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09/13/2023
Dynamic NeRFs for Soccer Scenes
The long-standing problem of novel view synthesis has many applications,...
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05/11/2023
Policy Gradient Algorithms Implicitly Optimize by Continuation
Direct policy optimization in reinforcement learning is usually solved w...
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04/18/2023
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping
Robotic grasping in highly noisy environments presents complex challenge...
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03/10/2023
Simulation-based Bayesian inference for robotic grasping
General robotic grippers are challenging to control because of their ric...
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12/07/2022
Adaptive Self-Training for Object Detection
Deep learning has emerged as an effective solution for solving the task ...
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02/08/2022
Robust Hybrid Learning With Expert Augmentation
Hybrid modelling reduces the misspecification of expert models by combin...
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12/30/2021
SAE: Sequential Anchored Ensembles
Computing the Bayesian posterior of a neural network is a challenging ta...
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11/03/2021
From global to local MDI variable importances for random forests and when they are Shapley values
Random forests have been widely used for their ability to provide so-cal...
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10/01/2021
Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference
In many areas of science, complex phenomena are modeled by stochastic pa...
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09/29/2021
Simulation-based Bayesian inference for multi-fingered robotic grasping
Multi-fingered robotic grasping is an undeniable stepping stone to unive...
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07/02/2021
Truncated Marginal Neural Ratio Estimation
Parametric stochastic simulators are ubiquitous in science, often featur...
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06/29/2021
Diffusion Priors In Variational Autoencoders
Among likelihood-based approaches for deep generative modelling, variati...
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06/06/2021
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
The distributional reinforcement learning (RL) approach advocates for re...
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02/12/2021
Leveraging Global Parameters for Flow-based Neural Posterior Estimation
Inferring the parameters of a stochastic model based on experimental obs...
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12/22/2020
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning
This paper introduces four new algorithms that can be used for tackling ...
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11/30/2020
Towards constraining warm dark matter with stellar streams through neural simulation-based inference
A statistical analysis of the observed perturbations in the density of s...
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11/27/2020
Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
We present algorithms (a) for nested neural likelihood-to-evidence ratio...
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11/11/2020
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
We revisit empirical Bayes in the absence of a tractable likelihood func...
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10/24/2020
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
Gravitational waves from compact binaries measured by the LIGO and Virgo...
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06/03/2020
Graphical Normalizing Flows
Normalizing flows model complex probability distributions by combining a...
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06/01/2020
You say Normalizing Flows I see Bayesian Networks
Normalizing flows have emerged as an important family of deep neural net...
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11/04/2019
The frontier of simulation-based inference
Many domains of science have developed complex simulations to describe p...
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09/04/2019
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
The subtle and unique imprint of dark matter substructure on extended ar...
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09/01/2019
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
This paper makes one step forward towards characterizing a new family of...
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08/14/2019
Unconstrained Monotonic Neural Networks
Monotonic neural networks have recently been proposed as a way to define...
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07/08/2019
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread atte...
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06/04/2019
Effective LHC measurements with matrix elements and machine learning
One major challenge for the legacy measurements at the LHC is that the l...
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03/10/2019
Likelihood-free MCMC with Approximate Likelihood Ratios
We propose a novel approach for posterior sampling with intractable like...
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11/30/2018
Recurrent machines for likelihood-free inference
Likelihood-free inference is concerned with the estimation of the parame...
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09/30/2018
Deep Quality-Value (DQV) Learning
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called ...
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08/02/2018
Likelihood-free inference with an improved cross-entropy estimator
We extend recent work (Brehmer, et. al., 2018) that use neural networks ...
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07/20/2018
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel framework that enables efficient probabilistic infere...
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07/08/2018
Machine Learning in High Energy Physics Community White Paper
Machine learning is an important research area in particle physics, begi...
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05/30/2018
Mining gold from implicit models to improve likelihood-free inference
Simulators often provide the best description of real-world phenomena; h...
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05/22/2018
Gradient Energy Matching for Distributed Asynchronous Gradient Descent
Distributed asynchronous SGD has become widely used for deep learning in...
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04/30/2018
A Guide to Constraining Effective Field Theories with Machine Learning
We develop, discuss, and compare several inference techniques to constra...
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04/30/2018
Constraining Effective Field Theories with Machine Learning
We present powerful new analysis techniques to constrain effective field...
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12/21/2017
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
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09/04/2017
Random Subspace with Trees for Feature Selection Under Memory Constraints
Dealing with datasets of very high dimension is a major challenge in mac...
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07/22/2017
Adversarial Variational Optimization of Non-Differentiable Simulators
Complex computer simulators are increasingly used across fields of scien...
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02/02/2017
QCD-Aware Recursive Neural Networks for Jet Physics
Recent progress in applying machine learning for jet physics has been bu...
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11/03/2016
Learning to Pivot with Adversarial Networks
Several techniques for domain adaptation have been proposed to account f...
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05/20/2016
Visualization of Publication Impact
Measuring scholarly impact has been a topic of much interest in recent y...
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05/12/2016
Context-dependent feature analysis with random forests
In many cases, feature selection is often more complicated than identify...
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08/31/2015
Ethnicity sensitive author disambiguation using semi-supervised learning
Author name disambiguation in bibliographic databases is the problem of ...
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06/06/2015
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
In many fields of science, generalized likelihood ratio tests are establ...
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07/28/2014
Understanding Random Forests: From Theory to Practice
Data analysis and machine learning have become an integrative part of th...
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06/30/2014
