You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

no code implementations • ACL 2022 • Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida

When working with textual data, a natural application of disentangled representations is fair classification where the goal is to make predictions without being biased (or influenced) by sensitive attributes that may be present in the data (e. g., age, gender or race).

1 code implementation • NeurIPS 2021 • Olivier Veilleux, Malik Boudiaf, Pablo Piantanida, Ismail Ben Ayed

Transductive inference is widely used in few-shot learning, as it leverages the statistics of the unlabeled query set of a few-shot task, typically yielding substantially better performances than its inductive counterpart.

1 code implementation • 30 Mar 2022 • Georg Pichler, Marco Romanelli, Leonardo Rey Vega, Pablo Piantanida

Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clients or between the clients and the central server.

1 code implementation • 17 Mar 2022 • Ganesh Del Grosso, Hamid Jalalzai, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida

The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in machine learning today.

1 code implementation • ICLR 2022 • Eduardo Dadalto Camara Gomes, Florence Alberge, Pierre Duhamel, Pablo Piantanida

Reliable out-of-distribution (OOD) detection is fundamental to implementing safer modern machine learning (ML) systems.

1 code implementation • 14 Feb 2022 • Georg Pichler, Pierre Colombo, Malik Boudiaf, Gunther Koliander, Pablo Piantanida

Mutual Information (MI) has been widely used as a loss regularizer for training neural networks.

no code implementations • 10 Dec 2021 • Matias Vera, Leonardo Rey Vega, Pablo Piantanida

In this work, we introduce an analysis based on point-wise PAC approach over the generalization gap considering the mismatch of testing based on the accuracy metric and training on the negative log-loss.

1 code implementation • 2 Dec 2021 • Pierre Colombo, Chloe Clavel, Pablo Piantanida

In this paper, we introduce InfoLM a family of untrained metrics that can be viewed as a string-based metric that addresses the aforementioned flaws thanks to a pre-trained masked language model.

2 code implementations • EMNLP 2021 • Pierre Colombo, Guillaume Staerman, Chloe Clavel, Pablo Piantanida

A new metric \texttt{BaryScore} to evaluate text generation based on deep contextualized embeddings e. g., BERT, Roberta, ELMo) is introduced.

no code implementations • 17 Jul 2021 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

We formulate this as the problem of learning a sparse representation of SMs data with minimum information leakage and maximum utility.

no code implementations • 14 Jul 2021 • Carlos Pinzón, Catuscia Palamidessi, Pablo Piantanida, Frank Valencia

One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy.

2 code implementations • 23 Jun 2021 • Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony, Jose Dolz, Ismail Ben Ayed, Pablo Piantanida

We motivate our transductive loss by deriving a formal relation between the classification accuracy and mutual-information maximization.

1 code implementation • 12 Jun 2021 • Marine Picot, Francisco Messina, Malik Boudiaf, Fabrice Labeau, Ismail Ben Ayed, Pablo Piantanida

Adversarial robustness has become a topic of growing interest in machine learning since it was observed that neural networks tend to be brittle.

1 code implementation • NeurIPS 2021 • Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida

Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations where DNN are implemented as "black boxes".

no code implementations • 9 May 2021 • Ganesh Del Grosso, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida

Machine Learning services are being deployed in a large range of applications that make it easy for an adversary, using the algorithm and/or the model, to gain access to sensitive data.

no code implementations • ACL 2021 • Pierre Colombo, Chloe Clavel, Pablo Piantanida

We show the superiority of this method on fair classification and on textual style transfer tasks.

Ranked #1 on Text Style Transfer on Yelp Review Dataset (Large) (BLEU metric)

no code implementations • 1 Jan 2021 • Pierre Colombo, Chloé Clavel, Pablo Piantanida

Learning disentangled representations of textual data is essential for many natural language tasks such as fair classification (\textit{e. g.} building classifiers whose decisions cannot disproportionately hurt or benefit specific groups identified by sensitive attributes), style transfer and sentence generation, among others.

2 code implementations • CVPR 2021 • Malik Boudiaf, Hoel Kervadec, Ziko Imtiaz Masud, Pablo Piantanida, Ismail Ben Ayed, Jose Dolz

We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances -- an aspect often overlooked in the literature in favor of the meta-learning paradigm.

no code implementations • 6 Dec 2020 • Ganesh Del Grosso, Georg Pichler, Pablo Piantanida

However, the use of power consumption data raises significant privacy concerns, as this data usually belongs to clients of a power company.

1 code implementation • NeurIPS 2020 • Malik Boudiaf, Imtiaz Ziko, Jérôme Rony, Jose Dolz, Pablo Piantanida, Ismail Ben Ayed

We introduce Transductive Infomation Maximization (TIM) for few-shot learning.

no code implementations • 20 Nov 2020 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

In this paper, we study this problem in the context of time series data and smart meters (SMs) power consumption measurements in particular.

no code implementations • 22 Oct 2020 • Matias Vera, Leonardo Rey Vega, Pablo Piantanida

In practice, this behaviour is controlled by various--sometimes heuristics--regularization techniques, which are motivated by developing upper bounds to the generalization error.

1 code implementation • 25 Aug 2020 • Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony, José Dolz, Pablo Piantanida, Ismail Ben Ayed

We introduce Transductive Infomation Maximization (TIM) for few-shot learning.

no code implementations • 29 Jun 2020 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

On the one hand, the releaser in the CAL method, by getting supervision from the actual values of the private variables and feedback from the adversary performance, tries to minimize the adversary log-likelihood.

no code implementations • 10 Jun 2020 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

Unlike previous studies, we model the whole temporal correlation in the data to learn the MI in its general form and use a neural network to estimate the MI-based reward signal to guide the PCMU learning process.

1 code implementation • 9 May 2020 • Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi, Pablo Piantanida

A feature of our approach is that it does not require to estimate the conditional probabilities, and that it is suitable for a large class of ML algorithms.

1 code implementation • ECCV 2020 • Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Eric Granger, Marco Pedersoli, Pablo Piantanida, Ismail Ben Ayed

Second, we show that, more generally, minimizing the cross-entropy is actually equivalent to maximizing the mutual information, to which we connect several well-known pairwise losses.

Ranked #7 on Metric Learning on In-Shop (using extra training data)

no code implementations • 10 Mar 2020 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time.

no code implementations • 7 Feb 2020 • Georg Pichler, Pablo Piantanida, Günther Koliander

In particular, we provide confidence bounds for simple histogram based estimation of differential entropy from a fixed number of samples, assuming that the probability density function is Lipschitz continuous with known Lipschitz constant and known, bounded support.

no code implementations • 14 Jun 2019 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

In this paper, we focus on real-time privacy threats, i. e., potential attackers that try to infer sensitive information from SMs data in an online fashion.

no code implementations • 28 May 2019 • Matias Vera, Pablo Piantanida, Leonardo Rey Vega

Our main result is that the testing gap between the empirical cross-entropy and its statistical expectation (measured with respect to the testing probability law) can be bounded with high probability by the mutual information between the input testing samples and the corresponding representations, generated by the encoder obtained at training time.

no code implementations • MIDL 2019 • Georg Pichler, Jose Dolz, Ismail Ben Ayed, Pablo Piantanida

We juxtapose our approach to state-of-the-art segmentation adaptation via adversarial training in the network-output space.

1 code implementation • 26 Feb 2018 • Clément Feutry, Pablo Piantanida, Yoshua Bengio, Pierre Duhamel

Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations.

no code implementations • 14 Feb 2018 • Matías Vera, Pablo Piantanida, Leonardo Rey Vega

This paper presents a sample-dependent bound on the generalization gap of the cross-entropy loss that scales with the information complexity (IC) of the representations, meaning the mutual information between inputs and their representations.

no code implementations • 19 Nov 2017 • Matías Vera, Leonardo Rey Vega, Pablo Piantanida

This paper investigates, from information theoretic grounds, a learning problem based on the principle that any regularity in a given dataset can be exploited to extract compact features from data, i. e., using fewer bits than needed to fully describe the data itself, in order to build meaningful representations of a relevant content (multiple labels).

no code implementations • 14 Nov 2017 • Qianqian Yang, Pablo Piantanida, Deniz Gündüz

Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variable, quantified by the mutual information.

no code implementations • 5 Apr 2016 • Matías Vera, Leonardo Rey Vega, Pablo Piantanida

On the other hand, in CDIB there are two cooperating encoders which separately observe $X_1$ and $X_2$ and a third node which can listen to the exchanges between the two encoders in order to obtain information about a hidden variable $Y$.

no code implementations • 15 Feb 2016 • Georg Pichler, Pablo Piantanida, Gerald Matz

We study a novel multi-terminal source coding setup motivated by the biclustering problem.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.