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Open problems in Supervised & diffusion models

Stable one-step generation in diffusion models1 paper raises it · 1 addresses it
Multi-step inference in diffusion models is slow, and one-step methods often collapse in performance due to unstable dynamics.
- Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Modelsraises · addresses
Evaluating model uncertainty with exact ground truth1 paper raises it · 1 addresses it
Real-world data lacks instance-level class probabilities, making it hard to assess model calibration and uncertainty.
- ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertaintyraises · addresses
Scalable optimal transport for flow matching1 paper raises it · 1 addresses it
Small minibatches limit the benefits of optimal transport in flow matching, and scaling is computationally expensive.
- Scaling Online Optimal Transport Minibatches for Flow Matchingraises · addresses
Prior-consistent sampling for ambiguous inverse problems1 paper raises it · 1 addresses it
Diffusion-based solvers for nonlinear inverse problems can produce prior-inconsistent solutions when multiple modes are measurement-consistent.
- Beyond Best-of-$k$: Prior-Consistent DAPS for Ambiguous Nonlinear Inverse Problemsraises · addresses
High-dimensional Bayesian optimization with limited budget1 paper raises it · 1 addresses it
Latent spaces in generative models can be extremely high-dimensional, making optimization with limited evaluations inefficient.
- SABLE: Scalable Affine Bayesian Latent-space Explorationraises · addresses

Named by an AI from these papers' abstracts.

Open problems

Recently funded

Topics in this field

supervised · labeled · classification163 papers15 this week
amortized bayesian · bayesian networks · variational inference108 papers14 this week
diffusion models · generative models · monte carlo155 papers9 this week
bayesian optimization · bayesian optimization bo · black box optimization87 papers9 this week
flow matching models · flow matching flow · matching flow matching95 papers7 this week
conformal prediction · split conformal prediction · prediction intervals82 papers7 this week

Topics in this field

Active contributors · 90 days

Henry MossSABLE: Scalable Affine Bayesian Latent-space Exploration5
Stefan T. RadevConditional Mean Flows: Few-Step Densities for Simulation-Based Inference5
Taiji SuzukiFinite-Sample Approximation of Hessian-Guided Perturbed Wasserstein Gradient Flows5
Xiaoyu WangAccelerating Non-Smooth and Heavy-Tailed Sampling4
Alexander KorotinDiscrete Action Matching: Learning Stochastic Dynamics from Samples via State Graphs4
Eric MoulinesOne-Step Generation via Riemannian Wasserstein Gradient Flows4

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv stat.MLOct 13
Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models
Allan Peter Engsig-Karup
Compartment models of infectious diseases are often linear in coefficients that are physical parameters or products or ratios of them. We exploit this with closed-form least squares and equip the estimates with distribution-free uncertainty. Physics-informed regression (PIR) fits the parameters to finite-difference derivatives of the data; we introduce physics-informed weak regression (PIWR), which integrates the model in time instead, needs no derivatives of the data, estimates the initial state jointly and is high-order accurate. To quantify uncertainty we wrap both estimators in split conformal prediction, with a calibration population generated by the physics-based model itself, giving simultaneous intervals for all parameters with a finite-sample coverage guarantee. A residual-adaptive factor, computed without ground truth, scales the intervals to each data set (PIWR-RACC). On the eleven benchmark models of disease-informed neural networks (DINNs), PIWR or PIR-FD6 is one to four orders of magnitude more accurate than the reported DINN results wherever the sampling resolves the dynamics, in about a millisecond per fit instead of minutes to hours of training, and the 90%…
PDF arXiv
arXiv cs.LGOct 13
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
Ethan Baron, Alan N. Amin, Andrew Gordon Wilson
Inference of phylogenetic trees from sequences plays a central role in understanding evolutionary relationships, with applications ranging from tracking pathogen spread to reconstructing the history of life. Conventionally, practitioners obtain posteriors over trees using likelihood-based methods like MCMC. However, likelihoods are only available for simple evolutionary models with restrictive assumptions. For realistic evolutionary models, inference using conventional methods is prohibitively expensive, inaccurate, or even impossible. In practice, practitioners settle for using as flexible a conventional method as possible, even though they expect it is misspecified. In principle, we could use simulation-based inference: simulate tree and sequence data from an evolutionary model and train a neural network to predict the posterior over trees given sequences. However it is a mathematical challenge to represent and train complex posterior distributions over trees. Here we present a flexible family of conditional generative models over trees. Our models iteratively build trees using three natural paradigms -- top-down, middle-out, or bottom-up -- and are trained using a discrete…
PDF arXiv
arXiv math.OCOct 13
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs
Justin Dumouchelle, Shunyu Yao, Beste Basciftci, Thiago Serra
Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability. Standard approaches either embed scenarios directly into optimization by considering sample average approximation (SAA)-based reformulations, growing prohibitively expensive and often failing to find feasible solutions; or employ conservative reformulations that guarantee feasibility but sacrifice objective quality by design. We propose ACCESS (Adaptive Chance-Constrained Exploration via Structured Sampling), a structured sampling framework that decouples optimization from feasibility evaluation. First, high-quality candidate solutions are generated by solving many inexpensive single-scenario subproblems, avoiding sampling directly in the constrained high-dimensional solution space. Second, feasibility is assessed separately via large-scale batched simulation over a massive number of scenarios, which is naturally amenable to GPU parallelism. That reduces the search from a high-dimensional solution space to a two-dimensional parameter space controlling conservatism and scenario diversity; while concentrating on candidates near the feasibility boundary, where…
PDF arXiv
arXiv hep-latOct 13
Data Free Training of Diffusion Models for Lattice Field Theories
Dennis Grinwald, Ankur Singha, Khaled Kahouli, Karl Jansen, Shinichi Nakajima
Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative samplers that can produce independent configurations. Diffusion models are promising for this purpose, but their standard denoising score matching objective requires samples from the target distribution, seemingly reintroducing the original sampling problem. Here, we show that diffusion models can instead be trained from their own generated samples using the target score identity, provided access to the unnormalized probability density. Importance-weighted formulations of this identity yield a consistent target throughout training, while unweighted variants define a self-consistent fixed-point training scheme. We benchmark the different approaches in two-dimensional $φ^4$ theory and identify effective choices that enable efficient training and sampling across the symmetric, critical, and broken phases. Ultimately, our data-free training approach achieves comparable performance to training on HMC reference data.
PDF arXiv
arXiv cs.LGOct 13▲ 7
Scaling Online Optimal Transport Minibatches for Flow Matching
Peter Chen, Sergey Levine
A central challenge in scaling flow models is maintaining stability in training and integration. In flow matching, this stability depends critically on how source samples are paired with data. Independent random pairing introduces substantial stochasticity, motivating minibatch optimal transport (OT) to construct online source-target couplings that reduce this randomness (Tong et al., 2024). However, as applications extend to larger and higher-dimensional datasets, small minibatches can limit these benefits, while scaling the coupling to substantially larger minibatches at every iteration is computationally prohibitive. Moreover, how OT coupling affects the full stochastic gradient and integration remains theoretically unresolved. In this work, we develop a scalable online entropic OT approximation that enables substantially larger source-target couplings while preserving the benefits of OT. We close the theoretical gap for both unregularized and entropic OT by proving strict reductions in full stochastic-gradient variance and integration error relative to independent pairing, and further characterize how these improvements strengthen as the online coupling minibatch grows.…
PDF arXiv
arXiv stat.MLOct 13
Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks
J. Arturo Esquivel, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo-García, Alán Aspuru-Guzik, Vianey Leos-Barajas
Simulation-based Bayesian inference for Chemical Reaction Networks (CRNs) must simultaneously solve two computationally demanding problems: discriminating between overlapping reaction pathways and estimating reaction rate constants. Classical approaches, such as Sequential Monte Carlo ABC, rely on handcrafted summary statistics and scale poorly across large parameter spaces. To overcome this, we introduce a neural inference architecture. Our pipeline utilizes shared 1D-CNNs and a permutation-invariant Set Attention mechanism to learn low-dimensional summary statistics from variable-length, multi-experiment time-series data. These statistics simultaneously condition a model selection network to compute posterior model probabilities and model-specific conditional Invertible Neural Networks (cINNs) that estimate parameter posterior distributions. We demonstrate the practical utility of our approach on a real protodeboronation example, illustrating how it handles structural ambiguity and extracts kinetic parameters from competing catalyst deactivation pathways.
PDF arXiv

Papers

Topics in this field

supervised · labeled · classification163 papers15 this week
amortized bayesian · bayesian networks · variational inference108 papers14 this week
diffusion models · generative models · monte carlo155 papers9 this week
bayesian optimization · bayesian optimization bo · black box optimization87 papers9 this week
flow matching models · flow matching flow · matching flow matching95 papers7 this week
conformal prediction · split conformal prediction · prediction intervals82 papers7 this week

Recent papers

All papers in Research

arXiv stat.MLOct 13
Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models
Allan Peter Engsig-Karup
Compartment models of infectious diseases are often linear in coefficients that are physical parameters or products or ratios of them. We exploit this with closed-form least squares and equip the estimates with distribution-free uncertainty. Physics-informed regression (PIR) fits the parameters to finite-difference derivatives of the data; we introduce physics-informed weak regression (PIWR), which integrates the model in time instead, needs no derivatives of the data, estimates the initial state jointly and is high-order accurate. To quantify uncertainty we wrap both estimators in split conformal prediction, with a calibration population generated by the physics-based model itself, giving simultaneous intervals for all parameters with a finite-sample coverage guarantee. A residual-adaptive factor, computed without ground truth, scales the intervals to each data set (PIWR-RACC). On the eleven benchmark models of disease-informed neural networks (DINNs), PIWR or PIR-FD6 is one to four orders of magnitude more accurate than the reported DINN results wherever the sampling resolves the dynamics, in about a millisecond per fit instead of minutes to hours of training, and the 90%…
PDF arXiv
arXiv cs.LGOct 13
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
Ethan Baron, Alan N. Amin, Andrew Gordon Wilson
Inference of phylogenetic trees from sequences plays a central role in understanding evolutionary relationships, with applications ranging from tracking pathogen spread to reconstructing the history of life. Conventionally, practitioners obtain posteriors over trees using likelihood-based methods like MCMC. However, likelihoods are only available for simple evolutionary models with restrictive assumptions. For realistic evolutionary models, inference using conventional methods is prohibitively expensive, inaccurate, or even impossible. In practice, practitioners settle for using as flexible a conventional method as possible, even though they expect it is misspecified. In principle, we could use simulation-based inference: simulate tree and sequence data from an evolutionary model and train a neural network to predict the posterior over trees given sequences. However it is a mathematical challenge to represent and train complex posterior distributions over trees. Here we present a flexible family of conditional generative models over trees. Our models iteratively build trees using three natural paradigms -- top-down, middle-out, or bottom-up -- and are trained using a discrete…
PDF arXiv
arXiv math.OCOct 13
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs
Justin Dumouchelle, Shunyu Yao, Beste Basciftci, Thiago Serra
Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability. Standard approaches either embed scenarios directly into optimization by considering sample average approximation (SAA)-based reformulations, growing prohibitively expensive and often failing to find feasible solutions; or employ conservative reformulations that guarantee feasibility but sacrifice objective quality by design. We propose ACCESS (Adaptive Chance-Constrained Exploration via Structured Sampling), a structured sampling framework that decouples optimization from feasibility evaluation. First, high-quality candidate solutions are generated by solving many inexpensive single-scenario subproblems, avoiding sampling directly in the constrained high-dimensional solution space. Second, feasibility is assessed separately via large-scale batched simulation over a massive number of scenarios, which is naturally amenable to GPU parallelism. That reduces the search from a high-dimensional solution space to a two-dimensional parameter space controlling conservatism and scenario diversity; while concentrating on candidates near the feasibility boundary, where…
PDF arXiv
arXiv hep-latOct 13
Data Free Training of Diffusion Models for Lattice Field Theories
Dennis Grinwald, Ankur Singha, Khaled Kahouli, Karl Jansen, Shinichi Nakajima
Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative samplers that can produce independent configurations. Diffusion models are promising for this purpose, but their standard denoising score matching objective requires samples from the target distribution, seemingly reintroducing the original sampling problem. Here, we show that diffusion models can instead be trained from their own generated samples using the target score identity, provided access to the unnormalized probability density. Importance-weighted formulations of this identity yield a consistent target throughout training, while unweighted variants define a self-consistent fixed-point training scheme. We benchmark the different approaches in two-dimensional $φ^4$ theory and identify effective choices that enable efficient training and sampling across the symmetric, critical, and broken phases. Ultimately, our data-free training approach achieves comparable performance to training on HMC reference data.
PDF arXiv
arXiv cs.LGOct 13▲ 7
Scaling Online Optimal Transport Minibatches for Flow Matching
Peter Chen, Sergey Levine
A central challenge in scaling flow models is maintaining stability in training and integration. In flow matching, this stability depends critically on how source samples are paired with data. Independent random pairing introduces substantial stochasticity, motivating minibatch optimal transport (OT) to construct online source-target couplings that reduce this randomness (Tong et al., 2024). However, as applications extend to larger and higher-dimensional datasets, small minibatches can limit these benefits, while scaling the coupling to substantially larger minibatches at every iteration is computationally prohibitive. Moreover, how OT coupling affects the full stochastic gradient and integration remains theoretically unresolved. In this work, we develop a scalable online entropic OT approximation that enables substantially larger source-target couplings while preserving the benefits of OT. We close the theoretical gap for both unregularized and entropic OT by proving strict reductions in full stochastic-gradient variance and integration error relative to independent pairing, and further characterize how these improvements strengthen as the online coupling minibatch grows.…
PDF arXiv
arXiv stat.MLOct 13
Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks
J. Arturo Esquivel, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo-García, Alán Aspuru-Guzik, Vianey Leos-Barajas
Simulation-based Bayesian inference for Chemical Reaction Networks (CRNs) must simultaneously solve two computationally demanding problems: discriminating between overlapping reaction pathways and estimating reaction rate constants. Classical approaches, such as Sequential Monte Carlo ABC, rely on handcrafted summary statistics and scale poorly across large parameter spaces. To overcome this, we introduce a neural inference architecture. Our pipeline utilizes shared 1D-CNNs and a permutation-invariant Set Attention mechanism to learn low-dimensional summary statistics from variable-length, multi-experiment time-series data. These statistics simultaneously condition a model selection network to compute posterior model probabilities and model-specific conditional Invertible Neural Networks (cINNs) that estimate parameter posterior distributions. We demonstrate the practical utility of our approach on a real protodeboronation example, illustrating how it handles structural ambiguity and extracts kinetic parameters from competing catalyst deactivation pathways.
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 1m ago

Supervised & diffusion models

Found by the topic model: supervised, diffusion models, amortized bayesian.

67papers this week−50.0% vs last week

All papers · 748 With code · 2

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612 papers this month

Open problems in Supervised & diffusion models

Stable one-step generation in diffusion models1 paper raises it · 1 addresses it
Multi-step inference in diffusion models is slow, and one-step methods often collapse in performance due to unstable dynamics.
- Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Modelsraises · addresses
Evaluating model uncertainty with exact ground truth1 paper raises it · 1 addresses it
Real-world data lacks instance-level class probabilities, making it hard to assess model calibration and uncertainty.
- ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertaintyraises · addresses
Scalable optimal transport for flow matching1 paper raises it · 1 addresses it
Small minibatches limit the benefits of optimal transport in flow matching, and scaling is computationally expensive.
- Scaling Online Optimal Transport Minibatches for Flow Matchingraises · addresses
Prior-consistent sampling for ambiguous inverse problems1 paper raises it · 1 addresses it
Diffusion-based solvers for nonlinear inverse problems can produce prior-inconsistent solutions when multiple modes are measurement-consistent.
- Beyond Best-of-$k$: Prior-Consistent DAPS for Ambiguous Nonlinear Inverse Problemsraises · addresses
High-dimensional Bayesian optimization with limited budget1 paper raises it · 1 addresses it
Latent spaces in generative models can be extremely high-dimensional, making optimization with limited evaluations inefficient.
- SABLE: Scalable Affine Bayesian Latent-space Explorationraises · addresses

Named by an AI from these papers' abstracts.

Active contributors · 90 days

Henry MossSABLE: Scalable Affine Bayesian Latent-space Exploration5
Stefan T. RadevConditional Mean Flows: Few-Step Densities for Simulation-Based Inference5
Taiji SuzukiFinite-Sample Approximation of Hessian-Guided Perturbed Wasserstein Gradient Flows5
Xiaoyu WangAccelerating Non-Smooth and Heavy-Tailed Sampling4
Alexander KorotinDiscrete Action Matching: Learning Stochastic Dynamics from Samples via State Graphs4
Eric MoulinesOne-Step Generation via Riemannian Wasserstein Gradient Flows4