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Found 28 papers, 6 papers with code
Replicability is Asymptotically Free in Multi-armed Bandits
For the analysis of these algorithms, we propose a principled approach to limiting the probability of nonreplication.
PMSM transient response optimization by end-to-end optimal control
The current vector trajectories of the RNN showed that the RNN could automatically determine arbitrary trajectories in the flux-weakening region in accordance with an arbitrarily designed loss function.
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
Variational autoencoders (VAEs) are one of the deep generative models that have experienced enormous success over the past decades.
Sparsification of Decomposable Submodular Functions
The underlying submodular functions for many of these tasks are decomposable, i. e., they are sum of several simple submodular functions.
Local Algorithms for Estimating Effective Resistance
Effective resistance is an important metric that measures the similarity of two vertices in a graph.
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities.
RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities.
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits
We propose novel algorithms with first- and second-order regret bounds for adversarial linear bandits.
Downsampling for Testing and Learning in Product Distributions
For many important classes of functions, such as intersections of halfspaces, polynomial threshold functions, convex sets, and $k$-alternating functions, the known algorithms either have complexity that depends on the support size of the distribution, or are proven to work only for specific examples of product distributions.
Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints
In this paper, we study the problem of maximizing monotone submodular functions subject to matroid constraints in the framework of differential privacy.
Hypergraph Clustering Based on PageRank
For both algorithms, we discuss theoretical guarantees on the conductance of the output vertex set.
Average Sensitivity of Spectral Clustering
To make reliable and efficient decisions based on spectral clustering, we assess the stability of spectral clustering against edge perturbations in the input graph using the notion of average sensitivity, which is the expected size of the symmetric difference of the output clusters before and after we randomly remove edges.
Statistical Learning with Conditional Value at Risk
For convex and Lipschitz loss functions, we show that our algorithm has $O(1/\sqrt{n})$-convergence to the optimal CVaR, where $n$ is the number of samples.
Approximability of Monotone Submodular Function Maximization under Cardinality and Matroid Constraints in the Streaming Model
The latter one almost matches our lower bound of $\frac{K}{2K-1}$ for a matroid constraint, which almost settles the approximation ratio for a matroid constraint that can be obtained by a streaming algorithm whose space complexity is independent of $n$.
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding
Existing methods for learning KGEs can be seen as a two-stage process where (a) entities and relations in the knowledge graph are represented using some linear algebraic structures (embeddings), and (b) a scoring function is defined that evaluates the strength of a relation that holds between two entities using the corresponding relation and entity embeddings.
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
In this paper, we study random subsampling of Gaussian process regression, one of the simplest approximation baselines, from a theoretical perspective.
Canonical and Compact Point Cloud Representation for Shape Classification
We present a novel compact point cloud representation that is inherently invariant to scale, coordinate change and point permutation.
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of stochastic variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct.
Spectral Normalization for Generative Adversarial Networks
One of the challenges in the study of generative adversarial networks is the instability of its training.
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on STL-10
Fitting Low-Rank Tensors in Constant Time
Then, we show that the residual error of the Tucker decomposition of $\tilde{X}$ is sufficiently close to that of $X$ with high probability.
Using $k$-way Co-occurrences for Learning Word Embeddings
Co-occurrences between two words provide useful insights into the semantics of those words.
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
We investigate the generalizability of deep learning based on the sensitivity to input perturbation.
Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent
In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct.
Minimizing Quadratic Functions in Constant Time
A sampling-based optimization method for quadratic functions is proposed.
Monotone k-Submodular Function Maximization with Size Constraints
A $k$-submodular function is a generalization of a submodular function, where the input consists of $k$ disjoint subsets, instead of a single subset, of the domain. Many machine learning problems, including influence maximization with $k$ kinds of topics and sensor placement with $k$ kinds of sensors, can be naturally modeled as the problem of maximizing monotone $k$-submodular functions. In this paper, we give constant-factor approximation algorithms for maximizing monotone $k$-submodular functions subject to several size constraints. The running time of our algorithms are almost linear in the domain size. We experimentally demonstrate that our algorithms outperform baseline algorithms in terms of the solution quality.
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice
We show that the generalized submodular cover problem can be applied to various problems and devise a bicriteria approximation algorithm.
Learning Word Representations from Relational Graphs
To evaluate the accuracy of the word representations learnt using the proposed method, we use the learnt word representations to solve semantic word analogy problems.
Half-integrality, LP-branching and FPT Algorithms
In addition to the insight into problems with half-integral relaxations, our results yield a range of new and improved FPT algorithms, including an $O^*(|\Sigma|^{2k})$-time algorithm for node-deletion Unique Label Cover with label set $\Sigma$ and an $O^*(4^k)$-time algorithm for Group Feedback Vertex Set, including the setting where the group is only given by oracle access.
Data Structures and Algorithms
