A new method uses Schrödinger bridges for deep conditional generative learning.
problem Learning conditional distributions with additional information.
method Schrödinger bridge approach with discretized SDE and deep neural network.
result Generated samples have higher quality and can estimate conditional density.
Generative neural nets learn deep policies conditioned on goals.
problem Learning optimal policies for specific goals in reinforcement learning.
method Goal-conditioned neural nets that generate deep neural policies.
result Single learned policy generator can achieve any desired return.
DRMMs enable flexible conditional sampling for interactive machine learning.
problem Limited flexibility in conditional sampling for deep generative models.
method Proposes Deep Residual Mixture Models (DRMMs) that allow flexible conditional sampling.
result DRMMs enable sampling with arbitrary combinations of conditioning variables and priors.
Proposes model-based robust deep learning to handle natural variation in data.
problem Deep learning's fragility to natural variation in data.
method Develops model-based robust training algorithms using deep generative models to learn natural variation.
result Deep neural networks trained with model-based algorithms outperform standard and norm-bounded robust algorithms.
In this paper, we address the problem of conditional modality learning, whereby one is interested in generating one modality given the other. While it is straightforward to learn a joint distribution over multiple modalities using a deep multimodal architecture, we observe that such models aren't very effective at cond…
Proposes a new framework for deep learning conditional mean estimation with confidence regions.
problem Lack of asymptotic properties in deep nonparametric regression models.
method Transforms deep estimation into conditional diffusion model for conditional mean estimation.
result Developed end-to-end convergence rate and asymptotic normality for conditional diffusion model.
The article proposes a deep learning method to test and infer the Markov property in time series data.
problem Testing and inferring the Markov property in high-dimensional time series data.
method Deep conditional generative learning to estimate conditional density functions and derive a doubly robust test statistic.
result The test controls the type-I error asymptotically and has power approaching one.
A new method for learning conditional distributions using ODEs and neural networks.
problem Learning conditional distributions efficiently and accurately.
method Conditional Föllmer Flow, discretized with Euler's method, using nonparametric velocity estimation.
result Effective approximation of target conditional distributions, with convergence results for Wasserstein-2 distance.
Proposes a deep learning method to ensure non-crossing quantiles in conditional distributions.
problem Non-crossing quantiles issue in deep learning QR models.
method Generic deep learning algorithm enforcing quantile monotonicity.
result Ensures non-crossing quantiles up to machine precision.
New approach uses deep generative models for inventory and pricing decisions.
problem Data-driven inventory and pricing decisions in feature-based newsvendor problems.
method Conditional deep generative models (cDGMs) to learn demand distribution and generate probabilistic forecasts.
result Effective in optimizing inventory and pricing decisions, with theoretical guarantees and real-world applications.
Deep neural operators learn complex probabilistic models efficiently.
problem Learning complex probabilistic models with global Lipschitz conditions.
method Deep neural-operator framework under global Lipschitz conditions.
result Explicit network-size bounds for universal approximation of probabilistic models.
Zero loss is achievable in overparametrized DL networks under specific conditions.
problem Achieving zero loss in overparametrized deep learning networks.
method Determine sufficient conditions for zero loss attainability and present an explicit construction of zero loss minimizers.
result Explicit minimizers for zero loss in overparametrized DL networks are constructed without gradient descent.
Unified approach for nonparametric regression and conditional distribution learning.
problem Nonparametric regression and conditional distribution learning problems.
method Generative learning framework with deep neural networks to estimate a conditional generator.
result The approach estimates a regression function and a conditional generator simultaneously, providing good prediction intervals.
SIGMA prior enables federated learning for non-factorizable models.
problem Current FL methods assume conditional independence, limiting applicability to non-factorizable models.
method SIGMA prior approximates deep generative model to induce conditional independence structure.
result SIGMA prior expands FL applicability to fields requiring modeling dependencies.
Deep Neural Networks (DNNs), with its promising performance, are being increasingly used in safety critical applications such as autonomous driving, cancer detection, and secure authentication. With growing importance in deep learning, there is a requirement for a more standardized framework to evaluate and test deep l…
Continuous-time SGD converges under certain conditions, useful for deep learning.
problem Minimizing population expected loss in learning problems.
method Continuous-time approximation of stochastic gradient descent.
result Establishes sufficient conditions for convergence, applicable to overparametrized neural networks.
New algorithm ensures global convergence in deep neural networks beyond NTK regime.
problem Existing global convergence guarantees do not apply to practical deep networks.
method Proposes an algorithm with global convergence guarantees under the expressivity condition.
result Algorithm ensures global convergence in practical settings beyond NTK regime.
New model generates unseen attribute combinations from limited data.
problem Lack of generalization in deep generative models for unseen attribute combinations.
method Introduces multilinear latent conditioning to capture multiplicative interactions.
result Demonstrates effectiveness on MNIST, Fashion-MNIST, and CelebA datasets.
Framework generates personalized insulin treatment strategies using deep models.
problem Developing optimal personalized treatment strategies for diabetes patients.
method Combines deep generative time series models with decision theory.
result Demonstrated improved personalized insulin treatment strategies for diabetes patients.
In recent years, intelligent condition-based monitor-ing of rotary machinery systems has become a major researchfocus of machine fault diagnosis. In condition-based monitoring,it is challenging to form a large-scale well-annotated datasetdue to the expense of data acquisition and costly annotation.The generated data ha…
A novel deep bootstrap framework for nonparametric regression using conditional diffusion models.
problem Nonparametric regression with efficient sampling and accurate estimation.
method Conditional diffusion model for learning conditional distributions, integrating sampling and regression into a unified generative framework.
result Established optimal convergence rates in the Wasserstein distance and convergence guarantees for the bootstrap procedure.
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provably exchangeable, meaning that the joint distribution over observations is invariant under permutation: this property lies at the heart of Ba…
Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case o…
We study likelihood-based methods for distribution regression with deep generative models.
problem Distribution regression with high-dimensional responses concentrated on a low-dimensional manifold.
method Likelihood-based approach using conditional deep generative models.
result Convergence rates for estimating conditional distributions in Hellinger and Wasserstein metrics.
We identify causal models with unobserved confounding using bijective generation mechanisms.
problem Identifying causal relationships with unobserved confounders.
method Establish counterfactual identifiability for BGMs and propose a learning method.
result Learned BGMs enable efficient counterfactual estimation.
A new model combines deep learning and Gaussian Processes with hyperdata learning.
problem Combining deep learning and Gaussian Processes for expressive and robust learning.
method Conditional Deep Gaussian Process (DGP) with hyperdata learning and approximate inference.
result Conditional DGP offers better expressiveness and robustness compared to existing methods.
Study on deep neural networks for reward modeling with pairwise comparison data.
problem Reward modeling with deep neural networks in non-parametric settings.
method Established a non-asymptotic regret bound for deep reward estimators, introduced a margin-type condition.
result Improved regret bound for deep reward estimators, highlighting the importance of clear human beliefs.
A new method quantizes conditional probability measures using deep learning.
problem Quantizing conditional probability measures efficiently.
method DCMQ method using Huber-energy kernel and deep neural network.
result Promising results on various examples.
Paper introduces GDR-learners for estimating potential outcomes from observational data.
problem Lack of theoretical property of general Neyman-orthogonality in deep generative models.
method Develops flexible GDR-learners based on various deep generative models.
result GDR-learners possess quasi-oracle efficiency and rate double robustness, asymptotically optimal.
CoFinDiff generates synthetic financial data capturing stylized facts and meeting specified conditions.
problem Limited data availability and difficulty in controlling synthetic financial data generation.
method Conditional diffusion model with cross-attention to incorporate conditions derived from price data.
result Synthetic data generated by CoFinDiff accurately meets specified conditions for trends and volatility.
A new deep learning model improves asset pricing predictions.
problem Improving asset pricing models for better predictions.
method Pseudo-Siamese Network (SNAP) for conditional asset pricing.
result The SNAP model outperforms benchmarks in out-of-sample prediction and Sharpe ratio.
Proposes a method to forecast non-stationary time series.
problem Challenges of non-stationary conditional distributions in deep learning.
method Bayesian dynamic model + deep conditional distribution model.
result Adapts to non-stationary time series better than state-of-the-art solutions.
Kernel density matrices simplify probabilistic deep learning.
problem Representing joint probability distributions of continuous and discrete variables.
method Extending density matrices to a reproducing kernel Hilbert space.
result Versatile representation for marginal and joint probability distributions.
SONA improves conditional generation by balancing authenticity and alignment.
problem Challenges in balancing authenticity and conditional alignment in conditional generative models.
method SONA integrates unconditional discrimination, matching-aware supervision, and adaptive weighting to balance authenticity and alignment.
result SONA achieves superior sample quality and conditional alignment compared to state-of-the-art methods.
Study develops a new tool for assessing asphalt pavement conditions using deep learning.
problem Challenges in automated pavement distress detection via road images.
method Developed a hybrid model using YOLO for classification and U-net for segmentation, creating a comprehensive pavement condition tool.
result Created a new asphalt pavement condition index using deep learning.
New method uses neural networks for estimating survival functions from censored data.
problem Estimating conditional survival functions from censored time-to-event data with complex predictors.
method Generative adversarial networks leveraging self-consistent equations, without parametric assumptions.
result Established the convergence rate of the proposed estimator.
CEFOL uses deep learning for dynamic programming with recursive utility.
problem Challenges in solving dynamic programming problems with recursive utility.
method Introduces a separate neural network for certainty equivalent, uses first-order optimality conditions to learn value and policy functions.
result CEFOL achieves high accuracy in learning value and policy functions, matching VFI benchmarks.
A nonparametric family of conditional distributions is introduced, which generalizes conditional exponential families using functional parameters in a suitable RKHS. An algorithm is provided for learning the generalized natural parameter, and consistency of the estimator is established in the well specified case. In ex…
A new method uses deep learning to price barrier options.
problem Pricing barrier options with boundary conditions.
method Forward deep BSDEs with added nodes for barrier conditions.
result Can handle any barrier condition and boundary conditions.
There has been much recent, exciting work on combining the complementary strengths of latent variable models and deep learning. Latent variable modeling makes it easy to explicitly specify model constraints through conditional independence properties, while deep learning makes it possible to parameterize these conditio…
Deeper neural networks learn lower frequency functions faster, according to a new principle.
problem Understanding why deeper learning is faster.
method Fourier analysis and filtering method to separate and analyze the frequency distribution of neural network outputs.
result Deeper hidden layers in neural networks bias towards lower frequency functions during training.
We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, which assumes that all classifiers are conditionally independent, is {\em equivalent} to a Restricted Boltzmann Machine (RBM) with a single hidd…
We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, from the approximation perspective. In particular, we establish general sufficient conditions for universal approximation using continuous-time deep residual networks, which can …
This paper aims to evaluate the suitability of current deep learning methods for clinical workflow especially by focusing on dermatology. Although deep learning methods have been attempted to get dermatologist level accuracy in several individual conditions, it has not been rigorously tested for common clinical complai…
Paper tackles fast convergence for non-convex strongly-concave min-max problems.
problem Non-convex strongly-concave min-max problems in deep learning.
method Proximal stage-based method with PL condition for faster convergence.
result Established fast convergence in primal objective gap and duality gap.
MSRL learns a representation maximizing mutual info with response variables.
problem Learning sufficient representations for complex, multi-dimensional data.
method Variational mutual information, deep neural networks, generalized Dudley's inequality.
result MSRL achieves consistent and accurate representation learning.
Deep residual networks (ResNets) have demonstrated better generalization performance than deep feedforward networks (FFNets). However, the theory behind such a phenomenon is still largely unknown. This paper studies this fundamental problem in deep learning from a so-called "neural tangent kernel" perspective. Specific…
As is known, an option price is a solution to a certain partial differential equation (PDE) with terminal conditions (payoff functions). There is a close association between the solution of PDE and the solution of a backward stochastic differential equation (BSDE). We can either solve the PDE to obtain option prices or…