yusuke1997/dblp-id-title huggingface dataset:
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model
Exploration via linearly perturbed loss minimisation
XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage
Diffusion Models under Group Transformations
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix
Structural perspective on constraint-based learning of Markov networks
Competing with Automata-based Expert Sequences
Logical Team Q-learning: An approach towards factored policies in cooperative MARL
Principal Subspace Estimation Under Information Diffusion
When No-Rejection Learning is Consistent for Regression with Rejection
Differentiable Causal Discovery Under Unmeasured Confounding
Optimal Multi-Objective Best Arm Identification with Fixed Confidence
Learning Theory for Conditional Risk Minimization
Longitudinal Variational Autoencoder
Direct Learning to Rank And Rerank
Tensor Network Based Feature Learning Model
Precision Recall Cover: A Method For Assessing Generative Models
The Efficient Propagation of Arbitrary Subsets of Beliefs in Discrete-Valued Bayesian Networks
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior
An Analysis of the Adaptation Speed of Causal Models
Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch
Efficient computation of the the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes
Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization
Uni6Dv2: Noise Elimination for 6D Pose Estimation
Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables
Minimum Volume Topic Modeling
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms
Provable Adversarial Robustness for Fractional Lp Threat Models
Comparison Based Learning from Weak Oracles
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
Online Student-t Processes with an Overall-local Scale Structure for Modelling Non-stationary Data
Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts
Direct Loss Minimization for Sparse Gaussian Processes
Mean-Variance Analysis in Bayesian Optimization under Uncertainty
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients
Bures-Wasserstein Means of Graphs
The Tree Loss: Improving Generalization with Many Classes
Identification of Matrix Joint Block Diagonalization
Temporal Graph Neural Networks for Irregular Data
Improper Deep Kernels
The Teaching Dimension of Kernel Perceptron
Bayesian Learning of Neural Network Architectures
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
Fixing Mini-batch Sequences with Hierarchical Robust Partitioning
Deep Clustering via Probabilistic Ratio-Cut Optimization
Automatically Adaptive Conformal Risk Control
Classification using margin pursuit
Minimax Testing of Identity to a Reference Ergodic Markov Chain
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models
All models are wrong, some are useful: Model Selection with Limited Labels
Consistent Optimal Transport with Empirical Conditional Measures
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference
A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space
Defensive Forecasting
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning
Bayesian Structure Learning for Dynamic Brain Connectivity
Heavy-tailed Streaming Statistical Estimation
Asynchronous Randomized Trace Estimation
Characterizing Internal Evasion Attacks in Federated Learning
Subset verification and search algorithms for causal DAGs
Doubly Sparse Variational Gaussian Processes
Indeterminacy in Generative Models: Characterization and Strong Identifiability
Meta-learning Task-specific Regularization Weights for Few-shot Linear Regression
Regularization via Structural Label Smoothing
Efficient mining of statistical dependencies
Doubly Fair Dynamic Pricing
Streaming Feature Selection using IIC
A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
Applying a Gaussian-Bernoulli Mixture Model Network to Binary and Continuous Missing Data in Medicine
On the role of data in PAC-Bayes
Simulator Calibration under Covariate Shift with Kernels
On Searching for Generalized Instrumental Variables
Queuing dynamics of asynchronous Federated Learning
Any-dimensional equivariant neural networks
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
Learning to Negotiate via Voluntary Commitment
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction
Harmless interpolation in regression and classification with structured features
Stochastic local search for Bayesian network
Stochastic Linear Bandits Robust to Adversarial Attacks
A Model for Part-of-Speech Prediction
No-Regret Reinforcement Learning with Heavy-Tailed Rewards
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness
Unbounded Bayesian Optimization via Regularization
Initialization and Coordinate Optimization for Multi-way Matching
Extragradient Type Methods for Riemannian Variational Inequality Problems
Masked Training of Neural Networks with Partial Gradients
Detection and Defense of Topological Adversarial Attacks on Graphs
On the High Accuracy Limitation of Adaptive Property Estimation
MMD-based Variable Importance for Distributional Random Forest
Tensorized Random Projections
Maximal Couplings of the Metropolis-Hastings Algorithm
Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent
Bayesian Support Vector Regression
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions
Bayesian Structure Scores for Probabilistic Circuits
Black Box Submodular Maximization: Discrete and Continuous Settings
International Conference on Artificial Intelligence and Statistics, AISTATS 2025, Mai Khao, Thailand, 3-5 May 2025
Neural score matching for high-dimensional causal inference
BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders
Variational Inference on the Boolean Hypercube with the Quantum Entropy
On Parameter Estimation in Deviated Gaussian Mixture of Experts
Nested-Wasserstein Self-Imitation Learning for Sequence Generation
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation
A Theoretical Case Study of Structured Variational Inference for Community Detection
DE-HNN: An effective neural model for Circuit Netlist representation
Tensor Regression Meets Gaussian Processes
On the Implicit Bias of Gradient Descent for Temporal Extrapolation
Optimal downsampling for Imbalanced Classification with Generalized Linear Models
Pulling back information geometry
Tilted Variational Bayes
Quick Training of Probabilistic Neural Nets by Importance Sampling
On Predictive Classification of Binary Vectors
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents
Uncover Topic-Sensitive Information Diffusion Networks
Knockoffs for the Mass: New Feature Importance Statistics with False Discovery Guarantees
Causal Discovery under Off-Target Interventions
Learning Entangled Single-Sample Distributions via Iterative Trimming
Graph Approximation and Clustering on a Budget
Efficient Kernelized UCB for Contextual Bandits
No-regret Algorithms for Multi-task Bayesian Optimization
Mirror Descent View for Neural Network Quantization
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions
Predictive Power of Nearest Neighbors Algorithm under Random Perturbation
Detecting Imperfect Patterns in Event Streams Using Local Search
Sketch based Memory for Neural Networks
Back to the Future: Radial Basis Function Networks Revisited
Causal Inference with Selectively Deconfounded Data
Convergence of online k-means
Strong Screening Rules for Group-based SLOPE Models
Revisiting Projection-free Online Learning: the Strongly Convex Case
How Well Can Transformers Emulate In-Context Newton's Method?
Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees
Self-Supervised Quantization-Aware Knowledge Distillation
Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture
Conditions beyond treewidth for tightness of higher-order LP relaxations
Strategic Usage in a Multi-Learner Setting
An Online Algorithm for Smoothed Regression and LQR Control
A Single-Timescale Method for Stochastic Bilevel Optimization
Reward-Free Policy Space Compression for Reinforcement Learning
Near Optimal Bayesian Active Learning for Decision Making
Super-resolution Enhancement of Video
A Three Sample Hypothesis Test for Evaluating Generative Models
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
What made you do this? Understanding black-box decisions with sufficient input subsets
Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise
Exploring the robustness of Bayesian and information-theoretic methods for predictive inference
Overcomplete Independent Component Analysis via SDP
Convergence Properties of Stochastic Hypergradients
Tight Variational Bounds via Random Projections and I-Projections
Piecewise Stationary Bandits under Risk Criteria
Diameter-based Interactive Structure Discovery
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path
WASP: Scalable Bayes via barycenters of subset posteriors
Factor Analysis on a Graph
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization
Deep Switch Networks for Generating Discrete Data and Language
Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
Prediction Performance After Learning in Gaussian Process Regression
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function
Assessing Local Generalization Capability in Deep Models
Stochastic Block Transition Models for Dynamic Networks
Cross Validation for Correlated Data in Classification Models
Active Learning for Single Neuron Models with Lipschitz Non-Linearities
Bayesian Circular Regression with von Mises Quasi-Processes
Robust descent using smoothed multiplicative noise
Lipschitz Density-Ratios, Structured Data, and Data-driven Tuning
Online Forgetting Process for Linear Regression Models
Distributed optimization of deeply nested systems
Support and Invertibility in Domain-Invariant Representations
Forward Amortized Inference for Likelihood-Free Variational Marginalization
Regret Bounds for Lifelong Learning
Causal discovery in mixed additive noise models
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation
Semi-Verified PAC Learning from the Crowd
Deep Optimal Sensor Placement for Black Box Stochastic Simulations
Riemannian Accelerated Gradient Methods via Extrapolation
Asynchronous Gibbs Sampling
Encrypted Accelerated Least Squares Regression
Iterative Bayesian Learning for Crowdsourced Regression
Multi-Agent Learning in Contextual Games under Unknown Constraints
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
All or None: Identifiable Linear Properties of Next-Token Predictors in Language Modeling
A Scalable Algorithm for Individually Fair k-Means Clustering
Fair Representation Learning with Unreliable Labels
On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks
Certifiably Quantisation-Robust training and inference of Neural Networks
On the Generalization of Representations in Reinforcement Learning
Black-box Importance Sampling
A Maximum Matching Algorithm for Basis Selection in Spectral Learning
An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions
Mixture-of-Linear-Experts for Long-term Time Series Forecasting
Origins of Low-Dimensional Adversarial Perturbations
Safe Sequential Testing and Effect Estimation in Stratified Count Data
Resampling Base Distributions of Normalizing Flows
LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series
Data Representations in Learning
Differentiable Change-point Detection With Temporal Point Processes
Sparse Algorithms for Markovian Gaussian Processes
Estimating the accuracies of multiple classifiers without labeled data
On Data Efficiency of Meta-learning
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles
Adversarial Robustness of Flow-Based Generative Models
Online (and Offline) on an Even Tighter Budget
Sequence Length Independent Norm-Based Generalization Bounds for Transformers
International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain
Clustering Items From Adaptively Collected Inconsistent Feedback
Near-optimal max-affine estimators for convex regression
Fixed-Budget Change Point Identification in Piecewise Constant Bandits
ContextGen: Targeted Data Generation for Low Resource Domain Specific Text Classification
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Harnessing Causality in Reinforcement Learning with Bagged Decision Times
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Quantile Additive Trend Filtering
A Swiss Army Infinitesimal Jackknife
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
Online Assortment and Price Optimization Under Contextual Choice Models
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers
A Linearly-Convergent Stochastic L-BFGS Algorithm
Truncated Back-propagation for Bilevel Optimization
On Connecting Stochastic Gradient MCMC and Differential Privacy
Gaussian Process Bandit Optimization with Few Batches
ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data
Superiority of Multi-Head Attention: A Theoretical Study in Shallow Transformers in In-Context Linear Regression
Coherent Probabilistic Forecasting of Temporal Hierarchies
Poisson-Networks: A Model for Structured Poisson Processes
Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations
Shuffled Model of Differential Privacy in Federated Learning
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control
An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise
Policy Learning for Localized Interventions from Observational Data
Value Preserving State-Action Abstractions
Causal inference in degenerate systems: An impossibility result
Differentially Private Continual Release of Histograms and Related Queries
Theoretically Grounded Loss Functions and Algorithms for Adversarial Robustness
Learning to Top-K Search using Pairwise Comparisons
Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space
Bayesian Inference with Certifiable Adversarial Robustness
Out-of-distribution robustness for multivariate analysis via causal regularisation
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC
Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation
Measuring the robustness of Gaussian processes to kernel choice
All of the Fairness for Edge Prediction with Optimal Transport
The VampPrior Mixture Model
Scalable Gaussian Process Classification via Expectation Propagation
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
A New Modeling Framework for Continuous, Sequential Domains
A learning rule based method of feature extraction with application to acoustic signal classification
Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA
Being a Bit Frequentist Improves Bayesian Neural Networks
Bayesian Generalised Ensemble Markov Chain Monte Carlo
On the Privacy Risks of Algorithmic Recourse
Adaptive Variable Clustering in Gaussian Graphical Models
Planning by Probabilistic Inference
NoisyMix: Boosting Model Robustness to Common Corruptions
Accurate and conservative estimates of MRF log-likelihood using reverse annealing
Propagation of Gaussian belief functions
Bernoulli Race Particle Filters
Sparse Principal Component Analysis for High Dimensional Multivariate Time Series
Bridging Domains with Approximately Shared Features
Amortized Bayesian Prototype Meta-learning: A New Probabilistic Meta-learning Approach to Few-shot Image Classification
An Adaptive Method for Weak Supervision with Drifting Data
Amortized Inference of Variational Bounds for Learning Noisy-OR
Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity
Online multiple testing with e-values
An Incremental Construction of a Nonparametric Regression Model
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables
Deep Gaussian Processes
Variance Reduction via Antithetic Markov Chains
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
Formal Verification of Unknown Stochastic Systems via Non-parametric Estimation
Adaptively Partitioning Max-Affine Estimators for Convex Regression
Coping With Simulators That Don't Always Return
Randomized Exploration in Generalized Linear Bandits
Low-Rank Approximation of Weighted Tree Automata
Identity Testing of Reversible Markov Chains
Evaluating Model Robustness and Stability to Dataset Shift
Efficiently Sampling Probabilistic Programs via Program Analysis
Stochastic Mirror Descent for Large-Scale Sparse Recovery
Differentially Private Graph Data Release: Inefficiencies & Unfairness
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow
Federated Learning with Compression: Unified Analysis and Sharp Guarantees
Posterior Tracking Algorithm for Classification Bandits
A Unified Dynamic Approach to Sparse Model Selection
Nonparametric Indirect Active Learning
Risk Bounds on Aleatoric Uncertainty Recovery
A Generalized Linear Model for Principal Component Analysis of Binary Data
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
Matrix completability analysis via graph k-connectivity
Robust Graph Embedding with Noisy Link Weights
Private Non-Convex Federated Learning Without a Trusted Server
Distributional Adversarial Loss
Directed Hypergraph Representation Learning for Link Prediction
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Fast Non-Parametric Bayesian Inference on Infinite Trees
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting
Robust Offline Policy Learning with Observational Data from Multiple Sources
Bridging the Theoretical Gap in Randomized Smoothing
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization
Evading the Curse of Dimensionality in Unconstrained Private GLMs
Imitation-Regularized Offline Learning
Overcoming Prior Misspecification in Online Learning to Rank
Nearly Optimal Latent State Decoding in Block MDPs
On the Relationship Between Robustness and Expressivity of Graph Neural Networks
Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
Learning Sparse Fixed-Structure Gaussian Bayesian Networks
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
On the Asymptotic Optimality of Maximum Margin Bayesian Networks
Distribution-Free Distribution Regression
On the Strategyproofness of the Geometric Median
The Sample Complexity of Level Set Approximation
Tensor Network-Constrained Kernel Machines as Gaussian Processes
On the Privacy of Selection Mechanisms with Gaussian Noise
High Dimensional Robust Sparse Regression
Beyond the Policy Gradient Theorem for Efficient Policy Updates in Actor-Critic Algorithms
Stochastic algorithms with descent guarantees for ICA
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence
Multi-Domain Causal Representation Learning via Weak Distributional Invariances
Sequential Multiple Hypothesis Testing with Type I Error Control
Functional Flow Matching
Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors
Are All Linear Regions Created Equal?
Coresets for Data Discretization and Sine Wave Fitting
Collaborative Ranking for Local Preferences
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
Sample Elicitation
BlockBoost: Scalable and Efficient Blocking through Boosting
Optimal Statistical and Computational Rates for One Bit Matrix Completion
Adaptive Experiment Design with Synthetic Controls
Parameter estimation in state space models using particle importance sampling
Tensor Networks for Probabilistic Sequence Modeling
OOBN for Forensic Identification through Searching a DNA profiles' Database
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment
Linear Convergence of Adaptive Stochastic Gradient Descent
Spread Flows for Manifold Modelling
Counting Graphlets of Size k under Local Differential Privacy
System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy
Online Sparse Reinforcement Learning
On the bias of K-fold cross validation with stable learners
Tight Verification of Probabilistic Robustness in Bayesian Neural Networks
A Topology Layer for Machine Learning
Asynchronous Decentralized Optimization with Constraints: Achievable Speeds of Convergence for Directed Graphs
Federated Learning under Distributed Concept Drift
Learning Structured Models with the AUC Loss and Its Generalizations
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games
Identifying Confounding from Causal Mechanism Shifts
A Simple Approach for Non-stationary Linear Bandits
Active Online Learning with Hidden Shifting Domains
Sharp Thresholds of the Information Cascade Fragility Under a Mismatched Model
Sequential learning of the Pareto front for multi-objective bandits
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support