A method for training neural networks to sample from target distributions.
problem Training stochastic neural networks to draw samples from complex target distributions.
method Iteratively adjusting neural network parameters using a Stein variational gradient to minimize KL divergence.
result Our method trains neural samplers to approximate likelihood functions effectively, producing realistic images.
New algorithms improve learning deep energy models.
problem Learning deep energy models efficiently and accurately.
method Proposed new algorithms combining GAN-style methods with traditional energy-based learning.
result SteinCD performs well in test likelihood, SteinGAN in generating realistic images.
EB-GFN models discrete data with amortized MCMC sampling.
problem Probabilistic modeling of high-dimensional discrete data.
method EB-GFN combines GFlowNets with energy-based models for efficient sampling.
result EB-GFN effectively models various discrete data tasks.
New method optimizes collaborative filtering for better ranking metrics.
problem Improving recommendation quality metrics like top-N ranking.
method Actor-critic reinforcement learning to directly optimize ranking metrics.
result The method outperforms state-of-the-art baselines on real-world datasets.
MLE and CVE are equivalent under exponential families, leading to faster and more stable EM algorithms.
problem Finding maximum likelihood estimators (MLE) efficiently and stably.
method Proved equivalence between MLE and CVE under exponential families, leading to an EM algorithm.
result EM algorithm achieves the same asymptotic variance as MLE and is faster and more stable.
We describe k-MLE, a fast and efficient local search algorithm for learning finite statistical mixtures of exponential families such as Gaussian mixture models. Mixture models are traditionally learned using the expectation-maximization (EM) soft clustering technique that monotonically increases the incomplete (expec…
Regularized MLE improves MoE models for high-dimensional data.
problem Modeling with high-dimensional predictors and feature selection.
method Gaussian gating network, ℓ1-regularized MLE, EM-Lasso algorithm, BIC-like criterion. result Regularized MLE outperforms standard MLE in clustering and regression tasks.
The paper analyzes RLHF with human feedback and provides convergence results for MLE and pessimistic MLE.
problem Improving RLHF with human feedback from pairwise or K-wise comparisons. method Theoretical framework for RLHF with convergence analysis of MLE and pessimistic MLE.
result MLE fails but pessimistic MLE provides improved policies under certain coverage assumptions.
The paper shows a phase transition for the existence of MLE in high-dimensional logistic regression.
problem The existence of the maximum likelihood estimate in high-dimensional logistic regression models.
method Established a phase transition boundary curve hextMLE parameterized by scalars measuring the magnitude of regression coefficients. result The existence of the MLE in high-dimensional logistic regression models undergoes a sharp phase transition.
Improved MLE for Hawkes Processes stabilizes unstable optimization.
problem Unstable Maximum Likelihood Estimation (MLE) for Hawkes Processes.
method Simple stabilization procedure to improve MLE without restrictive assumptions.
result Stabilized MLE outperforms traditional methods over various sequence lengths.
AMCI improves Monte Carlo integration by amortizing over both datasets and target functions.
problem Inefficiency in approximating expectations for known target functions using current approaches.
method Introduces AMCI, a method for amortizing Monte Carlo integration directly, producing three distinct amortized proposals.
result AMCI can theoretically produce arbitrarily small errors for any integrable target function using only a single sample from each proposal at runtime.
New anomaly estimator reduces bias in MLE for normally distributed data.
problem Bias in Maximum Likelihood Estimation of structured anomalies.
method Derive a new anomaly estimator using a mixture model.
result New estimator is asymptotically unbiased regardless of anomaly family size.
Paper establishes MLE consistency for market microstructure models.
problem Estimating parameters in partially observed diffusion models.
method Tractable sufficient condition for MLE consistency based on stationary distribution.
result Maximum likelihood estimators are consistent for market microstructure parameters.
The Chirikov standard map and the 2D Froeschlé map are investigated. A few thousand values of the Hurst exponent (HE) and the maximal Lyapunov exponent (mLE) are plotted in a mixed space of the nonlinear parameter versus the initial condition. Both characteristic exponents reveal remarkably similar structures in this s…
This paper reviews recent advancements in amortized Variational Inference.
problem Scalability and efficiency issues in traditional Variational Inference.
method Systematic review of various Variational Inference techniques, focusing on amortized approaches.
result Amortized Variational Inference improves scalability and efficiency for generative modeling tasks.
Improves point-cloud reconstruction by optimizing projections with self-attention.
problem Inefficient and non-metric projection methods for sliced Wasserstein distances.
method Proposes distributional sliced Wasserstein distance with self-attention for permutation-invariant and metric optimization.
result Self-attention amortized distributional projection optimization achieves better performance in point-cloud reconstruction.
AIR improves VAE generalization by controlling inference smoothness.
problem Overly expressive inference models harm VAE performance.
method Amortized inference regularization (AIR) controls inference smoothness.
result AIR improves VAE generalization on inference and generative tasks.
New option type preserves fungibility by amortizing payments over time.
problem Traditional installment options destroy fungibility and lapse when payments stop.
method Introduces amortizing perpetual options (AmPOs) with an implicit payment scheme.
result Valuation of AmPOs reduces to vanilla perpetual American options.
A new method optimizes projection directions for sliced Wasserstein distances.
problem Finding informative projecting directions for sliced Wasserstein distances is computationally expensive.
method Amortized projection optimization to predict directions efficiently.
result Proposed amortized models improve generative modeling performance.
New estimators improve Rasch model item parameter estimation for sparse data.
problem Estimating item parameters in sparse Rasch model data.
method Random pairing maximum likelihood estimator (RP-MLE) and its bootstrapped variant (MRP-MLE).
result RP-MLE and MRP-MLE are minimax optimal and provide precise item parameter estimates.
We have observed an interesting, yet unexplained, phenomenon: Semidefinite programming (SDP) based relaxations of maximum likelihood estimators (MLE) tend to be tight in recovery problems with noisy data, even when MLE cannot exactly recover the ground truth. Several results establish tightness of SDP based relaxations…
Adaptive workflow combines fast amortized inference with MCMC for many datasets.
problem Trade-off between computational speed and sampling accuracy in Bayesian inference.
method Adaptive workflow integrating amortized inference and MCMC with principled diagnostics.
result Efficiency gains with high posterior quality on tens of thousands of datasets.
Study on MLE growth rate for stable CIR process, proving consistency and normality.
problem Estimating the growth rate of a stable CIR process from continuous observations.
method Maximum likelihood estimation for a specific type of process.
result Strong consistency and asymptotic normality in subcritical and supercritical cases, asymptotic mixed normality in supercritical, open in critical case.
ASPIRE improves amortized posterior inference for Bayesian inverse problems.
problem Bayesian inverse problems are computationally challenging due to uncertainty quantification.
method Iterative refinement of amortized posteriors using physics-based and summary statistics.
result ASPIRE achieves better posterior approximations with minimal extra computations.
Operational risk models commonly employ maximum likelihood estimation (MLE) to fit loss data to heavy-tailed distributions. Yet several desirable properties of MLE (e.g. asymptotic normality) are generally valid only for large sample-sizes, a situation rarely encountered in operational risk. In this paper, we study how…
This paper explores the preference-based top-K rank aggregation problem. Suppose that a collection of items is repeatedly compared in pairs, and one wishes to recover a consistent ordering that emphasizes the top-K ranked items, based on partially revealed preferences. We focus on the Bradley-Terry-Luce (BTL) model…
Advocates for MLE in regression and forecasting for better inductive biases and post-hoc optimization.
problem Designing effective loss functions for regression and forecasting.
method Maximum Likelihood Estimation (MLE) approach for regression and forecasting.
result MLE approach outperforms direct empirical risk minimization under certain conditions and for various datasets.
This letter proposes a low-computational Bayesian algorithm for noisy sparse recovery in the context of one bit compressed sensing with sensing matrix perturbation. The proposed algorithm which is called BHT-MLE comprises a sparse support detector and an amplitude estimator. The support detector utilizes Bayesian hypot…
This paper examines how neural architectures support amortized Bayesian inference and its performance under varying conditions.
problem Understanding and evaluating amortized inference under signal-to-noise variation and distribution shift.
method Statistical analysis of neural architectures including feedforward networks, Deep Sets, and Transformers.
result Neural architectures support amortized Bayesian inference, offering controlled generalization error and robustness under varying conditions.
Improved fairness in pairwise comparisons using MLE with a simple modification.
problem Fairness in pairwise comparisons using MLE is suboptimal.
method Proposed a simple modification to MLE to improve fairness (bias) without sacrificing accuracy.
result Improved rate in bias while maintaining minimax-optimality in mean squared error.
Develops new Markov processes with switching rates and past dependence.
problem Modeling processes with dynamic switching rates and path dependence.
method Introduces a new class of Markov jump processes with regime switching and path dependence. Derives distributional properties and maximum likelihood estimates.
result Maximum likelihood estimates of the process parameters are derived in closed form and have asymptotic normality.
Proposes MLEs for MMJDM with EM-algorithm.
problem Estimating stock prices with varying drift and volatility.
method EM-algorithm for MLEs of MMJDM.
result Validated with simulated data and fitted to Amazon and Netflix stock prices.
DMLE improves active learning by correcting MLE for sample dependencies.
problem Dependencies among samples in active learning affect model parameter estimation.
method Dependency-aware Maximum Likelihood Estimation (DMLE).
result DMLE achieves superior performance across multiple benchmark datasets.
Adversarial dynamics embedding improves MLE of exponential family models.
problem Maximum likelihood estimation of exponential family models with neural network parametrization.
method Adversarial dynamics embedding to estimate the dual sampler and primal model simultaneously.
result Adversarial dynamics embedding leads to more effective learning and improved estimators compared to existing methods.
JANA trains networks to approximate Bayesian models efficiently.
problem Intractable likelihood functions and posterior densities in Bayesian models.
method End-to-end training of three networks: summary, posterior, and likelihood networks.
result JANA provides accurate amortized marginal likelihood and posterior predictive estimation.
MLE works best for covariate shift without modifications.
problem OOD generalization under covariate shift.
method Maximum Likelihood Estimation (MLE) without modifications.
result MLE achieves minimax optimality for covariate shift under well-specified setting.
Improved diffusion sampling for inverse problems with faster and more robust inference.
problem High computational cost and lack of robustness in diffusion posterior sampling.
method Amortized variational inference with explicit likelihood guidance.
result Improved trade-off between inference speed and robustness to unseen degradations.
Proposes ACP for efficient inference in noisy-or models.
problem Efficient inference in noisy-or models.
method Hybrid approach combining classical and modern variational inference.
result ACP outperforms or matches other approaches in noisy-or models.
Efficiently predicts optimal transport plans using sliced potentials.
problem Predicting optimal transport plans across multiple measure pairs efficiently.
method Regression-based and objective-based amortization strategies using sliced optimal transport potentials.
result Efficient and accurate prediction of optimal transport plans for various tasks.
Paper explores Elliptical Wishart distributions in signal processing and machine learning.
problem Estimating parameters of Elliptical Wishart distributions.
method Proposes fixed point and Riemannian optimization algorithms for maximum likelihood estimation.
result Characterizes existence, uniqueness, and convergence of the MLE.
BayesFlow trains neural networks for fast Bayesian inference.
problem Fast Bayesian inference for complex models.
method Amortized neural networks for intractable posterior distributions.
result Fast inference through pre-trained neural networks.
Simformer uses transformer models to perform flexible Bayesian inference.
problem Current simulation-based inference methods are inflexible and require fixed priors.
method Trains a probabilistic diffusion model with transformer architectures.
result Outperforms state-of-the-art methods on various benchmarks.
Improved convergence rates for MLE in mixture models using penalized log-likelihood.
problem Convergence rates for MLE in finite mixture models.
method Penalizing log-likelihood to discourage vanishing mixing weights, using Wasserstein distance and new loss functions.
result Improved convergence rates for some mixture components, faster than traditional methods.
New method improves generalization of VAEs by reducing overfitting.
problem Generalization issues in VAEs overfitting to training data.
method Proposed a new training objective to improve amortized inference.
result Improved performance in image modeling and lossless compression.
Adversarial robustness of amortized Bayesian inference is studied, showing it can be improved.
problem Adversarial robustness of amortized Bayesian inference.
method Simulation-based estimation, regularization scheme based on Fisher information.
result Adversarial robustness can be improved with a regularization scheme.
New method reduces infinite variance in probabilistic programs with rejection sampling.
problem Infinite variance in naive importance sampling for programs with rejection sampling.
method Developed a new amortized importance sampling estimator with finite variance proof.
result Empirically demonstrated efficiency and correctness compared to existing alternatives.
Unified detector calibration and simulation using MLE from generative models.
problem Combining detector calibration and simulation using traditional methods.
method Maximum likelihood estimation from conditional generative models.
result Prior-independent and non-Gaussian resolutions possible.
AQL uses amortized inference to handle high-dimensional action spaces in Q-learning.
problem Difficulty in maximizing over large action spaces in Q-learning.
method Replace expensive maximization over all actions with a maximization over a small subset sampled from a learned proposal distribution.
result AQL outperforms existing methods on continuous control tasks with up to 21 dimensional actions.