Search Results for author:
Found 14 papers, 2 papers with code
Multitask Online Learning: Listen to the Neighborhood Buzz
We study multitask online learning in a setting where agents can only exchange information with their neighbors on an arbitrary communication network.
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Surrogate kernel-based methods offer a flexible solution to structured output prediction by leveraging the kernel trick in both input and output spaces.
Linear Bandits with Memory: from Rotting to Rising
By choosing the cycle length so as to trade-off approximation and estimation errors, we then prove a bound of order $\sqrt{d}\,(m+1)^{\frac{1}{2}+\max\{\gamma, 0\}}\, T^{3/4}$ (ignoring log factors) on the regret against the optimal sequence of actions, where $T$ is the horizon and $d$ is the dimension of the linear action space.
On Medians of (Randomized) Pairwise Means
Tournament procedures, recently introduced in Lugosi & Mendelson (2016), offer an appealing alternative, from a theoretical perspective at least, to the principle of Empirical Risk Minimization in machine learning.
Fast Kernel Methods for Generic Lipschitz Losses via $p$-Sparsified Sketches
Kernel methods are learning algorithms that enjoy solid theoretical foundations while suffering from important computational limitations.
AdaTask: Adaptive Multitask Online Learning
We introduce and analyze AdaTask, a multitask online learning algorithm that adapts to the unknown structure of the tasks.
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
Motivated by the fact that humans like some level of unpredictability or novelty, and might therefore get quickly bored when interacting with a stationary policy, we introduce a novel non-stationary bandit problem, where the expected reward of an arm is fully determined by the time elapsed since the arm last took part in a switch of actions.
Fighting Selection Bias in Statistical Learning: Application to Visual Recognition from Biased Image Databases
In practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information.
Multitask Online Mirror Descent
We introduce and analyze MT-OMD, a multitask generalization of Online Mirror Descent (OMD) which operates by sharing updates between tasks.
When OT meets MoM: Robust estimation of Wasserstein Distance
Issued from Optimal Transport, the Wasserstein distance has gained importance in Machine Learning due to its appealing geometrical properties and the increasing availability of efficient approximations.
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
In contrast to the empirical mean, the Median-of-Means (MoM) is an estimator of the mean $\theta$ of a square integrable r. v.
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Operator-Valued Kernels (OVKs) and associated vector-valued Reproducing Kernel Hilbert Spaces provide an elegant way to extend scalar kernel methods when the output space is a Hilbert space.
Statistical Learning from Biased Training Samples
With the deluge of digitized information in the Big Data era, massive datasets are becoming increasingly available for learning predictive models.
Autoencoding any Data through Kernel Autoencoders
This paper investigates a novel algorithmic approach to data representation based on kernel methods.
