yusuke1997/dblp-id-title huggingface dataset:
A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning
Integrals over Gaussians under Linear Domain Constraints
Group testing for connected communities
Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs
Efficient Algorithms and Error Analysis for the Modified Nystrom Method
Sobolev Descent
Contextual Bandits with Stochastic Experts
Fast Threshold Tests for Detecting Discrimination
Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data
On-Demand Federated Learning for Arbitrary Target Class Distributions
Bethe Learning of Graphical Models via MAP Decoding
How To Backdoor Federated Learning
Greedy Spectral Embedding
International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain
High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching
Uncertainty Quantification for Low-Rank Matrix Completion with Heterogeneous and Sub-Exponential Noise
Positional Encoder Graph Neural Networks for Geographic Data
The Gaussian Process Autoregressive Regression Model (GPAR)
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
Computational Education using Latent Structured Prediction
Inference of Cause and Effect with Unsupervised Inverse Regression
Reinforcement Learning with Stepwise Fairness Constraints
Bayesian graphical models, intention-to-treat, and the rubin causal Model
Supervised Sequential Classification Under Budget Constraints
Mean field inference in a general probabilistic setting
Using Unsupervised Learning to Guide Resampling in Imbalanced Data Sets
Further Optimal Regret Bounds for Thompson Sampling
Optimistic Planning for the Stochastic Knapsack Problem
Clustering Oligarchies
Statistical Analysis of Complex Systems in Biomedicine
Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity
Multi-Dimensional Hyena for Spatial Inductive Bias
Density Ratio Estimation via Infinitesimal Classification
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
Sample efficient learning of image-based diagnostic classifiers via probabilistic labels
TenIPS: Inverse Propensity Sampling for Tensor Completion
Restructuring Tractable Probabilistic Circuits
On casting importance weighted autoencoder to an EM algorithm to learn deep generative models
On Parameter Priors for Discrete DAG Models
Bayesian Classifier Fusion with an Explicit Model of Correlation
Continuous Structure Constraint Integration for Robust Causal Discovery
A Spectral Analysis of Dot-product Kernels
An Improved Algorithm for Learning Drifting Discrete Distributions
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
Gray-box Inference for Structured Gaussian Process Models
Near-Optimal Machine Teaching via Explanatory Teaching Sets
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models
Towards Gradient Free and Projection Free Stochastic Optimization
Sparse Randomized Partition Trees for Nearest Neighbor Search
Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex
Robust Parameter Learning in Bayesian Networks with Missing Data
Contrastive learning of strong-mixing continuous-time stochastic processes
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
The Quantile Snapshot Scan: Comparing Quantiles of Spatial Data from Two Snapshots in Time
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning Uncertainty
Explicit Link Between Periodic Covariance Functions and State Space Models
Geometric Collaborative Filtering with Convergence
Disentangling Interactions and Dependencies in Feature Attributions
Hilbertian Metrics and Positive Definite Kernels on Probability Measures
Adaptive wavelet pooling for convolutional neural networks
Langevin Diffusion Variational Inference
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
BaCaDI: Bayesian Causal Discovery with Unknown Interventions
Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes
Focused Inference
Online Continuous Submodular Maximization
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
Nonparametric estimation and testing of exchangeable graph models
How to Learn a Graph from Smooth Signals
A Continuous-Time View of Early Stopping for Least Squares Regression
Dynamic Collaborative Filtering With Compound Poisson Factorization
Online Batch Decision-Making with High-Dimensional Covariates
Q-function Decomposition with Intervention Semantics for Factored Action Spaces
Adaptive importance sampling for heavy-tailed distributions via α-divergence minimization
Comparing Prequential Model Selection Criteria in Supervised Learning of Mixture Models
Combinatorial Preconditioners for Proximal Algorithms on Graphs
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning
Tighter Confidence Bounds for Sequential Kernel Regression
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
Online Binary Space Partitioning Forests
Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems
Harmonizable mixture kernels with variational Fourier features
Learning Where to Sample in Structured Prediction
How fine can fine-tuning be? Learning efficient language models
Exploiting Equality Constraints in Causal Inference
When OT meets MoM: Robust estimation of Wasserstein Distance
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects
Stochastic Three-Composite Convex Minimization with a Linear Operator
Rule Induction as Exploratory Data Analysis
Growth-Optimal Portfolio Selection under CVaR Constraints
Federated Linear Contextual Bandits with Heterogeneous Clients
Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data
Sobolev Norm Learning Rates for Conditional Mean Embeddings
Imputation estimators for unnormalized models with missing data
Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction
A primer on linear classification with missing data
Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition
Robust Learning from Discriminative Feature Feedback
Classification Using Bayes Averaging of Multiple, Relational Rule-based Models
Stochastic Structured Variational Inference
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
Towards Return Parity in Markov Decision Processes
Combinatorial Penalties: Which structures are preserved by convex relaxations?
Unifying local and global model explanations by functional decomposition of low dimensional structures
Lexicographic Optimization: Algorithms and Stability
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference
Exact Gradient Computation for Spiking Neural Networks via Forward Propagation
Improving Quadrature for Constrained Integrands
Unsupervised Feature Selection by Preserving Stochastic Neighbors
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation
A Deep Generative Model for Fragment-Based Molecule Generation
On Structured Filtering-Clustering: Global Error Bound and Optimal First-Order Algorithms
Why is parameter averaging beneficial in SGD? An objective smoothing perspective
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Efficient fair PCA for fair representation learning
Margin-distancing for safe model explanation
Online learning with feedback graphs and switching costs
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach
Contextual Constrained Learning for Dose-Finding Clinical Trials
Fitting low-rank models on egocentrically sampled partial networks
MEDUSA: Medical Data Under Shadow Attacks via Hybrid Model Inversion
Unsupervised Learning with Non-Ignorable Missing Data
Structured Optimal Transport
Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect
Off-Policy Risk Assessment for Markov Decision Processes
A Hybrid Approximation to the Marginal Likelihood
Hit-and-Run for Sampling and Planning in Non-Convex Spaces
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
Streaming Variational Inference for Bayesian Nonparametric Mixture Models
A unified view of likelihood ratio and reparameterization gradients
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent
Statistically Efficient Estimation for Non-Smooth Probability Densities
Clustering from Multiple Uncertain Experts
Stochastic blockmodeling of relational event dynamics
Some variations on variation independence.
Nonconvex Matrix Factorization from Rank-One Measurements
An Objective Function for Belief Net Triangulation
Understanding Progressive Training Through the Framework of Randomized Coordinate Descent
Adaptive Trade-Offs in Off-Policy Learning
Variational Schrödinger Momentum Diffusion
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning
Sequential Importance Sampling for Visual Tracking Reconsidered
Modelling Non-Smooth Signals with Complex Spectral Structure
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
Optimized Score Transformation for Fair Classification
Linear Convergence of Stochastic Frank Wolfe Variants
Generative Models as Distributions of Functions
Large Scale K-Median Clustering for Stable Clustering Instances
When MAML Can Adapt Fast and How to Assist When It Cannot
S-CFE: Simple Counterfactual Explanations
Nonstochastic Contextual Combinatorial Bandits
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems
Interpretable Cascade Classifiers with Abstention
Generalization Bounds for Label Noise Stochastic Gradient Descent
Hyperbolic graph embedding with enhanced semi-implicit variational inference
Understanding the wiring evolution in differentiable neural architecture search
A Safe Exploration Approach to Constrained Markov Decision Processes
"Plus/minus the learning rate": Easy and Scalable Statistical Inference with SGD
Unsupervised Link Selection in Networks
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback
Fast Robust Logistic Regression for Large Sparse Datasets with Binary Outputs
Scalable Nonparametric Multiway Data Analysis
Stochastic Variational Inference for the HDP-HMM
Near-optimal algorithms for private estimation and sequential testing of collision probability
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces
Discovering Many Diverse Solutions with Bayesian Optimization
Learning Gaussian Graphical Models via Multiplicative Weights
Finding Nearly Everything within Random Binary Networks
Generalised GPLVM with Stochastic Variational Inference
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
Reinforcement Learning for Mean Field Games with Strategic Complementarities
Comparison-Based Nearest Neighbor Search
Sparsity and the truncated \(l^2\)-norm
Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models
Revisiting Stochastic Extragradient
On the Computational Tractability of the (Many) Shapley Values
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
Neural Discovery of Permutation Subgroups
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011
A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery
End-to-end Feature Selection Approach for Learning Skinny Trees
Maximizing Agreements for Ranking, Clustering and Hierarchical Clustering via MAX-CUT
Learning Bayesian Networks Using Feature Selection
Statistical guarantees for local graph clustering
Outlier Detection and Robust Estimation in Nonparametric Regression
Best Arm Identification with Safety Constraints
Optimal and Private Learning from Human Response Data
Equivariant bootstrapping for uncertainty quantification in imaging inverse problems
Model-based Co-clustering for High Dimensional Sparse Data
Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation
Active Learning Algorithms for Graphical Model Selection
A unifying representation for a class of dependent random measures
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization
Tensor-view Topological Graph Neural Network
Survival regression with proper scoring rules and monotonic neural networks
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning
Variational Boosted Soft Trees
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, AISTATS 2005, Bridgetown, Barbados, January 6-8, 2005
Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representation
Streaming Kernel Principal Component Analysis
Trend Filtering on Graphs
Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition
From Shapley Values to Generalized Additive Models and back
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain
Private k-Means Clustering with Stability Assumptions
Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction
Hierarchical Bayesian Bandits
Federated f-Differential Privacy
Emergent specialization from participation dynamics and multi-learner retraining
Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances
New Coresets for Projective Clustering and Applications
Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks
Efficient optimization of large k real-time control algorithm
Improved Approximation for Fair Correlation Clustering
Semiparametric conformal prediction
On the Reducibility of Submodular Functions
Nuances in Margin Conditions Determine Gains in Active Learning
On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games
Fast and Bayes-consistent nearest neighbors
Average Adjusted Association: Efficient Estimation with High Dimensional Confounders
Controlling Bias in Adaptive Data Analysis Using Information Theory
Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
Calibrated Adaptive Probabilistic ODE Solvers
Falling Rule Lists
Loss-Curvature Matching for Dataset Selection and Condensation
Barlow Graph Auto-Encoder for Unsupervised Network Embedding
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification
Estimating Conditional Average Treatment Effects with Missing Treatment Information
Generalized Binary Search For Split-Neighborly Problems
Analysis of Privacy Leakage in Federated Large Language Models
Bayesian Structure Learning for Functional Neuroimaging
Non-Parametric Calibration for Classification
Model selection for behavioral learning data and applications to contextual bandits
Class Proportion Estimation with Application to Multiclass Anomaly Rejection
Lifted Division for Lifted Hugin Belief Propagation
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
Leaming Influence Diagram from Data
On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity
Filtered Search for Submodular Maximization with Controllable Approximation Bounds
Fast and Accurate Ranking Regression
Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes
An Analysis of Active Learning with Uniform Feature Noise
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles
Generative Model for Layers of Appearance and Deformation
Variational Inference for Neyman-Scott Processes
EM for Mixture of Linear Regression with Clustered Data
Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
Multi-Player Approaches for Dueling Bandits
Generalized Pseudolikelihood Methods for Inverse Covariance Estimation
Independent Learning in Constrained Markov Potential Games
DAGnosis: Localized Identification of Data Inconsistencies using Structures
Modular Block-diagonal Curvature Approximations for Feedforward Architectures
Student-t Processes as Alternatives to Gaussian Processes
Online-to-PAC generalization bounds under graph-mixing dependencies
Deep Value Function Networks for Large-Scale Multistage Stochastic Programs
Adversarial Training in High-Dimensional Regression: Generated Data and Neural Networks
Rapid Evaluation of Multiple Density Models
QuACK: A Multipurpose Queuing Algorithm for Cooperative k-Armed Bandits
The Binary Space Partitioning-Tree Process
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
Federated Heavy Hitters Discovery with Differential Privacy
Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence
Graphical Model Based Computer Adaptive Testing
A General Algorithm for Solving Rank-one Matrix Sensing
A Family of Algorithms for Finding Temporal Structure in Data
Consistent Complementary-Label Learning via Order-Preserving Losses
On the Interaction Effects Between Prediction and Clustering
Interpretable Almost-Exact Matching for Causal Inference
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes
Scalable Constrained Bayesian Optimization
A Graphical Model for Simultaneous Partitioning and Labeling
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression
On Margins and Derandomisation in PAC-Bayes
Deep Topic Models for Multi-label Learning
General Staircase Mechanisms for Optimal Differential Privacy
Probabilistic Approximate Least-Squares
SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search
Sparse Submodular Probabilistic PCA
Statistical Analysis of Karcher Means for Random Restricted PSD Matrices
Two-temperature logistic regression based on the Tsallis divergence
Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection
Regional Multi-Armed Bandits
Nonparametric Preference Completion
Fast Fourier Transform Reductions for Bayesian Network Inference
Regularity in Canonicalized Models: A Theoretical Perspective
Finite Sample Analysis of Mean-Volatility Actor-Critic for Risk-Averse Reinforcement Learning
Fast Classification with Binary Prototypes
Learning from biased positive-unlabeled data via threshold calibration
Information Projection and Approximate Inference for Structured Sparse Variables
Acceleration of Frank-Wolfe Algorithms with Open-Loop Step-Sizes
A single algorithm for both restless and rested rotting bandits
Adaptive Sampling for Coarse Ranking
Using Prediction to Improve Combinatorial Optimization Search
Deep Spectral Ranking
Statistical and Computational Rates in Graph Logistic Regression
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference
On the Consistent Recovery of Joint Distributions from Conditionals
Sample-and-threshold differential privacy: Histograms and applications
Continual Domain Adversarial Adaptation via Double-Head Discriminators
Task-Driven Discrete Representation Learning
Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable
Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness
Improved Bounds for Online Learning Over the Permutahedron and Other Ranking Polytopes
Variance-Dependent Regret Bounds for Nonstationary Linear Bandits
A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models
Get rid of your constraints and reparametrize: A study in NNLS and implicit bias
The Polynomial Iteration Complexity for Variance Exploding Diffusion Models: Elucidating SDE and ODE Samplers
Large-Scale Data-Dependent Kernel Approximation
Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information
Learning the Pareto Front Using Bootstrapped Observation Samples
Scaling Submodular Maximization via Pruned Submodularity Graphs
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components
List Learning with Attribute Noise
A Deterministic Streaming Sketch for Ridge Regression
Equalized odds postprocessing under imperfect group information
Smooth and Sparse Optimal Transport
Nearly Optimal Classification for Semimetrics
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
Stochastic Zeroth-order Optimization in High Dimensions
Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond
Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integrators
Information-theoretic Analysis of Bayesian Test Data Sensitivity
Reaping the Benefits of Bundling under High Production Costs
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits
Autoencoding any Data through Kernel Autoencoders
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces
Robust Stochastic Principal Component Analysis
Multi-Task Time Series Analysis applied to Drug Response Modelling
Few-shot Generative Modelling with Generative Matching Networks
Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy
Learning Rules-First Classifiers
Conformalized Deep Splines for Optimal and Efficient Prediction Sets
On Convergence in Wasserstein Distance and f-divergence Minimization Problems
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints
Learning Heterogeneous Hidden Markov Random Fields
Nonparametric Variable Screening with Optimal Decision Stumps
Bayesian Variable Selection in a Million Dimensions
Parameter-Free Online Linear Optimization with Side Information via Universal Coin Betting
Non-exchangeable feature allocation models with sublinear growth of the feature sizes
Scalable High-Order Gaussian Process Regression
Why did the distribution change?
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures
Significance of Gradient Information in Bayesian Optimization
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization
Structured variational inference in Bayesian state-space models
Fast Computation of Branching Process Transition Probabilities via ADMM