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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

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Topics in this field

supervised · labeled · classification163 papers24 this week
amortized bayesian · bayesian networks · variational inference107 papers17 this week
diffusion models · generative models · monte carlo155 papers11 this week
bayesian inverse · diffusion prior · diffusion posterior58 papers9 this week
bayesian optimization · bayesian optimization bo · black box optimization86 papers8 this week
conformal prediction · split conformal prediction · prediction intervals81 papers8 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 cs.AIOct 22
Semi-Linear Gaussian Bayesian Networks: Enhancing Flexibility in Node Dependencies via penalized B-Splines
Aitor Oviedo-Madrid, Mabel Morales-Otero, Jose Gonzalez-Gomariz, Ruben Armañanzas
Bayesian networks for continuous variables often rely on restrictive assumptions: linear Gaussian models lack flexibility to capture nonlinear dependencies, while non-parametric approaches provide greater flexibility at a high computational cost. This creates a gap for modeling scenarios where variables are approximately Gaussian but exhibit nonlinear relationships. We introduce Semi-Linear Gaussian Bayesian Networks (SLGBN), a novel class of continuous Bayesian networks designed to bridge this gap, preserving Gaussian conditional distributions while introducing nonlinear conditional mean functions. The proposed framework provides a unified probabilistic framework combining linear Gaussian and nonlinear Gaussian conditional models within the same Bayesian network Nonlinear conditional mean functions are estimated using generalized additive models (GAMs) with penalized B-splines. We present parameter estimation procedures for both types of nodes, as well as a greedy hill-climbing algorithm for learning the network structure under the SLGBN framework. To assess its performance, we compare SLGBN with other continuous Bayesian network models, including semiparametric Bayesian…
PDF arXiv
arXiv cs.LGOct 22
Learning Domain Trajectories with Flow Matching for Gradual Domain Adaptation
Yubo Huang, Yushe Cao, Zixi Wang, Jingzehua Xu, Xin Lai +2
Large distribution shifts remain a primary obstacle for domain generalization and unsupervised adaptation. Although gradual domain adaptation alleviates this difficulty by introducing ordered intermediate domains, most existing approaches still operate in a discrete, step-wise manner, which can accumulate errors and often relies on unstable Min--Max optimization. We propose \textbf{FlowGDA}, a flow-matching framework that models gradual adaptation as \emph{continuous} transport on a statistical manifold in a representation space. FlowGDA learns time-dependent velocity fields that induce smooth domain trajectories, using a simulation-free regression objective rather than adversarial training. To prevent semantic drift, we couple left-to-right transport between consecutive domains with a source-anchored transport and a semantic consistency loss, and we further stabilize learning via a two-stage sequential optimization scheme. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques, validating flow matching as an effective alternative for domain adaptation.
PDF arXiv
arXiv cs.CVOct 22
Residual Calibration via Local Feature-Space Refinement
Xihao Wang, Chengyao Yu, Ruixing Ming
Deep neural networks often struggle to provide reliable confidence estimates, making post-hoc calibration essential for reliable decision-making. Existing post-hoc methods largely rely on score-based transformations, which improve marginal calibration but often fail to capture spatially varying miscalibration in local regions of the learned feature space. To address this gap, we propose Local Adaptive Probability Estimation (LAPE), a post-hoc residual calibration framework. Specifically, LAPE treats the global calibrator output as a stable anchor and refines the individual prediction by leveraging non-parametric local evidence from neighboring calibration samples. As a plug-and-play refinement module, LAPE effectively utilizes the information about feature-space geometry and can reuse the calibration data that are used to construct the global calibrator. Furthermore, we extend LAPE to the setting with multi-view inputs through Cascade Feature Augmentation Fusion (CFAF). Extensive experiments demonstrate that the proposed methods can improve the calibration quality of various existing baselines and remain efficient and robust across different settings.
PDF arXiv
arXiv cs.ROOct 21
PhysibleMDM: Enforcing Inference-Time Physical Feasibility Constraints on Motion Diffusion Models via Noise Optimization
Maximilian Stölzle, David Müller, Agon Serifi, Ruben Grandia, Espen Knoop +1
Motion Diffusion Models (MDMs) can generate diverse and visually appealing human motion; yet, the resulting trajectories frequently violate physical constraints, limiting their downstream use in simulation and control. We present PhysibleMDM, a framework that promotes physical feasibility of MDM output by optimizing at inference time the diffusion noise without retraining the generative model. We introduce a set of surrogate feasibility constraints that can be evaluated without full trajectory simulation. These constraints combine kinematic checks (e.g., ground penetration, slip, and self-collision) with dynamic feasibility tests computed via an inverse-dynamics inner loop formulated as a cone program. We solve the resulting black-box noise optimization problem using the Separable Covariance Matrix Adaptation Evolution Strategy (Sep-CMA-ES), thereby avoiding reliance on computationally expensive and discontinuous gradients. An autoregressive scheme then chains optimized segments to synthesize long motion sequences. Across 100 motion styles on an SMPL humanoid, PhysibleMDM reduces mean ground penetration, self-collision penetration, and ground slip by 75%, 92%, and 74%,…
PDF arXiv
arXiv cs.LGOct 21
Towards Faithful Data Selection: Statistical Limits and Scalable Algorithms
Ahmed Mehdi Inane, Mahdi Haghifam, Gintare Karolina Dziugaite, Ioannis Mitliagkas
What does it mean to have an optimal data selection algorithm? Optimality is conventionally framed as finding a subset that minimizes the population error of the classifier trained only on that subset. Our starting point in this work is first to show that this goal is information-theoretically impossible unless the data selection algorithm has access to a validation set order-wise larger than the training set. This impossibility result motivates shifting from unverifiable population risk minimization to a one-sided guarantee that we refer to as faithfulness: for any candidate subset, we provide an estimable proxy that upper-bounds the difference between training on the full training data and on only the subset. We model training with the Unadjusted Langevin Algorithm and bound the Kullback--Leibler divergence between subset-trained and full-data-trained parameter distributions by the trajectory-accumulated gradient discrepancy. Minimizing this discrepancy gives a principled way to select a subset. Even though the resulting problem is NP-hard, we propose a swap-based local search that decreases the bound at every accepted step. It can therefore refine the output of any selector…
PDF arXiv
arXiv cs.LGOct 21
CTRL-FM: Inference-Time Structural Control for Flow-Based Virtual Sensing under Long-Horizon Node Outages
Boshra Khalili, Andrew Smyth
Long-horizon node outages are harder than conventional missing-value imputation because complete sensor trajectories remain unobserved for extended periods, while the usefulness of spatial and temporal dependencies varies across outage locations and operating regimes. Existing graph, diffusion, and flow-based methods typically impose a fixed structural bias, which can oversmooth abrupt changes or propagate errors from unreliable neighbors. We introduce CTRL-FM, an observation-preserving flow-matching framework that treats structural guidance as an outage-conditioned inference-time decision. A missing-only flow-matching backbone learns a conditional velocity field while keeping observed measurements exactly fixed. For each realized outage, CTRL-FM constructs self-supervised proxy outages from surviving measurements and uses their known values to select among uncontrolled, graph-dominant, temporal-dominant, and balanced control policies. The selected policy is locally modulated by graph and temporal reliability estimated exclusively from surviving observations, while boundary-anchored trends provide structural guidance. Control is applied only to missing entries and is rejected…
PDF arXiv

Papers

Topics in this field

supervised · labeled · classification163 papers24 this week
amortized bayesian · bayesian networks · variational inference107 papers17 this week
diffusion models · generative models · monte carlo155 papers11 this week
bayesian inverse · diffusion prior · diffusion posterior58 papers9 this week
bayesian optimization · bayesian optimization bo · black box optimization86 papers8 this week
conformal prediction · split conformal prediction · prediction intervals81 papers8 this week

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

Recent papers

All papers in Research

arXiv cs.AIOct 22
Semi-Linear Gaussian Bayesian Networks: Enhancing Flexibility in Node Dependencies via penalized B-Splines
Aitor Oviedo-Madrid, Mabel Morales-Otero, Jose Gonzalez-Gomariz, Ruben Armañanzas
Bayesian networks for continuous variables often rely on restrictive assumptions: linear Gaussian models lack flexibility to capture nonlinear dependencies, while non-parametric approaches provide greater flexibility at a high computational cost. This creates a gap for modeling scenarios where variables are approximately Gaussian but exhibit nonlinear relationships. We introduce Semi-Linear Gaussian Bayesian Networks (SLGBN), a novel class of continuous Bayesian networks designed to bridge this gap, preserving Gaussian conditional distributions while introducing nonlinear conditional mean functions. The proposed framework provides a unified probabilistic framework combining linear Gaussian and nonlinear Gaussian conditional models within the same Bayesian network Nonlinear conditional mean functions are estimated using generalized additive models (GAMs) with penalized B-splines. We present parameter estimation procedures for both types of nodes, as well as a greedy hill-climbing algorithm for learning the network structure under the SLGBN framework. To assess its performance, we compare SLGBN with other continuous Bayesian network models, including semiparametric Bayesian…
PDF arXiv
arXiv cs.LGOct 22
Learning Domain Trajectories with Flow Matching for Gradual Domain Adaptation
Yubo Huang, Yushe Cao, Zixi Wang, Jingzehua Xu, Xin Lai +2
Large distribution shifts remain a primary obstacle for domain generalization and unsupervised adaptation. Although gradual domain adaptation alleviates this difficulty by introducing ordered intermediate domains, most existing approaches still operate in a discrete, step-wise manner, which can accumulate errors and often relies on unstable Min--Max optimization. We propose \textbf{FlowGDA}, a flow-matching framework that models gradual adaptation as \emph{continuous} transport on a statistical manifold in a representation space. FlowGDA learns time-dependent velocity fields that induce smooth domain trajectories, using a simulation-free regression objective rather than adversarial training. To prevent semantic drift, we couple left-to-right transport between consecutive domains with a source-anchored transport and a semantic consistency loss, and we further stabilize learning via a two-stage sequential optimization scheme. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques, validating flow matching as an effective alternative for domain adaptation.
PDF arXiv
arXiv cs.CVOct 22
Residual Calibration via Local Feature-Space Refinement
Xihao Wang, Chengyao Yu, Ruixing Ming
Deep neural networks often struggle to provide reliable confidence estimates, making post-hoc calibration essential for reliable decision-making. Existing post-hoc methods largely rely on score-based transformations, which improve marginal calibration but often fail to capture spatially varying miscalibration in local regions of the learned feature space. To address this gap, we propose Local Adaptive Probability Estimation (LAPE), a post-hoc residual calibration framework. Specifically, LAPE treats the global calibrator output as a stable anchor and refines the individual prediction by leveraging non-parametric local evidence from neighboring calibration samples. As a plug-and-play refinement module, LAPE effectively utilizes the information about feature-space geometry and can reuse the calibration data that are used to construct the global calibrator. Furthermore, we extend LAPE to the setting with multi-view inputs through Cascade Feature Augmentation Fusion (CFAF). Extensive experiments demonstrate that the proposed methods can improve the calibration quality of various existing baselines and remain efficient and robust across different settings.
PDF arXiv
arXiv cs.ROOct 21
PhysibleMDM: Enforcing Inference-Time Physical Feasibility Constraints on Motion Diffusion Models via Noise Optimization
Maximilian Stölzle, David Müller, Agon Serifi, Ruben Grandia, Espen Knoop +1
Motion Diffusion Models (MDMs) can generate diverse and visually appealing human motion; yet, the resulting trajectories frequently violate physical constraints, limiting their downstream use in simulation and control. We present PhysibleMDM, a framework that promotes physical feasibility of MDM output by optimizing at inference time the diffusion noise without retraining the generative model. We introduce a set of surrogate feasibility constraints that can be evaluated without full trajectory simulation. These constraints combine kinematic checks (e.g., ground penetration, slip, and self-collision) with dynamic feasibility tests computed via an inverse-dynamics inner loop formulated as a cone program. We solve the resulting black-box noise optimization problem using the Separable Covariance Matrix Adaptation Evolution Strategy (Sep-CMA-ES), thereby avoiding reliance on computationally expensive and discontinuous gradients. An autoregressive scheme then chains optimized segments to synthesize long motion sequences. Across 100 motion styles on an SMPL humanoid, PhysibleMDM reduces mean ground penetration, self-collision penetration, and ground slip by 75%, 92%, and 74%,…
PDF arXiv
arXiv cs.LGOct 21
Towards Faithful Data Selection: Statistical Limits and Scalable Algorithms
Ahmed Mehdi Inane, Mahdi Haghifam, Gintare Karolina Dziugaite, Ioannis Mitliagkas
What does it mean to have an optimal data selection algorithm? Optimality is conventionally framed as finding a subset that minimizes the population error of the classifier trained only on that subset. Our starting point in this work is first to show that this goal is information-theoretically impossible unless the data selection algorithm has access to a validation set order-wise larger than the training set. This impossibility result motivates shifting from unverifiable population risk minimization to a one-sided guarantee that we refer to as faithfulness: for any candidate subset, we provide an estimable proxy that upper-bounds the difference between training on the full training data and on only the subset. We model training with the Unadjusted Langevin Algorithm and bound the Kullback--Leibler divergence between subset-trained and full-data-trained parameter distributions by the trajectory-accumulated gradient discrepancy. Minimizing this discrepancy gives a principled way to select a subset. Even though the resulting problem is NP-hard, we propose a swap-based local search that decreases the bound at every accepted step. It can therefore refine the output of any selector…
PDF arXiv
arXiv cs.LGOct 21
CTRL-FM: Inference-Time Structural Control for Flow-Based Virtual Sensing under Long-Horizon Node Outages
Boshra Khalili, Andrew Smyth
Long-horizon node outages are harder than conventional missing-value imputation because complete sensor trajectories remain unobserved for extended periods, while the usefulness of spatial and temporal dependencies varies across outage locations and operating regimes. Existing graph, diffusion, and flow-based methods typically impose a fixed structural bias, which can oversmooth abrupt changes or propagate errors from unreliable neighbors. We introduce CTRL-FM, an observation-preserving flow-matching framework that treats structural guidance as an outage-conditioned inference-time decision. A missing-only flow-matching backbone learns a conditional velocity field while keeping observed measurements exactly fixed. For each realized outage, CTRL-FM constructs self-supervised proxy outages from surviving measurements and uses their known values to select among uncontrolled, graph-dominant, temporal-dominant, and balanced control policies. The selected policy is locally modulated by graph and temporal reliability estimated exclusively from surviving observations, while boundary-anchored trends provide structural guidance. Control is applied only to missing entries and is rejected…
PDF arXiv

Activity · 12 weeks

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.

Fields · AI · updated 14m ago

Supervised & diffusion models

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

84papers this week−42.9% vs last week

All papers · 745 With code · 2

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