yusuke1997/dblp-id-title huggingface dataset:
Online Model Selection for Reinforcement Learning with Function Approximation
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy
Online Learning and Blackwell Approachability with Partial Monitoring: Optimal Convergence Rates
Model Evaluation in the Dark: Robust Classifier Metrics with Missing Labels
CRAFT: ClusteR-specific Assorted Feature selecTion
Regret Minimization for Causal Inference on Large Treatment Space
Lifted MAP Inference for Markov Logic Networks
Spatial Decompositions for Large Scale SVMs
Evaluating Prediction-based Interventions with Human Decision Makers In Mind
Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
Distributional Counterfactual Explanations With Optimal Transport
Bayesian Principles Improve Prompt Learning In Vision-Language Models
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
A bayesian model for collaborative filtering
On the Misspecification of Linear Assumptions in Synthetic Controls
Federated Experiment Design under Distributed Differential Privacy
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data
Sparsistency of 1-Regularized M-Estimators
TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility
On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition
Deep Joint Source-Channel Coding with Iterative Source Error Correction
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
Learning One-hidden-layer Neural Networks under General Input Distributions
Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers
A Simulation Study of Three Related Causal Data Mining Algorithms
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
Can we Generalize and Distribute Private Representation Learning?
Warped Diffusion for Latent Differentiation Inference
A Unified Statistically Efficient Estimation Framework for Unnormalized Models
Infinite-Horizon Reinforcement Learning with Multinomial Logit Function Approximation
A principled approach for generating adversarial images under non-smooth dissimilarity metrics
Markov Chain Truncation for Doubly-Intractable Inference
Optimal Sketching Bounds for Sparse Linear Regression
On Generalization of Decentralized Learning with Separable Data
Dirichlet Enhanced Latent Semantic Analysis
Learning Pareto-Efficient Decisions with Confidence
Variational inference for nonlinear ordinary differential equations
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
One-bit Compressed Sensing with the k-Support Norm
Early Stopping as Nonparametric Variational Inference
A Unified View of SDP-based Neural Network Verification through Completely Positive Programming
An Analysis of Categorical Distributional Reinforcement Learning
Online Multiclass Boosting with Bandit Feedback
Approximate Global Convergence of Independent Learning in Multi-Agent Systems
Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees
Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning
Learning the Structure of a Nonstationary Vector Autoregression
Sensor Selection for Crowdsensing Dynamical Systems
Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals
A Generalized Theory of Mixup for Structure-Preserving Synthetic Data
SDMTR: A Brain-inspired Transformer for Relation Inference
Towards Flexible Device Participation in Federated Learning
Accelerating Metropolis-Hastings with Lightweight Inference Compilation
Improved Representation Learning Through Tensorized Autoencoders
Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects
Mixed Memory Markov Models
Active Ranking with Subset-wise Preferences
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
Database Alignment with Gaussian Features
Langevin Monte Carlo without smoothness
Norm-Agnostic Linear Bandits
Adversarially Robust Kernel Smoothing
Are we there yet? Manifold identification of gradient-related proximal methods
Stochastic Variance-Reduced Algorithms for PCA with Arbitrary Mini-Batch Sizes
Rotting bandits are no harder than stochastic ones
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization
Online Control of the False Discovery Rate under "Decision Deadlines"
Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices
Pseudo-Marginal Slice Sampling
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI
Graphical Models for Non-Negative Data Using Generalized Score Matching
MDP Geometry, Normalization and Reward Balancing Solvers
Logistic Q-Learning
On the Faster Alternating Least-Squares for CCA
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields
SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication
Signal Recovery from Random Dot-Product Graphs under Local Differential Privacy
DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks
On the Absence of Spurious Local Minima in Nonlinear Low-Rank Matrix Recovery Problems
Generative Oversampling for Imbalanced Data via Majority-Guided VAE
DART: Dropouts meet Multiple Additive Regression Trees
Preserving Privacy of Continuous High-dimensional Data with Minimax Filters
Nested CRP with Hawkes-Gaussian Processes
Consensus Ranking with Signed Permutations
A Fast Sampling Algorithm for Maximum Inner Product Search
Statistical Aspects of Classification in Drifting Populations
TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation
Weight-Sharing Regularization
Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain
Online Learning with Noisy Side Observations
On the proliferation of support vectors in high dimensions
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Recurrent Neural Goodness-of-Fit Test for Time Series
Minimax Model Learning
Minimizing Dynamic Regret and Adaptive Regret Simultaneously
Actually Sparse Variational Gaussian Processes
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
Learning Rate Adaptation for Differentially Private Learning
Towards Fair Graph Learning without Demographic Information
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
Approximating Lipschitz continuous functions with GroupSort neural networks
Near-Polynomially Competitive Active Logistic Regression
Markov Properties of Discrete Determinantal Point Processes
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization
Proximal Sampler with Adaptive Step Size
Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments
On the Sample Complexity of Learning Sum-Product Networks
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
CWY Parametrization: a Solution for Parallelized Optimization of Orthogonal and Stiefel Matrices
Boosted Off-Policy Learning
Local Stochastic Sensitivity Analysis For Dynamical Systems
Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Finding Dynamics Preserving Adversarial Winning Tickets
On the Convergence of Continuous Constrained Optimization for Structure Learning
Bayesian Nonparametric Kernel-Learning
General Tail Bounds for Non-Smooth Stochastic Mirror Descent
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference
Time-series attribution maps with regularized contrastive learning
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data
The Sample Complexity of Stackelberg Games
Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data
The Local Learning Coefficient: A Singularity-Aware Complexity Measure
Uncertainty-aware Unsupervised Video Hashing
Symmetric Iterative Proportional Fitting
Robustness for Non-Parametric Classification: A Generic Attack and Defense
FastMap, MetricMap, and Landmark MDS are all Nystrom Algorithms
A Greedy Approximation for k-Determinantal Point Processes
Dynamic Resource Allocation for Optimizing Population Diffusion
Multiscale Gaussian Process Level Set Estimation
On the estimation of persistence intensity functions and linear representations of persistence diagrams
Breaking a Classical Barrier for Classifying Arbitrary Test Examples in the Quantum Model
Fast Dictionary Learning with a Smoothed Wasserstein Loss
On the Memory Mechanism of Tensor-Power Recurrent Models
Nonparametric Sequential Prediction While Deep Learning the Kernel
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems
From Learning to Optimize to Learning Optimization Algorithms
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network
Linear Queries Estimation with Local Differential Privacy
Comparing Tree-Simplification Procedures
Statistical Optimal Transport via Factored Couplings
Recovery Guarantees For Quadratic Tensors With Sparse Observations
Exploring Image Regions Not Well Encoded by an INN
Scalable Nonparametric Factorization for High-Order Interaction Events
Modeling Conditional Dependencies in Multiagent Trajectories
Global Optimization Methods for Extended Fisher Discriminant Analysis
Neural Decomposition: Functional ANOVA with Variational Autoencoders
Nonparametric Factor Analysis and Beyond
Strategic Conformal Prediction
Differentiable Feature Selection by Discrete Relaxation
An Optimization Approach to Learning Falling Rule Lists
Model folding for data subject to nonresponse
Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss
LP-based Construction of DC Decompositions for Efficient Inference of Markov Random Fields
Variational Tempering
Finite-sum Composition Optimization via Variance Reduced Gradient Descent
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
Low-rank regularization and solution uniqueness in over-parameterized matrix sensing
Binary and Multi-Bit Coding for Stable Random Projections
Pattern discovery via entropy minimization
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Combinatorial Topic Models using Small-Variance Asymptotics
Fair for All: Best-effort Fairness Guarantees for Classification
Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments
Reinforcement Learning for Adaptive MCMC
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Renyi Differentially Private ERM for Smooth Objectives
Predicting Preference Reversals via Gaussian Process Uncertainty Aversion
Uniformly Conservative Exploration in Reinforcement Learning
Doubly Mixed-Effects Gaussian Process Regression
Rank-Based Causal Discovery for Post-Nonlinear Models
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection
Learning Controllable Fair Representations
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks
Mean-Field Microcanonical Gradient Descent
Intelligent Support of Secondary Data Analysis
Inference Compilation and Universal Probabilistic Programming
Regularized Contextual Bandits
Distributionally Robust Bayesian Quadrature Optimization
Hierarchical Clustering of Composite Objects with a Variable Number of Components
Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves
Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks
Low-Precision Random Fourier Features for Memory-constrained Kernel Approximation
Tossing Coins Under Monotonicity
Lifelong Optimization with Low Regret
Towards Optimal Transport with Global Invariances
Designing Transportable Experiments Under S-admissability
Block Stability for MAP Inference
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums
Near-Optimal Sample Complexity in Reward-Free Kernel-based Reinforcement Learning
Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
GLASSES: Relieving The Myopia Of Bayesian Optimisation
Connected Sub-graph Detection
PAC-Bayesian Learning of Optimization Algorithms
Neural Point Processes for Pixel-wise Regression
Approximations with Reweighted Generalized Belief Propagation
Explicit Regularization in Overparametrized Models via Noise Injection
On the Naive Bayes Model for Text Categorization
Adaptive Multi-Goal Exploration
Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models - and a Deep-learning Solution
Distributed Latent Variable Models of Lexical Co-occurrences
Scalable Learning of Item Response Theory Models
SPONGE: A generalized eigenproblem for clustering signed networks
Differentially Private Kernelized Contextual Bandits
Deep learning with differential Gaussian process flows
Learning in Gated Neural Networks
Autoregressive Bandits
Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory
A Bayesian approach to CART
Cause-Effect Inference by Comparing Regression Errors
Differentially Private Monotone Submodular Maximization Under Matroid and Knapsack Constraints
Ultrahigh Dimensional Feature Screening via RKHS Embeddings
Transfer Neyman-Pearson Algorithm for Outlier Detection
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference
Inductive Mutual Information Estimation: A Convex Maximum-Entropy Copula Approach
Wavelet based Random Densities
Efficient Greedy Coordinate Descent for Composite Problems
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
Latent feature regression for multivariate count data
Online Learning for Unknown Partially Observable MDPs
On the Accelerated Noise-Tolerant Power Method
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data
Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks
Probabilistic Calibration by Design for Neural Network Regression
The Sylvester Graphical Lasso (SyGlasso)
Reducing Discretization Error in the Frank-Wolfe Method
Value-Aware Loss Function for Model-based Reinforcement Learning
Multi-scale Nystrom Method
Variational Inference based on Robust Divergences
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
Proximal Splitting Meets Variance Reduction
Faster Coordinate Descent via Adaptive Importance Sampling
Online Relative Entropy Policy Search using Reproducing Kernel Hilbert Space Embeddings
Parameter-Agnostic Optimization under Relaxed Smoothness
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift
Nonparametric Relational Models with Superrectangulation
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks
Active Membership Inference Attack under Local Differential Privacy in Federated Learning
Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems
Online Decentralized Leverage Score Sampling for Streaming Multidimensional Time Series
Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction
Relaxing the local independence assumption for quantitative learning in acyclic directed graphical models through hierarchical partition models
Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistency
Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Interpretable Companions for Black-Box Models
Asymptotics of Ridge(less) Regression under General Source Condition
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms
Probabilities of Causation: Role of Observational Data
A Sea of Words: An In-Depth Analysis of Anchors for Text Data
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
Multivariate Density Factorization for Independent Component Analysis: An Unsupervised Artificial Neural Network Approach
AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning
Omega-Stat: An Environment for Implementing Intelligent Modeling Strategies
On Test Selection Strategies for Belief Networks
Generalization Bounds for Dependent Data using Online-to-Batch Conversion
Random Coordinate Underdamped Langevin Monte Carlo
Estimation of Non-Normalized Mixture Models
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Structured Variational Inference Procedures and their Realizations
k-Pareto Optimality-Based Sorting with Maximization of Choice
Hypernym Bias: Unraveling Deep Classifier Training Dynamics through the Lens of Class Hierarchy
Differentiable Calibration of Inexact Stochastic Simulation Models via Kernel Score Minimization
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation
Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods
Robust Kernel Hypothesis Testing under Data Corruption
Learning to Rank for Optimal Treatment Allocation Under Resource Constraints
Horde of Bandits using Gaussian Markov Random Fields
Adaptive Quasi-Newton and Anderson Acceleration Framework with Explicit Global (Accelerated) Convergence Rates
A Statistical Learning Take on the Concordance Index for Survival Analysis
On the Error Probability of Model Selection for Classification
Using time-series privileged information for provably efficient learning of prediction models
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
Communication-efficient Distributed Sparse Linear Discriminant Analysis
A Learning Theory of Ranking Aggregation
Naive Feature Selection: Sparsity in Naive Bayes
Membership Inference Attacks against Synthetic Data through Overfitting Detection
Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization
A View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
Sample Complexity of Kernel-Based Q-Learning
Robustness and scalability under heavy tails, without strong convexity
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Model-X Sequential Testing for Conditional Independence via Testing by Betting
Adaptive Convergence Rates for Log-Concave Maximum Likelihood
The Sound of an Album Cover: A Probabilistic Approach to Multimedia
Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization
Incentive-aware Contextual Pricing with Non-parametric Market Noise
"Bring Your Own Greedy"+Max: Near-Optimal 1/2-Approximations for Submodular Knapsack
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
Relativistic Monte Carlo
Managing Multiple Models
Legitimate ground-truth-free metrics for deep uncertainty classification scoring
Efficient Bayesian Methods for Counting Processes in Partially Observable Environments
Discrete Langevin Samplers via Wasserstein Gradient Flow
An Homogeneous Unbalanced Regularized Optimal Transport Model with Applications to Optimal Transport with Boundary
Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation
Optimal Transport for Measures with Noisy Tree Metric
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?
Uncertainty Matters: Stable Conclusions under Unstable Assessment of Fairness Results
Variance Reduction for Evolution Strategies via Structured Control Variates
Learning in RKHM: a C*-Algebraic Twist for Kernel Machines
Fast and Robust Shortest Paths on Manifolds Learned from Data
Competing against Adaptive Strategies in Online Learning via Hints
Lifting high-dimensional non-linear models with Gaussian regressors
Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex
Frank-Wolfe Splitting via Augmented Lagrangian Method
Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods
Hypergraph Simultaneous Generators
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Hamiltonian Monte Carlo Swindles
Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions
Annular Augmentation Sampling
SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings
Avoiding Latent Variable Collapse with Generative Skip Models
Nonstochastic Bandits and Experts with Arm-Dependent Delays
Hierarchical IFA Belief Networks
International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain
Fast Distribution To Real Regression
Time-varying Gaussian Process Bandits with Unknown Prior
CONTRA: Contrarian statistics for controlled variable selection
Rethinking Neural-based Matrix Inversion: Why can't, and Where can
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Completion
Black-Box Policy Search with Probabilistic Programs
Product Kernel Interpolation for Scalable Gaussian Processes
State Space Methods for Efficient Inference in Student-t Process Regression
Doubly Semi-Implicit Variational Inference
Do Regularization Methods for Shortcut Mitigation Work As Intended?
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective
Variational inference for the multi-armed contextual bandit
Differentiable Bayesian inference of SDE parameters using a pathwise series expansion of Brownian motion
A New Causal Decomposition Paradigm towards Health Equity
Decentralized Collaborative Learning of Personalized Models over Networks
Distribution Regression for Sequential Data
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization
Online Robust Control of Nonlinear Systems with Large Uncertainty
Global-Local Regularization Via Distributional Robustness
A totally unimodular view of structured sparsity
Simple and scalable algorithms for cluster-aware precision medicine
Bandit optimisation of functions in the Matérn kernel RKHS
Consistently Estimating Markov Chains with Noisy Aggregate Data
Adaptive Extragradient Methods for Root-finding Problems under Relaxed Assumptions
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Joint probabilistic clustering of multivariate and sequential data
I-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers
Optimal Recovery of Tensor Slices
Medoids in Almost-Linear Time via Multi-Armed Bandits