yusuke1997/dblp-id-title huggingface dataset:
Learning Sparse Additive Models with Interactions in High Dimensions
Provable Bayesian Inference via Particle Mirror Descent
Hyperparameter Transfer Learning with Adaptive Complexity
Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
Multi-resolution Time-Series Transformer for Long-term Forecasting
Resolving the Approximability of Offline and Online Non-monotone DR-Submodular Maximization over General Convex Sets
MODL: Multilearner Online Deep Learning
Learning Pareto manifolds in high dimensions: How can regularization help?
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo
Heuristic Search for Model Structure: the Benefits of Restraining Greed
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
Dropout as a Low-Rank Regularizer for Matrix Factorization
Scalable Higher-Order Tensor Product Spline Models
Meta Learning in the Continuous Time Limit
Computation-Aware Kalman Filtering and Smoothing
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization
On the Geometry and Optimization of Polynomial Convolutional Networks
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging
On Theory for BART
Probabilistic Conformal Prediction Using Conditional Random Samples
Approximate Equivariance in Reinforcement Learning
Transforming Gaussian Processes With Normalizing Flows
Linearly Convergent Frank-Wolfe without Line-Search
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
Collaborative non-parametric two-sample testing
StableMDS: A Novel Gradient Descent-Based Method for Stabilizing and Accelerating Weighted Multidimensional Scaling
Nonparametric estimation of Hawkes processes with RKHSs
A New Perspective on Learning Linear Separators with Large \(L_qL_p\) Margins
Bayesian optimisation under uncertain inputs
Gaussian Mean Testing under Truncation
Decoupling epistemic and aleatoric uncertainties with possibility theory
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation
An Axiomatization of Loglinear Models with an Application to the Model-Search Problem
Fast Rank-1 NMF for Missing Data with KL Divergence
Parabolic Continual Learning
AUC-based Selective Classification
Fourier Bases for Solving Permutation Puzzles
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
Pareto Set Identification With Posterior Sampling
LassoNet: Neural Networks with Feature Sparsity
Knowledge Acquisition for Human-In-The-Loop Image Captioning
Incremental Aggregated Riemannian Gradient Method for Distributed PCA
Memory-Efficient Optimization with Factorized Hamiltonian Descent
Efficient Second-Order Gradient Boosting for Conditional Random Fields
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness
Fast Markov chain Monte Carlo algorithms via Lie groups
Orthogonal Multi-Manifold Enriching of Directed Networks
Calm Composite Losses: Being Improper Yet Proper Composite
Estimating Class Membership Probabilities using Classifier Learners
Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence
On Distributionally Robust Optimization and Data Rebalancing
Structured Disentangled Representations
A Linear-time Independence Criterion Based on a Finite Basis Approximation
Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations
Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods
Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions
Maillard Sampling: Boltzmann Exploration Done Optimally
A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation
Learning signals defined on graphs with optimal transport and Gaussian process regression
Pathwise Derivatives for Multivariate Distributions
Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions
The role of optimization geometry in single neuron learning
Minimax Nonparametric Two-Sample Test under Adversarial Losses
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity
Causal Inference under Networked Interference and Intervention Policy Enhancement
Thompson Sampling Itself is Differentially Private
Gaussian Regression with Convex Constraints
Optimizing Early Warning Classifiers to Control False Alarms via a Minimum Precision Constraint
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning
Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
Unbiased Smoothing using Particle Independent Metropolis-Hastings
Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models
Approximate Leave-one-out Cross Validation for Regression with ℓ 1 Regularizers
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization
MARS: Masked Automatic Ranks Selection in Tensor Decompositions
Statistical Sparse Online Regression: A Diffusion Approximation Perspective
Batch, match, and patch: low-rank approximations for score-based variational inference
Learning Extensive-Form Perfect Equilibria in Two-Player Zero-Sum Sequential Games
Efficient Rank Aggregation via Lehmer Codes
Training a Spiking Neural Network with Equilibrium Propagation
Learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order Optimization
On adaptivity and minimax optimality of two-sided nearest neighbors
General Identification of Dynamic Treatment Regimes Under Interference
Grassmann Stein Variational Gradient Descent
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate
Bayesian Conditional Random Fields
Regularized Policies are Reward Robust
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Sampling from Arbitrary Functions via PSD Models
MMD-B-Fair: Learning Fair Representations with Statistical Testing
Joint Structure Learning of Multiple Non-Exchangeable Networks
Stochastic algorithms for entropy-regularized optimal transport problems
Scalable Thompson Sampling via Optimal Transport
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities
One-Round Communication Efficient Distributed M-Estimation
Lagrange Coded Computing: Optimal Design for Resiliency, Security, and Privacy
Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference
Handling Missing and Unreliable Information in Speech Recognition
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures
SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI
Privacy-preserving Sparse Generalized Eigenvalue Problem
Diversity and Generalization in Neural Network Ensembles
Efficient Distributed Topic Modeling with Provable Guarantees
Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention
Scalable Bicriteria Algorithms for Non-Monotone Submodular Cover
A Stepwise uncertainty reduction approach to constrained global optimization
Principled Approaches for Private Adaptation from a Public Source
Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with Cardinality Constraint
A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries
Structure optimization of density estimation models applied to regression problems with dynamic noise
Semi-Supervised Learning with Adaptive Spectral Transform
Differentially Private Reward Estimation with Preference Feedback
Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem
Looped ReLU MLPs May Be All You Need as Practical Programmable Computers
Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Learning Sparse Nonparametric DAGs
Reparameterizing Distributions on Lie Groups
Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth
Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis
Optimal transport with f-divergence regularization and generalized Sinkhorn algorithm
High-probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent under Heavy-tailed Noise
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns
Bayesian Decision Theory on Decision Trees: Uncertainty Evaluation and Interpretability
CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models
Physics Informed Deep Kernel Learning
LIMESegment: Meaningful, Realistic Time Series Explanations
Estimating Total Correlation with Mutual Information Estimators
Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs
Minimax-optimal semi-supervised regression on unknown manifolds
On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction
Using Causal Knowledge to Learn More Useful Decision Rules From Data
Accumulations of Projections - A Unified Framework for Random Sketches in Kernel Ridge Regression
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms
Corruption Robust Offline Reinforcement Learning with Human Feedback
The non-parametric bootstrap and spectral analysis in moderate and high-dimension
Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm
One Scan 1-Bit Compressed Sensing
Backward Filtering Forward Deciding in Linear Non-Gaussian State Space Models
Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature
Last Layer Marginal Likelihood for Invariance Learning
Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM
Sequential Kernelized Stein Discrepancy
Learning in Markov Random Fields with Contrastive Free Energies
Is Multinomial PCA Multi-faceted Clustering or Dimensionality Reduction?
Less than a Single Pass: Stochastically Controlled Stochastic Gradient
Resampled Priors for Variational Autoencoders
Statistical challenges to inductive inference in linked data
Reparameterized Variational Rejection Sampling
Learning with Partial Forgetting in Modern Hopfield Networks
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Performance Bounds for Graphical Record Linkage
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery
Bounding Evidence and Estimating Log-Likelihood in VAE
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation
On Tradeoffs in Learning-Augmented Algorithms
Bayesian Gaussian Process ODEs via Double Normalizing Flows
TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression
k-experts - Online Policies and Fundamental Limits
Learnable Bernoulli Dropout for Bayesian Deep Learning
Gradient-Informed Neural Network Statistical Robustness Estimation
Learning conditional probabilities from incomplete databases - An experimental comparison
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters
Nyström Kernel Stein Discrepancy
LITE: Efficiently Estimating Gaussian Probability of Maximality
Scalable Generalized Dynamic Topic Models
Fisher Information and Natural Gradient Learning in Random Deep Networks
Towards Efficient Data Valuation Based on the Shapley Value
Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel
The Power of Recursion in Graph Neural Networks for Counting Substructures
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Rapid Convergence of Informed Importance Tempering
AdaDelay: Delay Adaptive Distributed Stochastic Optimization
On the Inherent Privacy of Zeroth-Order Projected Gradient Descent
Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations
Clustered Support Vector Machines
Sketching for Kronecker Product Regression and P-splines
Many processors, little time: MCMC for partitions via optimal transport couplings
Learning of Non-Parametric Control Policies with High-Dimensional State Features
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information
Convex Multi-Task Learning by Clustering
Probabilistic Multilevel Clustering via Composite Transportation Distance
Restricted concentration models - graphical Gaussian models with concentration parameters restricted to being equal
Differentially Private Range Queries with Correlated Input Perturbation
Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries
Optimal Submodular Extensions for Marginal Estimation
Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression
The communication cost of security and privacy in federated frequency estimation
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression
A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets
Beyond Sentiment: The Manifold of Human Emotions
Multi-agent Multi-armed Bandit Regret Complexity and Optimality
Optimal Testing in the Experiment-rich Regime
Efficient Data Shapley for Weighted Nearest Neighbor Algorithms
Generative Flow Networks as Entropy-Regularized RL
On the Interpretability of Conditional Probability Estimates in the Agnostic Setting
Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates
Approximate Inference and Forecast Algorithms in Graphical Models for Partially Observed Dynamic Systems
Local Perturb-and-MAP for Structured Prediction
Learning Quantile Functions for Temporal Point Processes with Recurrent Neural Splines
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation
Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery
Exponential Weights on the Hypercube in Polynomial Time
LdSM: Logarithm-depth Streaming Multi-label Decision Trees
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data
Particle-based Adversarial Local Distribution Regularization
Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints
Isotropic Gaussian Processes on Finite Spaces of Graphs
Model-Agnostic Counterfactual Explanations for Consequential Decisions
Graph Sparsification Approaches for Laplacian Smoothing
Permutation estimation and minimax rates of identifiability
Linear-time training of nonlinear low-dimensional embeddings
Fast Fourier Bayesian Quadrature
Model-Based Uncertainty in Value Functions
Fusing Individualized Treatment Rules Using Secondary Outcomes
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization
Decentralized gradient methods: does topology matter?
Robust Score Matching
A Robust-Equitable Copula Dependence Measure for Feature Selection
Multi-class classification in nonparametric active learning
Curve Clustering with Random Effects Regression Mixtures
From Cost-Sensitive to Tight F-measure Bounds
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
Provable local learning rule by expert aggregation for a Hawkes network
Policy Teaching via Data Poisoning in Learning from Human Preferences
Confident Least Square Value Iteration with Local Access to a Simulator
Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices
Data centering in feature space
Stability and Risk Bounds of Iterative Hard Thresholding
Generalized Group Testing
On the Assumptions of Synthetic Control Methods
Adaptivity of Diffusion Models to Manifold Structures
Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation
Non-separable Spatio-temporal Graph Kernels via SPDEs
Data-splitting improves statistical performance in overparameterized regimes
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization
Comparing Comparators in Generalization Bounds
An Asymptotic Rate for the LASSO Loss
An Unsupervised Hunt for Gravitational Lenses
Variational Integrator Networks for Physically Structured Embeddings
Latent Point Process Allocation
Meta-Learning Divergences for Variational Inference
On the Asymptotic Mean Square Error Optimality of Diffusion Models
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
Particle Gibbs for Bayesian Additive Regression Trees
GP-VAE: Deep Probabilistic Time Series Imputation
Fair Disaster Containment via Graph-Cut Problems
Sharp Analysis of a Simple Model for Random Forests
Electronic Medical Records Assisted Digital Clinical Trial Design
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations
Estimation of partially known Gaussian graphical models with score-based structural priors
Understanding the Generalization Benefits of Late Learning Rate Decay
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning
On the application of the bootstrap for computing confidence measures on features of induced Bayesian networks
Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers
Dynamic Copula Networks for Modeling Real-valued Time Series
Bandit Pareto Set Identification: the Fixed Budget Setting
Quick Streaming Algorithms for Maximization of Monotone Submodular Functions in Linear Time
High Probability Bounds for Stochastic Continuous Submodular Maximization
Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
Hadamard Wirtinger Flow for Sparse Phase Retrieval
Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models
Learning Hidden Quantum Markov Models
Sample Efficient Learning of Factored Embeddings of Tensor Fields
On Pruning for Score-Based Bayesian Network Structure Learning
Credibility-Aware Multimodal Fusion Using Probabilistic Circuits
Identifiable Feature Learning for Spatial Data with Nonlinear ICA
Overparameterized Random Feature Regression with Nearly Orthogonal Data
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions
Improved Robust Algorithms for Learning with Discriminative Feature Feedback
Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex Optimization
Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging
Learning Safety Constraints from Demonstrations with Unknown Rewards
A unifying theorem for spectral embedding and clustering
Select and Optimize: Learning to aolve large-scale TSP instances
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity
Graph Partitioning with a Move Budget
Variational Resampling
Marginalising over Stationary Kernels with Bayesian Quadrature
On Contrastive Divergence Learning
The exploration of new methods for learning in binary Boltzmann machines
Approximate Inference for Infinite Contingent Bayesian Networks
Regret-Optimal Filtering
Differentially Private Regression with Unbounded Covariates
Paths and Ambient Spaces in Neural Loss Landscapes
Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions
Multi-objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
The Geometry of Random Features
Sparse and Low-rank Tensor Estimation via Cubic Sketchings
Feasible Q-Learning for Average Reward Reinforcement Learning
Data Appraisal Without Data Sharing
Nonlinear Acceleration of Primal-Dual Algorithms
Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Information-Theoretic Advisors in Invisible Chess
Pointwise sampling uncertainties on the Precision-Recall curve
Bayesian Logistic Gaussian Process Models for Dynamic Networks
Manifold Restricted Interventional Shapley Values
An Information-Theoretic Route from Generalization in Expectation to Generalization in Probability
Exploiting the Limits of Structure Learning via Inherent Symmetry
On the connection between Noise-Contrastive Estimation and Contrastive Divergence
Riemannian Laplace Approximation with the Fisher Metric
Timing as an Action: Learning When to Observe and Act
Reconstructing Test Labels from Noisy Loss Functions
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
Equivariant Representation Learning via Class-Pose Decomposition
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era
Subsampling Error in Stochastic Gradient Langevin Diffusions
Analysis of Kernel Mirror Prox for Measure Optimization
Nystrom Approximation for Large-Scale Determinantal Processes
Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners
An improved training algorithm for kernel Fisher discriminants
Data Generation for Neural Programming by Example
Leveraging Frozen Batch Normalization for Co-Training in Source-Free Domain Adaptation
Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability
Reduced Rank Approximations of Transition Matrices
Using Sliced Mutual Information to Study Memorization and Generalization in Deep Neural Networks
Reversible Jump Probabilistic Programming
Reducing training time by efficient localized kernel regression
The Ordered Matrix Dirichlet for State-Space Models
Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization
Instrumental variable tests for Directed Acyclic Graph Models
Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer
Unbiased Quantization of the L 1 Ball for Communication-Efficient Distributed Mean Estimation
Contextual Bandits with Latent Confounders: An NMF Approach
Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach
Mixture model clustering with the multimix program
Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
TD-GEN: Graph Generation Using Tree Decomposition
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands, Spain, April 21-23, 2012
Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling
Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs
Probabilistic Integral Circuits
Memorization in Attention-only Transformers
Conditional Sparse $L_p$-norm Regression With Optimal Probability
Mitigating Underfitting in Learning to Defer with Consistent Losses
Q-learning with Logarithmic Regret
Online Algorithm for Unsupervised Sensor Selection
Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing
Towards a Complete Benchmark on Video Moment Localization
Sequential Inference for Deep Gaussian Process