Papers

Field

Top tools here

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 papers15 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 14
Time-partitioning Gaussian Process Mixture Model
Guilherme Piantino, Hedibert F. Lopes
Nonstationarity and heterogeneous dynamics are common in financial and macroeconomic time series. Volatility alternates between calm and turbulent regimes, interest rates follow monetary-policy cycles, and major events trigger persistent structural shifts. We propose the Time-partitioning Gaussian Process Mixture Model (TPGPMM), a Bayesian nonparametric framework for regime identification and nonstationary prediction that infers such regimes and their within-regime dependence from data, without Markovian assumptions. It combines (i) an adaptive Pitman-Yor process prior on ordered compositions, mapped to block lengths with contiguity enforced by construction, and (ii) regime-specific Gaussian processes for temporal dependence, yielding a nonstationary predictive distribution. By inferring its discount parameter, the prior adapts to homogeneous and highly heterogeneous regime lengths. Unlike sequential or Markov-switching methods, it flags persistent structural shifts as change points rather than reacting to transient shocks. A split-merge Metropolis-within-Gibbs sampler with exact Gibbs updates of regime boundaries jointly learns change points and hyperparameters in batch,…
PDF arXiv
arXiv cs.LGOct 14
Sliced Wasserstein Sketches with Error Certificates
Elaheh Akbari, James Liu, Keaton Hamm, Soheil Kolouri
Compact representations that support geometrically meaningful comparisons are valuable for learning from probability distributions. We study optimal, free-weight $M$-atom quantization of one-dimensional probability measures in quadratic Wasserstein distance and extend it to higher dimensions via slicing. Using $L$ shared projections yields a sketch with $O(LM)$ comparison time. Our main result is an exact decomposition of squared distance error into residual and partition-mismatch terms, yielding computable certificates for arbitrary centroid sketches. For optimal sketches, a finite $(2+δ)$ moment for some $δ>0$ guarantees $O(M^{-1})$ error in the finite-direction sliced distance, while equal-mass sketches can converge more slowly. Each slice preserves its mean, while storing residual energies enables covariance recovery from sufficiently many generic directions. Experiments on document, point-cloud, and flow-cytometry benchmarks show that optimal sketches substantially reduce storage while preserving the classification performance of uncompressed sliced distances. They also improve generalization from few labelled subjects and support end-to-end learning as compact pooling layers.
PDF arXiv
arXiv cs.LGOct 14
Selection-Safe Conformal Early Exit: Distribution-Free Coverage under Adaptive Depth
Adria Binte Habib, Ibne Farabi Shihab
Calibrating a conformal prediction set at each early exit does not preserve coverage once a data-dependent exit is selected. We construct one terminal path score from prefix maxima, calibrate a single split-conformal quantile, and return nested prefix sets that all contain the valid terminal set. The guarantee is selection-safe without an alpha/L correction and holds simultaneously for every post-calibration label-free stopping policy that preserves terminal-set containment. We also bound the efficiency cost of the path maximum by the empirical tail of the early-over-terminal score excess. Across eight encoder task-backbone cells, pathwise selection attains macro coverage 0.901 versus 0.814 for naive layerwise selection, with average set size 1.41 and latency fraction 0.71. On CIFAR-100 with a 24-exit ViT-L/16 (n_cal=500), first-eligible stopping at budget b=4 attains coverage 0.902, set size 2.76, and latency fraction 0.57, and the same calibration supports deployment-time budget changes without refitting. In a joint exit-count/calibration-size sweep at n=100, pathwise set size moves only from 3.02 to 3.11 as exits increase from 4 to 24, while Bonferroni degenerates to the full…
PDF arXiv
arXiv hep-latOct 14
Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows
Claudio Bonanno, Andrea Bulgarelli, Elia Cellini, Alessandro Nada, Dario Panfalone +2
In this work we provide numerical evidence that topological freezing in $\mathrm{SU}(3)$ Yang-Mills theory can be mitigated with a flow-based approach, as tested at lattice spacings as fine as 0.03~fm. We do so by analyzing the scaling of the costs of a computational strategy based on the combination of open boundary conditions and Stochastic Normalizing Flows. We show that the integrated autocorrelation time of the squared topological charge and the statistical uncertainty on the topological susceptibility remain under control throughout the explored parameter range with currently available computational resources. This marks an important step toward the use of even more efficient flow-based architectures for ergodic sampling of topology in state-of-the-art lattice gauge theory calculations.
PDF arXiv
arXiv cs.LGOct 14
Vertical Sliced Wasserstein Distances for Meta-Measures
Elaheh Akbari, Ahmadreza Moradipari, Kyungtae Han, Hamed Pirsiavash, Yikun Bai +2
Many modern learning problems require comparing probability distributions whose individual observations are themselves probability measures, naturally giving rise to meta-measures in $\mathcal{P}(\mathcal{P}(Ω))$. However, optimal transport distances on this space can be prohibitively expensive to evaluate. We introduce the vertical sliced Wasserstein ($\mathrm{VSW}$) distance, a scalable metric on $\mathcal{P}(\mathcal{P}(Ω))$. Rather than slicing along the transport geometry of $\mathcal{P}(Ω)$, $\mathrm{VSW}$ projects each inner measure using continuous test functions and compares the resulting one-dimensional distributions with one-dimensional Wasserstein distance. We prove that $\mathrm{VSW}$ is a valid metric on $\mathcal{P}(\mathcal{P}(Ω))$, metrizes weak convergence, and admits dimension-independent statistical convergence guarantees. Empirically, $\mathrm{VSW}$ provides high accuracy and efficiency compared with existing distances for meta-measures. We further derive a practical particle gradient for the empirical $\mathrm{VSW}_2^2$ objective and demonstrate its use for gradient-based transport of meta-measures. $\mathrm{VSW}$ provides a simple and scalable alternative…
PDF arXiv
arXiv stat.MLOct 14
Mixed Poisson diffusion models and application to posterior sampling
Thibaut Pellerin, Alain Durmus, Elinor Berger, Eric Barat
Diffusion models are a powerful framework for learning complex probability distributions. While widely used diffusion models for continuous data rely on Gaussian noising processes, we instead develop diffusion models based on Poisson noise. More precisely, we introduce and study a new mathematical framework, called Mixed Poisson Diffusion Models (MPDM), with noising dynamics driven by multivariate counting processes. Our methodology is motivated by Poisson inverse problems. The success of Gaussian diffusion models has led to their use as prior models in a growing number of posterior sampling methods for inverse problems. However, most of these approaches focus on Gaussian inverse problems, in which observations are generated through a deterministic forward operator corrupted by additive Gaussian noise. By contrast, Poisson inverse problems, where observations are discrete and Poisson distributed, remain comparatively underexplored in this literature. Building on our Poisson generative dynamics, we develop a natural framework for posterior sampling in Poisson inverse problems and derive several posterior sampling algorithms based on the proposed model. We evaluate our approach on…
PDF arXiv

Papers

Topics in this field

supervised · labeled · classification163 papers15 this week
amortized bayesian · bayesian networks · variational inference108 papers15 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

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

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 18m ago

Supervised & diffusion models

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

68papers this week−49.6% vs last week

All papers · 748 With code · 2

Newest Most relevant

617 papers this month

Recent papers

All papers in Research

arXiv stat.MLOct 14
Time-partitioning Gaussian Process Mixture Model
Guilherme Piantino, Hedibert F. Lopes
Nonstationarity and heterogeneous dynamics are common in financial and macroeconomic time series. Volatility alternates between calm and turbulent regimes, interest rates follow monetary-policy cycles, and major events trigger persistent structural shifts. We propose the Time-partitioning Gaussian Process Mixture Model (TPGPMM), a Bayesian nonparametric framework for regime identification and nonstationary prediction that infers such regimes and their within-regime dependence from data, without Markovian assumptions. It combines (i) an adaptive Pitman-Yor process prior on ordered compositions, mapped to block lengths with contiguity enforced by construction, and (ii) regime-specific Gaussian processes for temporal dependence, yielding a nonstationary predictive distribution. By inferring its discount parameter, the prior adapts to homogeneous and highly heterogeneous regime lengths. Unlike sequential or Markov-switching methods, it flags persistent structural shifts as change points rather than reacting to transient shocks. A split-merge Metropolis-within-Gibbs sampler with exact Gibbs updates of regime boundaries jointly learns change points and hyperparameters in batch,…
PDF arXiv
arXiv cs.LGOct 14
Sliced Wasserstein Sketches with Error Certificates
Elaheh Akbari, James Liu, Keaton Hamm, Soheil Kolouri
Compact representations that support geometrically meaningful comparisons are valuable for learning from probability distributions. We study optimal, free-weight $M$-atom quantization of one-dimensional probability measures in quadratic Wasserstein distance and extend it to higher dimensions via slicing. Using $L$ shared projections yields a sketch with $O(LM)$ comparison time. Our main result is an exact decomposition of squared distance error into residual and partition-mismatch terms, yielding computable certificates for arbitrary centroid sketches. For optimal sketches, a finite $(2+δ)$ moment for some $δ>0$ guarantees $O(M^{-1})$ error in the finite-direction sliced distance, while equal-mass sketches can converge more slowly. Each slice preserves its mean, while storing residual energies enables covariance recovery from sufficiently many generic directions. Experiments on document, point-cloud, and flow-cytometry benchmarks show that optimal sketches substantially reduce storage while preserving the classification performance of uncompressed sliced distances. They also improve generalization from few labelled subjects and support end-to-end learning as compact pooling layers.
PDF arXiv
arXiv cs.LGOct 14
Selection-Safe Conformal Early Exit: Distribution-Free Coverage under Adaptive Depth
Adria Binte Habib, Ibne Farabi Shihab
Calibrating a conformal prediction set at each early exit does not preserve coverage once a data-dependent exit is selected. We construct one terminal path score from prefix maxima, calibrate a single split-conformal quantile, and return nested prefix sets that all contain the valid terminal set. The guarantee is selection-safe without an alpha/L correction and holds simultaneously for every post-calibration label-free stopping policy that preserves terminal-set containment. We also bound the efficiency cost of the path maximum by the empirical tail of the early-over-terminal score excess. Across eight encoder task-backbone cells, pathwise selection attains macro coverage 0.901 versus 0.814 for naive layerwise selection, with average set size 1.41 and latency fraction 0.71. On CIFAR-100 with a 24-exit ViT-L/16 (n_cal=500), first-eligible stopping at budget b=4 attains coverage 0.902, set size 2.76, and latency fraction 0.57, and the same calibration supports deployment-time budget changes without refitting. In a joint exit-count/calibration-size sweep at n=100, pathwise set size moves only from 3.02 to 3.11 as exits increase from 4 to 24, while Bonferroni degenerates to the full…
PDF arXiv
arXiv hep-latOct 14
Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows
Claudio Bonanno, Andrea Bulgarelli, Elia Cellini, Alessandro Nada, Dario Panfalone +2
In this work we provide numerical evidence that topological freezing in $\mathrm{SU}(3)$ Yang-Mills theory can be mitigated with a flow-based approach, as tested at lattice spacings as fine as 0.03~fm. We do so by analyzing the scaling of the costs of a computational strategy based on the combination of open boundary conditions and Stochastic Normalizing Flows. We show that the integrated autocorrelation time of the squared topological charge and the statistical uncertainty on the topological susceptibility remain under control throughout the explored parameter range with currently available computational resources. This marks an important step toward the use of even more efficient flow-based architectures for ergodic sampling of topology in state-of-the-art lattice gauge theory calculations.
PDF arXiv
arXiv cs.LGOct 14
Vertical Sliced Wasserstein Distances for Meta-Measures
Elaheh Akbari, Ahmadreza Moradipari, Kyungtae Han, Hamed Pirsiavash, Yikun Bai +2
Many modern learning problems require comparing probability distributions whose individual observations are themselves probability measures, naturally giving rise to meta-measures in $\mathcal{P}(\mathcal{P}(Ω))$. However, optimal transport distances on this space can be prohibitively expensive to evaluate. We introduce the vertical sliced Wasserstein ($\mathrm{VSW}$) distance, a scalable metric on $\mathcal{P}(\mathcal{P}(Ω))$. Rather than slicing along the transport geometry of $\mathcal{P}(Ω)$, $\mathrm{VSW}$ projects each inner measure using continuous test functions and compares the resulting one-dimensional distributions with one-dimensional Wasserstein distance. We prove that $\mathrm{VSW}$ is a valid metric on $\mathcal{P}(\mathcal{P}(Ω))$, metrizes weak convergence, and admits dimension-independent statistical convergence guarantees. Empirically, $\mathrm{VSW}$ provides high accuracy and efficiency compared with existing distances for meta-measures. We further derive a practical particle gradient for the empirical $\mathrm{VSW}_2^2$ objective and demonstrate its use for gradient-based transport of meta-measures. $\mathrm{VSW}$ provides a simple and scalable alternative…
PDF arXiv
arXiv stat.MLOct 14
Mixed Poisson diffusion models and application to posterior sampling
Thibaut Pellerin, Alain Durmus, Elinor Berger, Eric Barat
Diffusion models are a powerful framework for learning complex probability distributions. While widely used diffusion models for continuous data rely on Gaussian noising processes, we instead develop diffusion models based on Poisson noise. More precisely, we introduce and study a new mathematical framework, called Mixed Poisson Diffusion Models (MPDM), with noising dynamics driven by multivariate counting processes. Our methodology is motivated by Poisson inverse problems. The success of Gaussian diffusion models has led to their use as prior models in a growing number of posterior sampling methods for inverse problems. However, most of these approaches focus on Gaussian inverse problems, in which observations are generated through a deterministic forward operator corrupted by additive Gaussian noise. By contrast, Poisson inverse problems, where observations are discrete and Poisson distributed, remain comparatively underexplored in this literature. Building on our Poisson generative dynamics, we develop a natural framework for posterior sampling in Poisson inverse problems and derive several posterior sampling algorithms based on the proposed model. We evaluate our approach on…
PDF arXiv