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Found 18 papers, 1 papers with code
Exploiting 3D Shape Bias towards Robust Vision
To tackle the problem from a new perspective, we encourage closer collaboration between the robustness and 3D vision communities.
Geon3D: Exploiting 3D Shape Bias towards Building Robust Machine Vision
To tackle the problem from a new perspective, we encourage closer collaboration between the robustness and 3D vision communities.
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs
We study estimation of a gradient-sparse parameter vector $\boldsymbol{\theta}^* \in \mathbb{R}^p$, having strong gradient-sparsity $s^*:=\|\nabla_G \boldsymbol{\theta}^*\|_0$ on an underlying graph $G$.
Alternating Linear Bandits for Online Matrix-Factorization Recommendation
We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time.
Feature Selection using Stochastic Gates
Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions.
Minimax Estimation of Bandable Precision Matrices
In particular, when the distribution over variables is assumed to be multivariate normal, the sparsity pattern in the inverse covariance matrix, commonly referred to as the precision matrix, corresponds to the adjacency matrix representation of the Gauss-Markov graph, which encodes conditional independence statements between variables.
Learning from Comparisons and Choices
This also allows one to compute similarities among users and items to be used for categorization and search.
Scalable Greedy Feature Selection via Weak Submodularity
Furthermore, we show that a bounded submodularity ratio can be used to provide data dependent bounds that can sometimes be tighter also for submodular functions.
On Approximation Guarantees for Greedy Low Rank Optimization
We provide new approximation guarantees for greedy low rank matrix estimation under standard assumptions of restricted strong convexity and smoothness.
Restricted Strong Convexity Implies Weak Submodularity
Our results extend the work of Das and Kempe (2011) from the setting of linear regression to arbitrary objective functions.
Super-resolution estimation of cyclic arrival rates
Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a nonhomogeneous Poisson process.
Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization
We propose a general framework for increasing local stability of Artificial Neural Nets (ANNs) using Robust Optimization (RO).
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions
We develop and analyze stochastic optimization algorithms for problems in which the expected loss is strongly convex, and the optimum is (approximately) sparse.
Iterative ranking from pair-wise comparisons
In most settings, in addition to obtaining ranking, finding ‘scores’ for each object (e. g. player’s rating) is of interest to understanding the intensity of the preferences.
Rank Centrality: Ranking from Pair-wise Comparisons
To study the efficacy of the algorithm, we consider the popular Bradley-Terry-Luce (BTL) model (equivalent to the Multinomial Logit (MNL) for pair-wise comparisons) in which each object has an associated score which determines the probabilistic outcomes of pair-wise comparisons between objects.
Fast global convergence rates of gradient methods for high-dimensional statistical recovery
Many statistical $M$-estimators are based on convex optimization problems formed by the weighted sum of a loss function with a norm-based regularizer.
A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers
The estimation of high-dimensional parametric models requires imposing some structure on the models, for instance that they be sparse, or that matrix structured parameters have low rank.
Phase transitions for high-dimensional joint support recovery
We consider the following instance of transfer learning: given a pair of regression problems, suppose that the regression coefficients share a partially common support, parameterized by the overlap fraction $\overlap$ between the two supports.
