Search Results for author:
Found 24 papers, 3 papers with code
Integrating knowledge graph embeddings to improve mention representation for bridging anaphora resolution
Specifically, we convert the external knowledge source (in this case, WordNet) into a graph, and learn embeddings of the graph nodes of low dimension to capture the crucial features of the graph topology and, at the same time, rich semantic information.
Federated Wasserstein Distance
We introduce a principled way of computing the Wasserstein distance between two distributions in a federated manner.
Personalised Federated Learning On Heterogeneous Feature Spaces
Most personalised federated learning (FL) approaches assume that raw data of all clients are defined in a common subspace i. e. all clients store their data according to the same schema.
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
The Sliced-Wasserstein distance (SW) is a computationally efficient and theoretically grounded alternative to the Wasserstein distance.
Differentially Private Sliced Wasserstein Distance
Developing machine learning methods that are privacy preserving is today a central topic of research, with huge practical impacts.
Photonic Differential Privacy with Direct Feedback Alignment
Optical Processing Units (OPUs) -- low-power photonic chips dedicated to large scale random projections -- have been used in previous work to train deep neural networks using Direct Feedback Alignment (DFA), an effective alternative to backpropagation.
Partial Trace Regression and Low-Rank Kraus Decomposition
The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs.
Quantum Bandits
We consider the quantum version of the bandit problem known as {\em best arm identification} (BAI).
QuicK-means: Acceleration of K-means by learning a fast transform
K-means -- and the celebrated Lloyd algorithm -- is more than the clustering method it was originally designed to be.
Frank-Wolfe Algorithm for the Exact Sparse Problem
In this paper, we study the properties of the Frank-Wolfe algorithm to solve the \ExactSparse reconstruction problem.
Bandits Dueling on Partially Ordered Sets
We propose an algorithm, UnchainedBandits, that efficiently finds the set of optimal arms, or Pareto front, of any poset even when pairs of comparable arms cannot be a priori distinguished from pairs of incomparable arms, with a set of minimal assumptions.
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm
This paper presents a new, efficient method for learning task-specific word vectors using a variant of the Passive-Aggressive algorithm.
Cornering Stationary and Restless Mixing Bandits with Remix-UCB
We study the restless bandit problem where arms are associated with stationary $\varphi$-mixing processes and where rewards are therefore dependent: the question that arises from this setting is that of carefully recovering some independence by `ignoring' the values of some rewards.
Greedy methods, randomization approaches and multi-arm bandit algorithms for efficient sparsity-constrained optimization
Several sparsity-constrained algorithms such as Orthogonal Matching Pursuit or the Frank-Wolfe algorithm with sparsity constraints work by iteratively selecting a novel atom to add to the current non-zero set of variables.
From Cutting Planes Algorithms to Compression Schemes and Active Learning
Cutting-plane methods are well-studied localization(and optimization) algorithms.
Unconfused ultraconservative multiclass algorithms
We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting.
On Generalizing the C-Bound to the Multiclass and Multi-label Settings
The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier.
Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso
Recent computational strategies based on screening tests have been proposed to accelerate algorithms addressing penalized sparse regression problems such as the Lasso.
On the Generalization of the C-Bound to Structured Output Ensemble Methods
This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs.
Stationary Mixing Bandits
To do so, we provide a UCB strategy together with a general regret analysis for the case where the size of the independence blocks (the ignored rewards) is fixed and we go a step beyond by providing an algorithm that is able to compute the size of the independence blocks from the data.
Unconfused Ultraconservative Multiclass Algorithms
We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting.
Confusion-Based Online Learning and a Passive-Aggressive Scheme
This paper provides the first ---to the best of our knowledge--- analysis of online learning algorithms for multiclass problems when the {\em confusion} matrix is taken as a performance measure.
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification
In this work, we propose a PAC-Bayes bound for the generalization risk of the Gibbs classifier in the multi-class classification framework.
Empirical Bernstein Inequalities for U-Statistics
They are expressed with respect to empirical estimates of either the variance of q or the conditional variance that appears in the Bernstein-type inequality for U-statistics derived by Arcones [2].
