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06/06/2023
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection
A key feature of out-of-distribution (OOD) detection is to exploit a tra...
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06/02/2023
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection
Misclassification detection is an important problem in machine learning,...
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02/20/2023
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection
Out-of-distribution (OOD) detection is a rapidly growing field due to ne...
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02/04/2023
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Multi-armed adversarial attacks, in which multiple algorithms and object...
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01/20/2023
Open-Set Likelihood Maximization for Few-Shot Learning
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classi...
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12/19/2022
Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation
Neural machine translation (NMT) has become the de-facto standard in rea...
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12/18/2022
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data
As more and more conversational and translation systems are deployed in ...
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11/24/2022
Beyond Mahalanobis-Based Scores for Textual OOD Detection
Deep learning methods have boosted the adoption of NLP systems in real-l...
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08/31/2022
The Glass Ceiling of Automatic Evaluation in Natural Language Generation
Automatic evaluation metrics capable of replacing human judgments are cr...
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08/30/2022
On the (Im)Possibility of Estimating Various Notions of Differential Privacy
We analyze to what extent final users can infer information about the le...
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06/30/2022
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors
Detection of adversarial examples has been a hot topic in the last years...
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06/18/2022
Model-Agnostic Few-Shot Open-Set Recognition
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classi...
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05/07/2022
Learning Disentangled Textual Representations via Statistical Measures of Similarity
When working with textual data, a natural application of disentangled re...
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04/24/2022
Realistic Evaluation of Transductive Few-Shot Learning
Transductive inference is widely used in few-shot learning, as it levera...
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03/30/2022
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning
Federated Learning is expected to provide strong privacy guarantees, as ...
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03/17/2022
Leveraging Adversarial Examples to Quantify Membership Information Leakage
The use of personal data for training machine learning systems comes wit...
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03/15/2022
Igeood: An Information Geometry Approach to Out-of-Distribution Detection
Reliable out-of-distribution (OOD) detection is fundamental to implement...
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02/14/2022
KNIFE: Kernelized-Neural Differential Entropy Estimation
Mutual Information (MI) has been widely used as a loss regularizer for t...
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12/10/2021
PACMAN: PAC-style bounds accounting for the Mismatch between Accuracy and Negative log-loss
The ultimate performance of machine learning algorithms for classificati...
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12/02/2021
InfoLM: A New Metric to Evaluate Summarization Data2Text Generation
Assessing the quality of natural language generation systems through hum...
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08/27/2021
Automatic Text Evaluation through the Lens of Wasserstein Barycenters
A new metric to evaluate text generation based on deep contextualized e...
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07/14/2021
On the impossibility of non-trivial accuracy under fairness constraints
One of the main concerns about fairness in machine learning (ML) is that...
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07/11/2021
On Universal D-Semifaithful Coding for Memoryless Sources with Infinite Alphabets
The problem of variable length and fixed-distortion universal source cod...
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06/23/2021
Mutual-Information Based Few-Shot Classification
We introduce Transductive Infomation Maximization (TIM) for few-shot lea...
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06/13/2021
Finite-Length Bounds on Hypothesis Testing Subject to Vanishing Type I Error Restrictions
A central problem in Binary Hypothesis Testing (BHT) is to determine the...
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06/12/2021
Adversarial Robustness via Fisher-Rao Regularization
Adversarial robustness has become a topic of growing interest in machine...
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06/04/2021
DOCTOR: A Simple Method for Detecting Misclassification Errors
Deep neural networks (DNNs) have shown to perform very well on large sca...
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05/09/2021
Bounding Information Leakage in Machine Learning
Machine Learning services are being deployed in a large range of applica...
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05/06/2021
A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations
Learning disentangled representations of textual data is essential for m...
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12/11/2020
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
Few-shot segmentation has recently attracted substantial interest, with ...
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12/06/2020
Privacy-Preserving Synthetic Smart Meters Data
Power consumption data is very useful as it allows to optimize power gri...
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11/20/2020
Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release
The explosion of data collection has raised serious privacy concerns in ...
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10/22/2020
The Role of Mutual Information in Variational Classifiers
Overfitting data is a well-known phenomenon related with the generation ...
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08/25/2020
Transductive Information Maximization For Few-Shot Learning
We introduce Transductive Infomation Maximization (TIM) for few-shot lea...
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05/09/2020
Estimating g-Leakage via Machine Learning
This paper considers the problem of estimating the information leakage o...
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02/07/2020
On the Estimation of Information Measures of Continuous Distributions
The estimation of information measures of continuous distributions based...
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01/14/2020
An Index Coding Approach to Caching with Uncoded Cache Placement
Caching is an efficient way to reduce network traffic congestion during ...
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10/28/2019
Finite Sample-Size Regime of Testing Against Independence with Communication Constraints
The central problem of Hypothesis Testing (HT) consists in determining t...
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06/14/2019
Deep Recurrent Adversarial Learning for Privacy-Preserving Smart Meter Data Release
Smart Meters (SMs) are an important component of smart electrical grids,...
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05/28/2019
Understanding the Behaviour of the Empirical Cross-Entropy Beyond the Training Distribution
Machine learning theory has mostly focused on generalization to samples ...
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04/04/2019
On Direct Distribution Matching for Adapting Segmentation Networks
Minimization of distribution matching losses is a principled approach to...
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06/29/2018
Fundamental Limits of Distributed Data Shuffling
Data shuffling of training data among different computing nodes (workers...
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03/16/2018
On Combination Networks with Cache-aided Relays and Users
Caching is an efficient way to reduce peak hour network traffic congesti...
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02/28/2018
A Novel Asymmetric Coded Placement in Combination Networks with end-user Caches
The tradeoff between the user's memory size and the worst-case download ...
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02/28/2018
Caching in Combination Networks: A Novel Delivery by Leveraging the Network Topology
Maddah-Ali and Niesen (MAN) in 2014 surprisingly showed that it is possi...
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02/28/2018
On the Benefits of Asymmetric Coded Cache Placement in Combination Networks with End-User Caches
This paper investigates the fundamental tradeoff between cache size and ...
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02/26/2018
Learning Anonymized Representations with Adversarial Neural Networks
Statistical methods protecting sensitive information or the identity of ...
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02/14/2018
The Role of Information Complexity and Randomization in Representation Learning
A grand challenge in representation learning is to learn the different e...
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01/11/2018
Performance Evaluation of Advanced Relaying Protocols in Large Wireless Networks
This paper studies the performance of some state-of-the-art cooperative ...
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11/19/2017