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Found 23 papers, 11 papers with code
SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations
The Latent Stochastic Differential Equation (SDE) is a powerful tool for time series and sequence modeling.
Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space
Hyperbolic space naturally encodes hierarchical structures such as phylogenies (binary trees), where inward-bending geodesics reflect paths through least common ancestors, and the exponential growth of neighborhoods mirrors the super-exponential scaling of topologies.
Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
Reconstructing jets, which provide vital insights into the properties and histories of subatomic particles produced in high-energy collisions, is a main problem in data analyses in collider physics.
Fast yet Safe: Early-Exiting with Risk Control
Scaling machine learning models significantly improves their performance.
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
Conventional diffusion models typically relies on a fixed forward process, which implicitly defines complex marginal distributions over latent variables.
Ranked #1 on
Image Generation
on ImageNet 32x32
(bpd metric)
VISA: Variational Inference with Sequential Sample-Average Approximations
We present variational inference with sequential sample-average approximation (VISA), a method for approximate inference in computationally intensive models, such as those based on numerical simulations.
Max-Rank: Efficient Multiple Testing for Conformal Prediction
Multiple hypothesis testing (MHT) frequently arises in scientific inquiries, and concurrent testing of multiple hypotheses inflates the risk of Type-I errors or false positives, rendering MHT corrections essential.
Neural Diffusion Models
In this paper, we present Neural Diffusion Models (NDMs), a generalization of conventional diffusion models that enables defining and learning time-dependent non-linear transformations of data.
Ranked #6 on
Image Generation
on ImageNet 64x64
(Bits per dim metric)
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation.
E-Valuating Classifier Two-Sample Tests
We introduce a powerful deep classifier two-sample test for high-dimensional data based on E-values, called E-value Classifier Two-Sample Test (E-C2ST).
A Variational Perspective on Generative Flow Networks
Generative flow networks (GFNs) are a class of models for sequential sampling of composite objects, which approximate a target distribution that is defined in terms of an energy function or a reward.
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport
Variational inference often minimizes the "reverse" Kullbeck-Leibler (KL) KL(q||p) from the approximate distribution q to the posterior p. Recent work studies the "forward" KL KL(p||q), which unlike reverse KL does not lead to variational approximations that underestimate uncertainty.
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference
Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial Sequential Monte Carlo (CSMC).
Markovian Score Climbing: Variational Inference with KL(p||q)
Modern variational inference (VI) uses stochastic gradients to avoid intractable expectations, enabling large-scale probabilistic inference in complex models.
Elements of Sequential Monte Carlo
A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations.
Variational Sequential Monte Carlo
The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior.
High-dimensional Filtering using Nested Sequential Monte Carlo
Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering.
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations.
Interacting Particle Markov Chain Monte Carlo
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers.
Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state.
Nested Sequential Monte Carlo Methods
NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm.
Divide-and-Conquer with Sequential Monte Carlo
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models.
Sequential Monte Carlo for Graphical Models
We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM).
