Publications related to Adaptive Joint Distribution Learning

Related publications (17)

lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators

Conditional cumulative distribution functions (CDFs), conditional probability density functions (PDFs), and derivatives thereof, are important parameters of interest in statistics, econometrics, and other data science disciplines. The package lpcde impleme ...

The Open Journal2025

How data structures affect generalization in Kernel Methods and Deep Learning

Umberto Maria Tomasini

Artificial Intelligence has revolutionized numerous fields, driving advancements in healthcare, finance, and autonomous systems. At the core of this revolution lies Deep Learning---algorithms that learn representations from data through computational layer ...

EPFL2025

Adaptive energy reference for machine-learning models of the electronic density of states

Michele Ceriotti, Federico Grasselli, Kevin Kazuki Huguenin-Dumittan, Sanggyu Chong, Wei Bin How

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

2025

Joint Compression of Multi-model Systems for Edge Devices

Mathieu Salzmann, João Luís Prado Vieira

Deploying deep learning models for computer vision (CV) often requires a careful trade-off between performance, efficiency, and cost. In the context of edge inference of machine learning systems, this trade-off translates into a challenge, as computing and ...

Springer Science and Business Media Deutschland GmbH2025

Low-Resource Speech Recognition and Understanding for Challenging Applications

Juan Pablo Zuluaga Gomez

Automatic speech recognition (ASR) and spoken language understanding (SLU) is the core component of current voice-powered AI assistants such as Siri and Alexa. It involves speech transcription with ASR and its comprehension with natural language understand ...

EPFL2024

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Martin Josifoski

Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks. While, in practice, the number of related tasks available is often small, most of the existing approaches assume ...

2023

Multi-agent Learning with Privacy Guarantees

Elsa Rizk

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

EPFL2023

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Negar Kiyavash, Ehsan Mokhtarian, Saber Salehkaleybar

We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skele ...

2023

Graph Representation Learning with Optimal Transport: Analysis and Applications

Effrosyni Simou

In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...

EPFL2022

Proof of the Contiguity Conjecture and Lognormal Limit for the Symmetric Perceptron

Emmanuel Abbé

YY We consider the symmetric binary perceptron model, a simple model of neural networks that has gathered significant attention in the statistical physics, information theory and probability theory communities, with recent connections made to the performan ...

IEEE COMPUTER SOC2022

Capturing trade-offs between daily scheduling choices

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The funda ...

ELSEVIER SCI LTD2022

Automatic pathological speech assessment

Parvaneh Janbakhshi

Many pathologies cause impairments in the speech production mechanism resulting in reduced speech intelligibility and communicative ability. To assist the clinical diagnosis, treatment and management of speech disorders, automatic pathological speech asses ...

EPFL2022

Practical issues with modeling extreme Brazilian rainfall

Anthony Christopher Davison

Accurately quantifying extreme rainfall is important for the design of hydraulic structures, for flood mapping and zoning and for disaster management. In order to produce maps of estimates of 25-year rainfall return levels in Brazil, we selected 893 shorte ...

BRAZILIAN STATISTICAL ASSOCIATION2021

Sifting through the Noise: Universal First-Order Methods for Stochastic Variational Inequalities

Volkan Cevher, Ali Kavis, Kimon Antonakopoulos, Thomas Michaelsen Pethick

We examine a flexible algorithmic framework for solving monotone variational inequalities in the presence of randomness and uncertainty. The proposed template encompasses a wide range of popular first-order methods, including dual averaging, dual extrapola ...

2021

Collaborative Sampling in Generative Adversarial Networks

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

2020

Fast and Robust Distributed Learning in High Dimension

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault

Could a gradient aggregation rule (GAR) for distributed machine learning be both robust and fast? This paper answers by the affirmative through Multi-Bulyan. Given n workers, f of which are arbitrary malicious (Byzantine) and m = n − f are not, we prove th ...

2020

Special Issue: "Data Science versus Classical Inference: Prediction, Estimation, and Attribution", honouring Prof. Brad Efron's International Prize in Statistics in 2019 Discussion

Anthony Christopher Davison

WILEY2020