yusuke1997/dblp-id-title huggingface dataset:
Efficient Statistics for Sparse Graphical Models from Truncated Samples
Approximate Inference Using DC Programming For Collective Graphical Models
On Learning Mixture Models with Sparse Parameters
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
On Euclidean k-Means Clustering with alpha-Center Proximity
Structured Multi-task Learning for Molecular Property Prediction
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness
Steering No-Regret Agents in MFGs under Model Uncertainty
Measuring Mutual Information Between All Pairs of Variables in Subquadratic Complexity
Learning High-dimensional Gaussians from Censored Data
The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan
Fair Machine Unlearning: Data Removal while Mitigating Disparities
Sample-Efficient Imitation Learning via Generative Adversarial Nets
Pareto Optimal Model Selection in Linear Bandits
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition
The Log-Shift Penalty for Adaptive Estimation of Multiple Gaussian Graphical Models
Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery
On the Sample Complexity of Next-Token Prediction
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
Scalable Hash-Based Estimation of Divergence Measures
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, AISTATS 2014, Reykjavik, Iceland, April 22-25, 2014
A prior-based approximate latent Riemannian metric
A la Carte - Learning Fast Kernels
Spectral Representation for Causal Estimation with Hidden Confounders
Combining Neural Network Regression Estimates Using Principal Components
Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
On the effectiveness of the skew divergence for statistical language analysis
Learning One-hidden-layer ReLU Networks via Gradient Descent
Nothing but Regrets - Privacy-Preserving Federated Causal Discovery
Fairness Risks for Group-Conditionally Missing Demographics
Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications
Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning
Efficient Online Bayesian Inference for Neural Bandits
Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio
Hybrid Discriminative-Generative Approach with Gaussian Processes
To go deep or wide in learning?
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
Modeling Skill Acquisition Over Time with Sequence and Topic Modeling
Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation
Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage
Learning in Stochastic Monotone Games with Decision-Dependent Data
Model Consistency for Learning with Mirror-Stratifiable Regularizers
Hierarchical Clustering for Euclidean Data
How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability
Fast Sparse Classification for Generalized Linear and Additive Models
Clustering High-dimensional Data with Ordered Weighted ℓ 1 Regularization
Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States
Consistency of Dictionary-Based Manifold Learning
Pessimistic Off-Policy Multi-Objective Optimization
Performance Metric Elicitation from Pairwise Classifier Comparisons
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors
Entropy-driven inference and inconsistency
Learning Generative Models with Sinkhorn Divergences
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization
When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time?
Online Competitive Influence Maximization
On the challenges of learning with inference networks on sparse, high-dimensional data
Pick-to-Learn and Self-Certified Gaussian Process Approximations
Adaptive Exploration in Linear Contextual Bandit
Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation
Microfoundation inference for strategic prediction
Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds
One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits
Robust Learning under Strong Noise via SQs
Statistical Aspects of Stochastic Logic Programs
Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons
Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication
On the Calibration of Probabilistic Classifier Sets
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization
Accelerated Stochastic Power Iteration
Additive Model Boosting: New Insights and Path(ologie)s
Learning to Generalize Provably in Learning to Optimize
The Importance of Future Information in Credit Card Fraud Detection
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
Comparing Predictive Inference Methods for Discrete Domains
Greedy and IHT Algorithms for Non-convex Optimization with Monotone Costs of Non-zeros
Sketch-and-lift: scalable subsampled semidefinite program for K-means clustering
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning
Spectral Tensor Train Parameterization of Deep Learning Layers
Distribution of Gaussian Process Arc Lengths
Strong Lottery Ticket Hypothesis with ε-perturbation
A Subquadratic Time Approximation Algorithm for Individually Fair k-Center
Fully-Automatic Bayesian Piecewise Sparse Linear Models
Multi-attribute Bayesian optimization with interactive preference learning
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
Variance reduction properties of the reparameterization trick
Asynchronous Doubly Stochastic Group Regularized Learning
Discrete Action On-Policy Learning with Action-Value Critic
Minimizing Convex Functionals over Space of Probability Measures via KL Divergence Gradient Flow
On the Vulnerability of Fairness Constrained Learning to Malicious Noise
HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent
Robust Interpretation of Neural Network models
On Minimax Optimality of GANs for Robust Mean Estimation
Causal Entropy Optimization
Efficient Transfer Learning Method for Automatic Hyperparameter Tuning
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning
A Shared Low-Rank Adaptation Approach to Personalized RLHF
On Boosting and the Exponential Loss
Rate-Regularization and Generalization in Variational Autoencoders
Model-agnostic out-of-distribution detection using combined statistical tests
Chernoff Sampling for Active Testing and Extension to Active Regression
Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades
Noise-Aware Differentially Private Variational Inference
Mixed-Effect Thompson Sampling
Online learning in bandits with predicted context
Online Calibrated and Conformal Prediction Improves Bayesian Optimization
Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows
Generalizing the theory of cooperative inference
Adaptive Discretization for Event PredicTion (ADEPT)
FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery
Alternating Direction Method of Multipliers for Quantization
On Model Selection Consistency of Lasso for High-Dimensional Ising Models
Approximate Inference with Wasserstein Gradient Flows
SMOGS: Social Network Metrics of Game Success
Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility
A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks
DAG-Structured Clustering by Nearest Neighbors
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
Finite-Time Error Bounds for Biased Stochastic Approximation with Applications to Q-Learning
Simulation-Based Stacking
Active Exploration in Markov Decision Processes
Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition
Stochastic Gradient Descent Meets Distribution Regression
Debiasing Samples from Online Learning Using Bootstrap
ODE parameter inference using adaptive gradient matching with Gaussian processes
Blind Demixing via Wirtinger Flow with Random Initialization
Incremental Tree-Based Inference with Dependent Normalized Random Measures
Provable Policy Gradient Methods for Average-Reward Markov Potential Games
Variational Rejection Sampling
FairRR: Pre-Processing for Group Fairness through Randomized Response
Discovering and Exploiting Additive Structure for Bayesian Optimization
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization
Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Models
Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function
Proxy Methods for Domain Adaptation
Cost aware Inference for IoT Devices
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, AISTATS 2007, San Juan, Puerto Rico, March 21-24, 2007
Robust Importance Weighting for Covariate Shift
A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response
Acceleration and Implicit Regularization in Gaussian Phase Retrieval
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift
Adversarial robustness of VAEs through the lens of local geometry
Multi-armed Bandit Experimental Design: Online Decision-making and Adaptive Inference
Reinforcement Learning in Parametric MDPs with Exponential Families
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
Clustered Invariant Risk Minimization
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, AISTATS 2001, Key West, Florida, USA, January 4-7, 2001
Learning While Scheduling in Multi-Server Systems With Unknown Statistics: MaxWeight with Discounted UCB
Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression
Sparse Accelerated Exponential Weights
Efficient Training of Structured SVMs via Soft Constraints
Graph Machine Learning through the Lens of Bilevel Optimization
Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron
Breaking isometric ties and introducing priors in Gromov-Wasserstein distances
Improving Classifier Confidence using Lossy Label-Invariant Transformations
Learning GPLVM with arbitrary kernels using the unscented transformation
Explaining the Explainer: A First Theoretical Analysis of LIME
Combining Conjugate Direction Methods with Stochastic Approximation of Gradients
A Blessing of Dimensionality in Membership Inference through Regularization
A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
Influence Diagnostics under Self-concordance
A Topological Regularizer for Classifiers via Persistent Homology
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning
Online Learning in Contextual Second-Price Pay-Per-Click Auctions
Scalable MCMC for Mixed Membership Stochastic Blockmodels
Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Interaction Detection with Bayesian Decision Tree Ensembles
Differentially Private Weighted Sampling
Statistical Tests for Contagion in Observational Social Network Studies
Extensions of Undirected and Acyclic, Directed Graphical Models
Designing Optimal Binary Rating Systems
Lovasz Convolutional Networks
DYNACARE: Dynamic Cardiac Arrest Risk Estimation
Federated Reinforcement Learning with Environment Heterogeneity
Nearly Optimal Algorithms for Level Set Estimation
Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants
Provable Estimation of the Number of Blocks in Block Models
Active Learning for Undirected Graphical Model Selection
Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection
On learning history-based policies for controlling Markov decision processes
A latent variable model for multivariate discretization
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent
Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes
Mixed Strategies for Robust Optimization of Unknown Objectives
Score Operator Newton transport
Tracking Objects with Higher Order Interactions via Delayed Column Generation
Convergence diagnostics for stochastic gradient descent with constant learning rate
Parallel MCMC Without Embarrassing Failures
Average case analysis of Lasso under ultra sparse conditions
Feature screening with kernel knockoffs
TempTest: Local Normalization Distortion and the Detection of Machine-generated Text
Using Classification Trees to Improve Causal Inferences in Observational Studies
Flow-based Alignment Approaches for Probability Measures in Different Spaces
Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds
On Feynman-Kac training of partial Bayesian neural networks
DPFL: Decentralized Personalized Federated Learning
Point Cloud Generation with Continuous Conditioning
Generalization Bounds for Stochastic Saddle Point Problems
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
InnerThoughts: Disentangling Representations and Predictions in Large Language Models
Adaptive Approximate Policy Iteration
Warping Layer: Representation Learning for Label Structures in Weakly Supervised Learning
Very Large SVM Training using Core Vector Machines
Dynamical Selection of Learning Algorithms
PF 2 ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
On the Capacity Limits of Privileged ERM
Hierarchical Probabilistic Neural Network Language Model
Global Multi-armed Bandits with Hölder Continuity
Efficient Conformal Prediction under Data Heterogeneity
Free-form Flows: Make Any Architecture a Normalizing Flow
Product Manifold Learning
Manifold-Aligned Counterfactual Explanations for Neural Networks
Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees
Invariant Aggregator for Defending against Federated Backdoor Attacks
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects
Mixtures of All Trees
Sensing Cox Processes via Posterior Sampling and Positive Bases
Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions
On the Consistency Rate of Decision Tree Learning Algorithms
Ethics in Action: Training Reinforcement Learning Agents for Moral Decision-making In Text-based Adventure Games
Deterministic Decoding for Discrete Data in Variational Autoencoders
Solving the Robust Matrix Completion Problem via a System of Nonlinear Equations
Federated Myopic Community Detection with One-shot Communication
Dissecting the Impact of Model Misspecification in Data-Driven Optimization
Accelerating Imitation Learning with Predictive Models
A Case of Exponential Convergence Rates for SVM
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
On the Behavior of MDL Denoising
Latent variable modeling with random features
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime
Data-Driven Confidence Intervals with Optimal Rates for the Mean of Heavy-Tailed Distributions
Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Models
A recursive estimate for the predictive likelihood in a topic model
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
Scalable Meta-Learning with Gaussian Processes
Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields
Deep Generative Missingness Pattern-Set Mixture Models
Revealing Graph Bandits for Maximizing Local Influence
Geometry, moments and Bayesian networks with hidden variables
On Preference-based Stochastic Linear Contextual Bandits with Knapsacks
Chained Gaussian Processes
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Cross-validated Likelihood for Model Selection in Unsupervised Learning
Enriched mixtures of generalised Gaussian process experts
A Multiclass Classification Approach to Label Ranking
Testing Conditional Independence with Deep Neural Network Based Binary Expansion Testing (DeepBET)
Approximation Algorithms for Orthogonal Non-negative Matrix Factorization
Poisoning Bayesian Inference via Data Deletion and Replication
Error bounds for sparse classifiers in high-dimensions
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Online Learning to Rank with Feedback at the Top
Testing Generated Distributions in GANs to Penalize Mode Collapse
Reinforcement Learning for Constrained Markov Decision Processes
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model
Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections
Identifying Copeland Winners in Dueling Bandits with Indifferences
Meta-Uncertainty in Bayesian Model Comparison
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting
Learning Inconsistent Preferences with Gaussian Processes
Actor-Critic Fictitious Play in Simultaneous Move Multistage Games
Asymptotically exact inference in differentiable generative models
Modal-set estimation with an application to clustering
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning
Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space
Generalized Linear Models for Aggregated Data
Implicit Kernel Learning
Foundations of Bayesian Learning from Synthetic Data
Structure Learning of Mixed Graphical Models
Large deviations rates for stochastic gradient descent with strongly convex functions
Control Functionals for Quasi-Monte Carlo Integration
Label Filters for Large Scale Multilabel Classification
Optimal estimation of Gaussian DAG models
Protein Fitness Landscape: Spectral Graph Theory Perspective
Learning Bayesian Networks From Dependency Networks: A Preliminary Study
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning
Batched Large-scale Bayesian Optimization in High-dimensional Spaces
The True Sample Complexity of Identifying Good Arms
Neural Topic Model with Attention for Supervised Learning
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer
Tighter Theory for Local SGD on Identical and Heterogeneous Data
Boosting Variational Inference: an Optimization Perspective
Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach
Local Causal Discovery with Linear non-Gaussian Cyclic Models
Compressed Sensing with Very Sparse Gaussian Random Projections
Accurate Shapley Values for explaining tree-based models
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes
Dimensionality Reduction and Prioritized Exploration for Policy Search
Balanced Off-Policy Evaluation for Personalized Pricing
Differentially Private Federated Learning on Heterogeneous Data
Credal Two-Sample Tests of Epistemic Uncertainty
Best-Arm Identification in Unimodal Bandits
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications
Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets
Towards an Understanding of Default Policies in Multitask Policy Optimization
Fast and Smooth Interpolation on Wasserstein Space
Weighted Gaussian Process Bandits for Non-stationary Environments
Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons
AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization
Differentially Private Synthetic Control
Online Learning with Non-Convex Losses and Non-Stationary Regret
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Clamping Improves TRW and Mean Field Approximations
Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptation
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems
Neural Enhanced Belief Propagation on Factor Graphs
Tight Analysis of Difference-of-Convex Algorithm (DCA) Improves Convergence Rates for Proximal Gradient Descent
Flexible Accuracy for Differential Privacy
Distributed Adaptive Sampling for Kernel Matrix Approximation
Factorized Recurrent Neural Architectures for Longer Range Dependence
A Framework for Optimal Matching for Causal Inference
Model-Free Linear Quadratic Control via Reduction to Expert Prediction
Approximate Top-m Arm Identification with Heterogeneous Reward Variances
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions
Matroids, Matchings, and Fairness
A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback
Learning-Augmented Algorithms for Online Concave Packing and Convex Covering Problems
Node Feature Kernels Increase Graph Convolutional Network Robustness
Energy-Based Models for Functional Data using Path Measure Tilting
The Curse of Passive Data Collection in Batch Reinforcement Learning
Causal Modeling with Stochastic Confounders
FuSSO: Functional Shrinkage and Selection Operator
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
Locally Private Sampling with Public Data
Provable Continual Learning via Sketched Jacobian Approximations
The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits
Selective Classification via One-Sided Prediction
Bayesian Hierarchical Models for Counterfactual Estimation
A Mini-Block Fisher Method for Deep Neural Networks
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
Estimating Functionals of the Out-of-Sample Error Distribution in High-Dimensional Ridge Regression
Top-m identification for linear bandits
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection