Proposes SOVR loss to improve adversarial robustness by increasing logit margins.
problem Adversarial training's difficulty in robustness against sophisticated attacks.
method Introduces SOVR loss function that switches from cross-entropy to one-vs-the-rest loss for important samples.
result SOVR loss increases logit margins of important samples, improving robustness against Auto-Attack.
Efficiently estimates marginal likelihood using SGAIS.
problem Estimating marginal likelihood in i.i.d. data settings.
method Stochastic Gradient Annealed Importance Sampling (SGAIS).
result Significantly faster and more accurate estimates of marginal likelihood.
Kernel methods have revolutionized the fields of pattern recognition and machine learning. Their success, however, critically depends on the choice of kernel parameters. Using Gaussian process (GP) classification as a working example, this paper focuses on Bayesian inference of covariance (kernel) parameters using Mark…
AIS uses a suboptimal extended target distribution, which this paper improves using SGM.
problem Improving the efficiency of Annealed Importance Sampling for marginal likelihood estimation.
method Leveraging score-based generative modeling to approximate the optimal extended target distribution.
result Demonstrated novel, differentiable AIS procedures on synthetic and real-world data.
Optimizes AIS hyperparameters for efficient marginal likelihood estimation.
problem Limited computation budget affects AIS performance.
method Flexible intermediary distributions defined by residual density, parameter sharing, and fix linear schedule.
result Optimized-Path AIS reduces sampling iterations and improves performance.
mAIS improves free energy evaluation efficiency.
problem Computational infeasibility of exact free energy evaluation.
method mAIS, a marginalized version of AIS.
result mAIS is more efficient under certain conditions.
Corrects errors in ILA for Bayesian inference in LGMs.
problem Error in ILA for non-Gaussian likelihoods in LGMs.
method Importance sampling scheme to correct ILA errors.
result Corrected posterior converges to the true posterior with increased samples.
A new approach to cost-sensitive multiclass classification prioritizes certain classes over others.
problem Cost-sensitive multiclass classification where some classes are more important than others.
method Apportioned margin framework that shifts the decision boundary to prioritize certain classes.
result The method improves the error rate for important classes while reducing overall error.
UM-IS combines neural networks and importance sampling for efficient probabilistic inference.
problem High computational cost and lack of theoretical guarantees in probabilistic inference.
method Hybrid inference scheme combining neural networks and importance sampling.
result UM-IS outperforms sampling-based methods in efficiency and accuracy.
A new method estimates marginal likelihood using normalizing flows.
problem Estimating marginal likelihood in Bayesian model selection.
method Learned harmonic mean estimator using normalizing flows.
result Normalizing flows avoid the exploding variance problem.
Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dynamic models. These methods allow us to approximate the joint posterior distribution using sequential importance sampling. In this framework, the dimension of the target distribution grows with each time step, thus it is nec…
The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.
problem The need for accurate joint predictions in decision-making problems.
method The paper analyzes combinatorial decision problems, sequential predictions, and multi-armed bandits, introducing an approximate Thompson sampling algorithm and new regret bounds.
result Accurate joint predictions are essential for good performance in decision-making problems.
New method reduces variance in off-policy evaluation for RL.
problem Reducing variance in off-policy evaluation for RL with long horizons.
method Marginalized Importance Sampling (MIS) estimator.
result Achieves mean-squared error bound matching Cramer-Rao lower bound.
DAIS improves AIS for differentiable marginal likelihood estimation.
problem Differentiable marginal likelihood estimation for complex models.
method Proposes Differentiable Annealed Importance Sampling (DAIS) to make AIS differentiable.
result DAIS achieves convergence and consistency in Bayesian linear regression.
This survey explores various optimality concepts in importance sampling.
problem Designing optimal proposal densities for Monte Carlo methods.
method Review of multiple frameworks and theoretical comparisons.
result Comprehensive understanding of optimality in importance sampling.
New MI bounds improve estimation in deep generative models.
problem Estimating mutual information without density information is intractable.
method Importance sampling, Annealed Importance Sampling, Generalized IWAE, MINE-AIS.
result Improved bounds for estimating mutual information in deep models.
Paper proposes a new method for learning deep generative models using annealed importance sampling.
problem Learning deep generative models efficiently and accurately.
method Proposes annealed importance sampling as a bridge between variational inference and Markov chain Monte Carlo.
result Demonstrates improved density models and better trade-off between computation and model accuracy.
We consider the problem of inference in a causal generative model where the set of available observations differs between data instances. We show how combining samples drawn from the graphical model with an appropriate masking function makes it possible to train a single neural network to approximate all the correspond…
Deep RL approach improves MIS for complex environments.
problem Improving off-policy evaluation for complex environments.
method Uses successor representation from deep RL to decouple reward and dynamics.
result Empirically stable and applicable to high-dimensional domains.
Recent reports have described that the equivalent sample size (ESS) in a Dirichlet prior plays an important role in learning Bayesian networks. This paper provides an asymptotic analysis of the marginal likelihood score for a Bayesian network. Results show that the ratio of the ESS and sample size determine the penalty…
The support vector machine (SVM) is an important class of learning machines for function approach, pattern recognition, and time-serious prediction, etc. It maps samples into the feature space by so-called support vectors of selected samples, and then feature vectors are separated by maximum margin hyperplane. The pres…
Computing the marginal likelihood (ML) of a model requires marginalizing out all of the parameters and latent variables, a difficult high-dimensional summation or integration problem. To make matters worse, it is often hard to measure the accuracy of one's ML estimates. We present bidirectional Monte Carlo, a technique…
New offline RL method works with limited data and function approximators.
problem Sample efficiency with limited data and weak function approximators.
method Pessimistic algorithm based on version space formed by marginalized importance sampling (MIS), with gap assumption.
result Guarantees sample efficiency for simple algorithm under specific assumptions.
Study shows offline RL with partial coverage and weak function classes is possible.
problem Learning optimal policies from logged data with function approximation.
method Marginalized Importance Sampling (MIS) with additional covering distribution.
result Finite-sample guarantees for sample-efficient offline RL for general MDPs.
A distributed method for Bayesian model choice using marginal likelihood and Monte Carlo sampling.
problem Bayesian model choice in large datasets with limited communication.
method Split data into subsets, locally compute model evidence, combine results using summary statistics.
result The method enables model choice in large datasets with speed-ups and theoretical error bounds.
This paper resolves Breiman's dilemma in neural networks by analyzing phase transitions of margin dynamics.
problem Breiman's dilemma in neural networks: uniform margin improvement does not guarantee reduced generalization errors.
method Revisiting Breiman's dilemma in deep neural networks with spectrally normalized margins, analyzing phase transitions of normalized margin distributions.
result Margin-based generalization bounds can predict test error trends during training phase transitions.
Variational inference approximates the posterior distribution of a probabilistic model with a parameterized density by maximizing a lower bound for the model evidence. Modern solutions fit a flexible approximation with stochastic gradient descent, using Monte Carlo approximation for the gradients. This enables variatio…
SUMO provides unbiased log marginal likelihood estimation for latent variable models.
problem Biased estimates of log marginal likelihood in latent variable models.
method Randomized truncation of infinite series for unbiased estimation.
result Models trained with SUMO give better test-set likelihoods than standard methods.
Develops bounds predicting deep learning generalization using optimal transport.
problem Discrepancy between theoretical error bounds and empirical observations in deep learning.
method Margin-based generalization bounds with optimal transport costs.
result Theoretical bounds robustly predict generalization error on large datasets.
The paper explains why estimating a history-dependent policy can reduce MSE in reinforcement learning.
problem Understanding why history-dependent policies can improve MSE in off-policy evaluation.
method The paper derives a bias-variance decomposition of MSE for various OPE estimators, showing how history-dependent policies can decrease variance and increase bias.
result History-dependent policies can decrease the variance of importance sampling estimators, leading to lower MSE.
Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator. The estimator is a product of Poisson estimators,…
New method improves model explainability.
problem Improper model explanations fail to reflect true data-generating process.
method Shapley Marginal Surplus for Strong Models
result Significant outperformance in inferential capabilities.
A new method improves maximum margin criterion for better pattern analysis.
problem Handling high dimensionality and large data in pattern analysis.
method Introducing an improved maximum margin criterion (MMC) and its variants.
result Experimental results show the MMC methods are effective in complex scenarios.
New theorem for deep neural networks improves classification margins.
problem Improving classification margins in deep neural networks.
method Local class-purity theorem and margin p-values for training and testing samples.
result Enhanced understanding and computation of classification margins.
New methods improve Bayesian inference and decision-making in online learning.
problem Current Bayesian deep learning does not fully utilize joint predictives for sequential decision-making.
method Proposes new evaluation settings for active learning and active sampling, focusing on marginal and joint cross-entropies.
result Initial experiments suggest challenges in applying current BDL inference techniques in high-dimensional spaces.
New insights on bias in multi-armed bandits under conditional sampling.
problem Understanding bias in multi-armed bandits under conditional sampling.
method Characterized the sign of conditional bias of monotone functions of rewards.
result Sign of conditional bias can differ from marginal bias, depending on conditioning events.
Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, infections are caused by the interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI pr…
Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex and multimodal distributions. Such distributions are often sampled using simulated tempering, which augments the target space with an auxiliar…
A new active learning method that improves model diversity and performance.
problem Efficiently selecting examples to label for training multiple models.
method Trains multiple models on bootstrap samples and selects examples based on minimum margin.
result Min-margin outperforms other methods, especially with larger batch sizes.
Decision stumps accurately screen variables in nonparametric models.
problem Challenges in theoretical properties of tree-based variable importance measures.
method Derive performance guarantees for variable selection using a single-level CART decision tree (decision stump).
result Decision stumps can perform consistent model selection despite being inaccurate for estimation.
MACQ method explains deep learning models by analyzing feature contributions across prediction levels.
problem Explaining deep learning model predictions.
method Global gradient-based, model-agnostic approach focusing on marginal attribution.
result MACQ separates feature contributions from interaction effects and visualizes 3-way relationships.
The paper proposes a method to improve Bayesian inference for periodic data using data-driven priors.
problem Efficiency in approximating posterior distribution in models with periodicity.
method Construct a prior distribution from data using a Gaussian process with a periodic kernel, approximated using adaptive importance sampling.
result The proposed method improves the marginal posterior distribution of the period parameter.
A method detects out-of-distribution inputs using an ensemble of self-supervised classifiers.
problem Detecting out-of-distribution inputs in deep learning applications.
method Train classifiers by leaving out random subsets of training data as OOD data, using margin-based loss.
result Method outperforms state-of-the-art on OOD detection benchmarks.
Paper reviews methods for conditional sampling in generative diffusion models.
problem Extending generative diffusion models to sample from conditional distributions.
method Review of existing computational approaches to conditional sampling.
result Highlight key methodologies for constructing conditional generative samplers.
New method for mixed data FI controls type I error and achieves high power.
problem Statistical inadequacy of feature importance measures for mixed data.
method Combining CPI framework with sequential knockoffs for mixed data.
result Our method controls type I error and achieves high power for mixed data.
New methods for off-policy evaluation in reinforcement learning.
problem Improving off-policy evaluation in reinforcement learning.
method Developed MWL and MQL estimators for importance weights and value functions.
result MWL and MQL provide a unified view of algorithms in reinforcement learning.
The paper tackles fVaR prediction methods in finance.
problem Predicting future values at risk (fVaR) in finance.
method Various methods including Nested MC-empirical quantile, percentiles from distributions, quantile regressions, and limited inner simulations.
result Improved methods for predicting fVaRs, including those that are computationally efficient.
Improves sampling from complex hierarchical models using HMC and automatic marginalization.
problem Sampling from complex hierarchical models is difficult for HMC.
method Proposes automatic marginalization as part of the sampling process using HMC in a graphical model extracted from a PPL.
result Significantly improves sampling from real-world hierarchical models.