yusuke1997/dblp-id-title huggingface dataset:
Natural Language Counterfactual Explanations for Graphs Using Large Language Models
Learning Bayesian networks with mixed variables
Post Selection Inference with Kernels
Quantifying Knowledge Distillation using Partial Information Decomposition
Hermitian matrices for clustering directed graphs: insights and applications
Simple and Scalable Constrained Clustering: a Generalized Spectral Method
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model
Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models
Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport
Linear Thompson Sampling Revisited
Learning linear structural equation models in polynomial time and sample complexity
On the Complexity of Representation Learning in Contextual Linear Bandits
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization
Sample Efficient Graph-Based Optimization with Noisy Observations
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise
Evaluating model calibration in classification
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework
The Sample Complexity of Meta Sparse Regression
Learning a Single Neuron for Non-monotonic Activation Functions
Automatic structured variational inference
Neural Enhanced Dynamic Message Passing
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs
Learning multivariate temporal point processes via the time-change theorem
Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
Shadow Manifold Hamiltonian Monte Carlo
Anderson acceleration of coordinate descent
A note on the comparison of polynomial selection methods
Stochastic Multi-Armed Bandits with Strongly Reward-Dependent Delays
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Scaling Nonparametric Bayesian Inference via Subsample-Annealing
Label differential privacy via clustering
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
On Information Gain and Regret Bounds in Gaussian Process Bandits
Ordered Weighted L1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects
Correspondence Analysis Using Neural Networks
Deep equilibrium models as estimators for continuous latent variables
Metalearning Linear Bandits by Prior Update
Non-linear process convolutions for multi-output Gaussian processes
A Tale of Sampling and Estimation in Discounted Reinforcement Learning
An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo
Adaptive Discretization for Evaluation of Probabilistic Cost Functions
A Deep Generative Deconvolutional Image Model
Differential privacy for symmetric log-concave mechanisms
Changepoint Estimation in Sparse Dynamic Stochastic Block Models under Near-Optimal Signal Strength
Lipschitz Continuous Autoencoders in Application to Anomaly Detection
Distributionally Robust Policy Gradient for Offline Contextual Bandits
Estimators of Entropy and Information via Inference in Probabilistic Models
On the Expected Size of Conformal Prediction Sets
Length independent PAC-Bayes bounds for Simple RNNs
Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity
Performative Prediction in a Stateful World
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
Accelerated Stochastic Gradient Method for Composite Regularization
Unsupervised Alignment of Embeddings with Wasserstein Procrustes
Domain-Liftability of Relational Marginal Polytopes
Similarity Learning for High-Dimensional Sparse Data
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees
Learning Fair Representations for Kernel Models
FedBaF: Federated Learning Aggregation Biased by a Foundation Model
Simulation-Free Schrödinger Bridges via Score and Flow Matching
On the Linear Convergence of Policy Gradient Methods for Finite MDPs
Surveillance Evasion Through Bayesian Reinforcement Learning
A Variational Inference Approach to Learning Multivariate Wold Processes
A Note on Cyclic Graphs and Dynamical Feedback Systems
Optimal Sparse Survival Trees
Improving predictions of Bayesian neural nets via local linearization
The Solution Path of SLOPE
Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization
E(3)-Equivariant Mesh Neural Networks
Fast Dynamic Sampling for Determinantal Point Processes
Provably Efficient Actor-Critic for Risk-Sensitive and Robust Adversarial RL: A Linear-Quadratic Case
Self-Compatibility: Evaluating Causal Discovery without Ground Truth
Batch-Expansion Training: An Efficient Optimization Framework
Approximate Data Deletion from Machine Learning Models
ASAGA: Asynchronous Parallel SAGA
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
Loopy Belief Propagation in the Presence of Determinism
Thompson Sampling with a Mixture Prior
Modularity-based Sparse Soft Graph Clustering
Minimizing Nonconvex Non-Separable Functions
Learning spectrograms with convolutional spectral kernels
When the Universe is Too Big: Bounding Consideration Probabilities for Plackett-Luce Rankings
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Stochastic Bandits with Delay-Dependent Payoffs
Random Consensus Robust PCA
Membership Testing in Markov Equivalence Classes via Independence Queries
LOFT: Finding Lottery Tickets through Filter-wise Training
Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis
Faster Kernel Interpolation for Gaussian Processes
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
On Kernel Derivative Approximation with Random Fourier Features
Robust Non-linear Normalization of Heterogeneous Feature Distributions with Adaptive Tanh-Estimators
Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity
Sparse Dueling Bandits
A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems
Towards Stability and Optimality in Stochastic Gradient Descent
Ridge Regression with Over-parametrized Two-Layer Networks Converge to Ridgelet Spectrum
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent
Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side-Information
A Causal Calculus for Statistical Research
Boosting methodology for regression problems
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints
Understanding Robustness in Teacher-Student Setting: A New Perspective
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
Multi-level Gaussian Graphical Models Conditional on Covariates
A Theoretical and Practical Framework for Regression and Classification from Truncated Samples
Density Ratio-based Proxy Causal Learning Without Density Ratios
Recovery Guarantees for Distributed-OMP
On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition
Invertible Generative Modeling using Linear Rational Splines
Randomized Primal-Dual Methods with Adaptive Step Sizes
Locally Private Mean Estimation: $Z$-test and Tight Confidence Intervals
Geometrically Enriched Latent Spaces
An Efficient and Continuous Voronoi Density Estimator
Computing high-dimensional optimal transport by flow neural networks
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms
Second Order Path Variationals in Non-Stationary Online Learning
qttPOTS: Efficient Batch Multiobjective Bayesian Optimization via Pareto Optimal Thompson Sampling
Minimax Density Estimation for Growing Dimension
Learning Shared Subgraphs in Ising Model Pairs
High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation
Classification of Adolescents' Risky Behavior in Instant Messaging Conversations
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport
Robust Cost Sensitive Support Vector Machine
Learning Fair Division from Bandit Feedback
Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions
Deep Structured Mixtures of Gaussian Processes
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes?
Deep Kernel Learning
Convolutional Persistence as a Remedy to Neural Model Analysis
Approximate Cross-validation: Guarantees for Model Assessment and Selection
Adaptive ADMM with Spectral Penalty Parameter Selection
Quantization based Fast Inner Product Search
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models
Graph Coarsening with Preserved Spectral Properties
Compressed Rule Ensemble Learning
A Higher-Order Kolmogorov-Smirnov Test
Identification of Blackwell Optimal Policies for Deterministic MDPs
Region-Based Active Learning
Tightness of LP Relaxations for Almost Balanced Models
Adversarial Risk Bounds through Sparsity based Compression
Learning with Hyperspherical Uniformity
An LP for Sequential Learning Under Budgets
Projection-Free Bandit Convex Optimization
Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces
Private Learning with Public Features
Density-Regression: Efficient and Distance-aware Deep Regressor for Uncertainty Estimation under Distribution Shifts
On the Suboptimality of Negative Momentum for Minimax Optimization
Interpreting Black Box Predictions using Fisher Kernels
Generalization Error of Invariant Classifiers
Making Better Use of Unlabelled Data in Bayesian Active Learning
Improving Stochastic Cubic Newton with Momentum
Multiplicative Gaussian Particle Filter
Part-of-Speech Tagging from "Small" Data Sets
Flexible Copula-Based Mixed Models in Deep Learning: A Scalable Approach to Arbitrary Marginals
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
Universal Models of Multivariate Temporal Point Processes
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach
PAC Mode Estimation using PPR Martingale Confidence Sequences
Low-Rank Generalized Linear Bandit Problems
Learning Robust Graph Neural Networks with Limited Supervision
Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms
Learning Complexity of Simulated Annealing
Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
Best-item Learning in Random Utility Models with Subset Choices
Dirichlet Pruning for Convolutional Neural Networks
ROTI-GCV: Generalized Cross-Validation for right-ROTationally Invariant Data
Local experts combination through density decomposition
Simulating weighted automata over sequences and trees with transformers
Looping in the Human: Collaborative and Explainable Bayesian Optimization
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
Viewpoint-Based Measurement of Semantic Similarity between Words
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
Metric recovery from directed unweighted graphs
Cross-Modal Imputation and Uncertainty Estimation for Spatial Transcriptomics
On Convergence of Lookahead in Smooth Games
Corralling Stochastic Bandit Algorithms
Conditional Gradients for the Approximately Vanishing Ideal
Reinforcement Learning for Adaptive Mesh Refinement
Smooth Bandit Optimization: Generalization to Holder Space
Integrating Uncertainty Awareness into Conformalized Quantile Regression
Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices
Random Oscillators Network for Time Series Processing
Learning Markov Networks With Arithmetic Circuits
Completeness Results for Lifted Variable Elimination
Learning Latent Partial Matchings with Gumbel-IPF Networks
Entropic Matching for Expectation Propagation of Markov Jump Processes
WWW Cache Layout to Ease Network Overload
On Coresets for Fair Regression and Individually Fair Clustering
Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications
AdaBlock: SGD with Practical Block Diagonal Matrix Adaptation for Deep Learning
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs
Structured Neural Topic Models for Reviews
Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees
Online Continual Adaptation with Active Self-Training
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds
A Bayesian Approach for Stochastic Continuum-armed Bandit with Long-term Constraints
Distributionally Robust Submodular Maximization
Analysis of Network Lasso for Semi-Supervised Regression
Accelerating Online Convex Optimization via Adaptive Prediction
Fast Block Coordinate Descent for Non-Convex Group Regularizations
Latent Gaussian process with composite likelihoods and numerical quadrature
Variational Autoencoder with Learned Latent Structure
Coordinate Descent for SLOPE
Collapsed Variational Bayesian Inference for Hidden Markov Models
Obtaining Causal Information by Merging Datasets with MAXENT
Unifying Clustered and Non-stationary Bandits
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
The Joint Causal Effect in Linear Structural Equation Model and Its Application to Process Analysis
Intrinsic Gaussian Vector Fields on Manifolds
Meta-learning for Robust Anomaly Detection
High Dimensional Bayesian Optimization using Lasso Variable Selection
Boosting Transfer Learning with Survival Data from Heterogeneous Domains
Optimal Cooperative Inference
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
Fast and accurate optimization on the orthogonal manifold without retraction
Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond
Random Warping Series: A Random Features Method for Time-Series Embedding
Learning the Pareto Set Under Incomplete Preferences: Pure Exploration in Vector Bandits
Real-time On-line Learning of Transformed Hidden Markov Models from Video
Parameter learning from incomplete data for Bayesian networks
On Ranking-based Tests of Independence
Efficient Interpolation of Density Estimators
Probabilistic Querying of Continuous-Time Event Sequences
Bayesian Model Averaging in Rule Induction
On Subjective Uncertainty Quantification and Calibration in Natural Language Generation
Learning and Evaluation in Presence of Non-i.i.d. Label Noise
A Non-Parametric EM-Style Algorithm for Imputing Missing Values
Scalable and Sound Low-Rank Tensor Learning
On Linear Model with Markov Signal Priors
The Implicit Regularization of Ordinary Least Squares Ensembles
Learning Proposals for Practical Energy-Based Regression
Global Convergence of Over-parameterized Deep Equilibrium Models
Optimal estimation of linear non-Gaussian structure equation models
Don't Be Pessimistic Too Early: Look K Steps Ahead!
Sum-Product-Quotient Networks
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning
Exploration in Reward Machines with Low Regret
Enumerating Equivalence Classes of Bayesian Networks using EC Graphs
Causally Inspired Regularization Enables Domain General Representations
Learning with minibatch Wasserstein : asymptotic and gradient properties
GraphAdaMix: Enhancing Node Representations with Graph Adaptive Mixtures
Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes
DivMCuts: Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
Differentially private algorithms for linear queries via stochastic convex optimization
Fast Variational Estimation of Mutual Information for Implicit and Explicit Likelihood Models
Distilling Policy Distillation
Competing with an Infinite Set of Models in Reinforcement Learning
Adaptive Cholesky Gaussian Processes
On Statistical Optimality of Variational Bayes
Scalable Greedy Feature Selection via Weak Submodularity
Efficient Variational Inference for Gaussian Process Regression Networks
Dueling RL: Reinforcement Learning with Trajectory Preferences
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity
Deep Non-crossing Quantiles through the Partial Derivative
Bayesian Nonparametric Poisson Factorization for Recommendation Systems
Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere
Temporal Matching under Uncertainty
Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks
Learning Determinantal Point Processes in Sublinear Time
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport
On the Global Optimum Convergence of Momentum-based Policy Gradient
Kernel Conditional Moment Constraints for Confounding Robust Inference
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
Feasible Recourse Plan via Diverse Interpolation
Certified private data release for sparse Lipschitz functions
Analyzing Generative Models by Manifold Entropic Metrics
To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models
Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets
Noisy Low-Rank Matrix Completion via Transformed L 1 Regularization and its Theoretical Properties
Efficient Computing of Stochastic Complexity
Optimal Zero-Shot Detector for Multi-Armed Attacks
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2013, Scottsdale, AZ, USA, April 29 - May 1, 2013
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates
Distributed Offline Policy Optimization Over Batch Data
Theoretical Convergence Guarantees for Variational Autoencoders
Learning and Generalization in Overparameterized Normalizing Flows
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
Online Active Model Selection for Pre-trained Classifiers
Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings
Efficient Graph Laplacian Estimation by Proximal Newton
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features
Fast generalization error bound of deep learning from a kernel perspective
Level Set Teleportation: An Optimization Perspective
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach
Investigating the Role of Negatives in Contrastive Representation Learning
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits
Latent Gaussian Models for Topic Modeling
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization
On the Power of Multitask Representation Learning with Gradient Descent
Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding
Learning Revenue-Maximizing Auctions With Differentiable Matching
Stochastic Linear Contextual Bandits with Diverse Contexts
Learning Matching Representations for Individualized Organ Transplantation Allocation
Singular Value Representation: A New Graph Perspective On Neural Networks
Fundamental Limits of Perfect Concept Erasure
Effective Nonlinear Feature Selection Method based on HSIC Lasso and with Variational Inference
A Statistical Model for Event Sequence Data
Learning Structured Low-Rank Representation via Matrix Factorization
Zero-Shot Action Generalization with Limited Observations
Matching Map Recovery with an Unknown Number of Outliers
On searching for optimal classifiers among Bayesian networks
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems
Sharpened Lazy Incremental Quasi-Newton Method
Elastic Representation: Mitigating Spurious Correlations for Group Robustness
Balanced Off-Policy Evaluation in General Action Spaces
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
A Reduction from Reinforcement Learning to No-Regret Online Learning
Gap-Dependent Bounds for Two-Player Markov Games
High-dimensional Time Series Clustering via Cross-Predictability
Signal-based Bayesian Seismic Monitoring
Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit
PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time
A Unifying Variational Framework for Gaussian Process Motion Planning
Implicit Regularization in Deep Tucker Factorization: Low-Rankness via Structured Sparsity
Functional Stochastic Gradient MCMC for Bayesian Neural Networks
A principled framework for the design and analysis of token algorithms
Convex Collective Matrix Factorization
Learning extensible multi-entity directed graphical models
Amortized Rejection Sampling in Universal Probabilistic Programming
Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory Costs
Loss Bounds and Time Complexity for Speed Priors
Nonmyopic Gaussian Process Optimization with Macro-Actions
Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization
Picking the Best Expert from a Sequence
MINTY: Rule-based models that minimize the need for imputing features with missing values
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
Structure based SAT dataset for analysing GNN generalisation
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
Kernel Interpolation for Scalable Online Gaussian Processes
Factor Analysis with Correlated Topic Model for Multi-Modal Data
Provably Efficient Reinforcement Learning via Surprise Bound
Model Averaging with Bayesian Network Classifiers
A Multi-Task Learning Approach to Linear Multivariate Forecasting
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data
Cost-aware simulation-based inference
Importance-weighted Positive-unlabeled Learning for Distribution Shift Adaptation
A Causal Framework for Evaluating Deferring Systems
Behavior-Inspired Neural Networks for Relational Inference
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
Dimensionality estimation without distances
Scoring anomalies: a M-estimation formulation