expand
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
TLDR
The FastRNN and FastGRNN algorithms are developed to address the twin RNN limitations of inaccurate training and inefficient prediction and to be deployed on severely resource-constrained IoT microcontrollers too tiny to store other RNN models. Expand
Online Metric Learning and Fast Similarity Search
TLDR
This work presents a new online metric learning algorithm that updates a learned Mahalanobis metric based on LogDet regularization and gradient descent and develops an online locality-sensitive hashing scheme which leads to efficient updates to data structures used for fast approximate similarity search. Expand
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