yusuke1997/dblp-id-title huggingface dataset:
Feature Collapsing for Gaussian Process Variable Ranking
Optimal channel selection with discrete QCQP
A constrained risk inequality for general losses
On the Effect of Key Factors in Spurious Correlation: A theoretical Perspective
Confident Feature Ranking
Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
Location Trace Privacy Under Conditional Priors
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
Learning Smooth and Fair Representations
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
Two-sample Testing Using Deep Learning
Combinatorial Semi-Bandits with Knapsacks
EGG-GAE: scalable graph neural networks for tabular data imputation
Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
Scalable Variational Inference for Super Resolution Microscopy
A Bayesian Model for Online Activity Sample Sizes
Rank Aggregation and Prediction with Item Features
Maximally Informative Hierarchical Representations of High-Dimensional Data
Learning to Defer to a Population: A Meta-Learning Approach
A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data
Differentially Private Online Submodular Minimization
Contextual Bandits with Budgeted Information Reveal
Recovering Distributions from Gaussian RKHS Embeddings
Does data interpolation contradict statistical optimality?
Random Subspace with Trees for Feature Selection Under Memory Constraints
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond
Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach
Entropic Risk Optimization in Discounted MDPs
General Identifiability and Achievability for Causal Representation Learning
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback
Data Augmentation for Imbalanced Regression
Computation Efficient Coded Linear Transform
Independent Natural Policy Gradient always converges in Markov Potential Games
Anytime-Constrained Reinforcement Learning
A general sample complexity analysis of vanilla policy gradient
Minimax-Bayes Reinforcement Learning
Pairwise Fairness for Ordinal Regression
On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative
Interpretable Sparse High-Order Boltzmann Machines
Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation
Attributing Hacks
Tractable structure search in the presence of latent variables
Variational Information Planning for Sequential Decision Making
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence
Moment-Based Variational Inference for Stochastic Differential Equations
Iterative Spectral Method for Alternative Clustering
A Complete Characterisation of ReLU-Invariant Distributions
Efficient Inference in Multi-task Cox Process Models
Non-Stationary Off-Policy Optimization
A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference
Spectral Differential Network Analysis for High-Dimensional Time Series
Efficient and principled score estimation with Nyström kernel exponential families
AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity
Conservative Exploration using Interleaving
A Greedy Homotopy Method for Regression with Nonconvex Constraints
Robust Imitation Learning from Noisy Demonstrations
Robust Fair Clustering with Group Membership Uncertainty Sets
Scalable Feature Selection for (Multitask) Gradient Boosted Trees
Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning
Pure Exploration with Feedback Graphs
Improved Semi-Supervised Learning with Multiple Graphs
A Multi-Task Gaussian Process Model for Inferring Time-Varying Treatment Effects in Panel Data
The Effective Number of Shared Dimensions Between Paired Datasets
Anytime-Valid A/B Testing of Counting Processes
Distance Estimation for High-Dimensional Discrete Distributions
Conditionally Tractable Density Estimation using Neural Networks
Structured adaptive and random spinners for fast machine learning computations
Learning to Defend by Learning to Attack
Powerful batch conformal prediction for classification
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Scalable Implicit Graphon Learning
Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory
Post-processing for Fair Regression via Explainable SVD
Approximating the Total Variation Distance between Gaussians
A Unified Evaluation Framework for Epistemic Predictions
Nonparametric Distributional Regression via Quantile Regression
Infinite Width Limits of Self Supervised Neural Networks
Conditional Adjustment in a Markov Equivalence Class
Survey Propagation beyond Constraint Satisfaction Problems
Error Estimation for Random Fourier Features
Finding Overlapping Distributions with MML
Max-Margin Zero-Shot Learning for Multi-class Classification
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization
Fast Convergence of Softmax Policy Mirror Ascent
Fix-A-Step: Semi-supervised Learning From Uncurated Unlabeled Data
SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits
Poisson intensity estimation with reproducing kernels
Stochastic Gradient Descent for Bézier Simplex Representation of Pareto Set in Multi-Objective Optimization
Active Learning for Interactive Visualization
Computing the M Most Probable Modes of a Graphical Model
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data
On Learning Continuous Pairwise Markov Random Fields
Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators
Application of Structured State Space Models to High energy physics with locality sensitive hashing
Online Learning with Continuous Variations: Dynamic Regret and Reductions
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
A Predictive Approach to Bayesian Nonparametric Survival Analysis
Fast 1-Wasserstein distance approximations using greedy strategies
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures
Learning Ising and Potts Models with Latent Variables
Reward-Relevance-Filtered Linear Offline Reinforcement Learning
Finite-Sample Regret Bound for Distributionally Robust Offline Tabular Reinforcement Learning
Foundations of Sequence-to-Sequence Modeling for Time Series
Non-vacuous Generalization Bounds for Adversarial Risk in Stochastic Neural Networks
Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses
PAC-Bayesian Bounds based on the Rényi Divergence
Differentially Private Analysis on Graph Streams
Linear Models are Robust Optimal Under Strategic Behavior
Automated Inference with Adaptive Batches
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits
Crowdsourcing Regression: A Spectral Approach
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits
On PAC-Bayesian reconstruction guarantees for VAEs
Understanding the Effects of Batching in Online Active Learning
Data Driven Threshold and Potential Initialization for Spiking Neural Networks
An Efficient Algorithm for Large Scale Compressive Feature Learning
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
Is interpolation benign for random forest regression?
Cost-Aware Optimal Pairwise Pure Exploration
Minimax Bounds for Structured Prediction Based on Factor Graphs
Truncated Inverse-Lévy Measure Representation of the Beta Process
Towards Principled Feature Selection: Relevancy, Filters and Wrappers
Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions
Learning Adaptive Kernels for Statistical Independence Tests
Choice is what matters after Attention
Recursive Karcher Expectation Estimators And Geometric Law of Large Numbers
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
Planning and Learning in Risk-Aware Restless Multi-Arm Bandits
Covariance Selection over Networks
Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming
Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings
Discriminative Model Selection for Density Models
Characterization of Overlap in Observational Studies
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
An Optimal Algorithm for Stochastic Three-Composite Optimization
Efficient Optimization Algorithms for Linear Adversarial Training
Optimal sampling in unbiased active learning
Training Neural Samplers with Reverse Diffusive KL Divergence
Calibrated Computation-Aware Gaussian Processes
Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis
Error bounds for any regression model using Gaussian processes with gradient information
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences
Dynamic Scaled Sampling for Deterministic Constraints
Searching for Dependencies in Bayesian Classifiers
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
Private Causal Inference
Personalized Convolutional Dictionary Learning of Physiological Time Series
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory
Hyperbolic Manifold Regression
Outcome Assumptions and Duality Theory for Balancing Weights
Submodular Point Processes with Applications to Machine learning
Cubic regularized subspace Newton for non-convex optimization
A comparative study on sampling with replacement vs Poisson sampling in optimal subsampling
Semantic Strengthening of Neuro-Symbolic Learning
Lower Bounds for Time-Varying Kernelized Bandits
Online Optimization : Competing with Dynamic Comparators
Heuristic Greedy Search Algorithms for Latent Variable Models
On Generalization Bounds of a Family of Recurrent Neural Networks
Policy Evaluation and Optimization with Continuous Treatments
Nonparametric Estimation in the Dynamic Bradley-Terry Model
Sharp Analysis of Learning with Discrete Losses
Provable Hierarchy-Based Meta-Reinforcement Learning
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, AISTATS 2009, Clearwater Beach, Florida, USA, April 16-18, 2009
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
Consistent Collective Matrix Completion under Joint Low Rank Structure
Improved Regret Bounds for Projection-free Bandit Convex Optimization
Compression with Exact Error Distribution for Federated Learning
An inclusion optimal algorithm for chain graph structure learning
Analysis of multivariate time series via a hidden graphical model
Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization
Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem
Parametric Bootstrap for Differentially Private Confidence Intervals
Distance-to-Set Priors and Constrained Bayesian Inference
Multimodal Learning with Uncertainty Quantification based on Discounted Belief Fusion
Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers
Transfer Learning for High-dimensional Reduced Rank Time Series Models
AdaGeo: Adaptive Geometric Learning for Optimization and Sampling
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
Double InfoGAN for Contrastive Analysis
Collision Probability Matching Loss for Disentangling Epistemic Uncertainty from Aleatoric Uncertainty
Causal Discovery in the Presence of Missing Data
SADI: Similarity-Aware Diffusion Model-Based Imputation for Incomplete Temporal EHR Data
MML Mixture Modelling of Multi-state, Poisson, vonMises circular and Gaussian Distributions
Predicting with Variables Constructed from Temporal Sequences
Active Pointillistic Pattern Search
Consistent k-Median: Simpler, Better and Robust
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games
POPCORN: Partially Observed Prediction Constrained Reinforcement Learning
Feasible Learning
Hidden Cost of Randomized Smoothing
Enhancing In-context Learning via Linear Probe Calibration
Learning Nonparametric Forest Graphical Models with Prior Information
Classification of High-dimensional Time Series in Spectral Domain Using Explainable Features with Applications to Neuroimaging Data
Approximate ranking from pairwise comparisons
Multiresolution Matrix Compression
Fast maximum a-posteriori inference on Monte Carlo state spaces
Separation-Based Distance Measures for Causal Graphs
Column Subset Selection with Missing Data via Active Sampling
Fast Distributed k-Means with a Small Number of Rounds
Robust learning of inhomogeneous PMMs
A Thompson Sampling Algorithm for Cascading Bandits
Consistent Hierarchical Classification with A Generalized Metric
Bipartite Correlation Clustering: Maximizing Agreements
Bayesian Information Retrieval: Preliminary Evaluation
Robust Variational Autoencoding with Wasserstein Penalty for Novelty Detection
A Generic Approach for Escaping Saddle points
Analyzing the Role of Permutation Invariance in Linear Mode Connectivity
Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm
Fair Soft Clustering
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Online Learning for Traffic Routing under Unknown Preferences
Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems
On the geometry of DAG models with hidden variables
Teacher Improves Learning by Selecting a Training Subset
Computing D-Stationary Points of ρ-Margin Loss SVM
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes
Theory and Algorithm for Batch Distribution Drift Problems
Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension
Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
Direct-Search for a Class of Stochastic Min-Max Problems
One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic Normality and Limitation
Doubly Aggressive Selective Sampling Algorithms for Classification
Explicit Regularization of Stochastic Gradient Methods through Duality
Tuning-Free Generalized Hamiltonian Monte Carlo
Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group
Two-stage sampled learning theory on distributions
P-tensors: a General Framework for Higher Order Message Passing in Subgraph Neural Networks
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation
Agnostic PAC Learning of k-juntas Using L 2 -Polynomial Regression
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
Inference for High-dimensional Exponential Family Graphical Models
A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning
Turing: Composable inference for probabilistic programming
The Risks of Recourse in Binary Classification
A Safe Bayesian Learning Algorithm for Constrained MDPs with Bounded Constraint Violation
LIBRE: Learning Interpretable Boolean Rule Ensembles
Censored Quantile Regression Forest
Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds
Optimizing Millions of Hyperparameters by Implicit Differentiation
Data-driven covariate selection for nonparametric estimation of causal effects
Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints
Improved Learning Complexity in Combinatorial Pure Exploration Bandits
Weak Separation in Mixture Models and Implications for Principal Stratification
Sinkformers: Transformers with Doubly Stochastic Attention
Inadequacy of interval estimates corresponding to variational Bayesian approximations
Weighted Sum of Gaussian Process Latent Variable Models
The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Study
Auditing ML Models for Individual Bias and Unfairness
Metrics for Deep Generative Models
Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL
Piecewise Linear Instrumental Variable Estimation of Causal Influence
Deep Fourier Kernel for Self-Attentive Point Processes
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
Supervised Neighborhoods for Distributed Nonparametric Regression
On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions
Regret, stability & fairness in matching markets with bandit learners
Bootstrapping EM via Power EM and Convergence in the Naive Bayes Model
Ploxoma: Testbed for Uncertain Inference
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit
An Active Approach to Collaborative Filtering
On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark
Dependent randomized rounding for clustering and partition systems with knapsack constraints
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
BLIS-Net: Classifying and Analyzing Signals on Graphs
Constructing a provably adversarially-robust classifier from a high accuracy one
Size of Interventional Markov Equivalence Classes in random DAG models
Online Regression with Partial Information: Generalization and Linear Projection
Random Bayesian networks with bounded indegree
Provably Safe PAC-MDP Exploration Using Analogies
Least Squares Estimation of Weakly Convex Functions
Local SGD: Unified Theory and New Efficient Methods
Safe exploration in reproducing kernel Hilbert spaces
Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy
LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection
On the equivalence of Oja's algorithm and GROUSE
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
Recurrent Neural Networks and Universal Approximation of Bayesian Filters
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
Human Interaction with Recommendation Systems
Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings
Informative Path Planning with Limited Adaptivity
Solving lp-norm regularization with tensor kernels
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
Sublinear Optimal Policy Value Estimation in Contextual Bandits
MaxHedge: Maximizing a Maximum Online
Beyond Discretization: Learning the Optimal Solution Path
A Nonparametric Off-Policy Policy Gradient
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches
Prediction with Finitely many Errors Almost Surely
Convergent tree-reweighted message passing for energy minimization
Differentially Private Matrix Completion through Low-rank Matrix Factorization
Nonlinear Functional Output Regression: A Dictionary Approach
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
Conceptual Clustering with Numeric-and-Nominal Mixed Data - A New Similarity Based System
Learning with risk-averse feedback under potentially heavy tails
Estimating treatment effects from single-arm trials via latent-variable modeling
Global Ground Metric Learning with Applications to scRNA data
Efficient Linear Bandits through Matrix Sketching
Representation Learning on Graphs: A Reinforcement Learning Application
Sketching the Support of a Probability Measure
Vine copula structure learning via Monte Carlo tree search
Probabilistic Semantic Inpainting with Pixel Constrained CNNs
Soft-constrained Schrödinger Bridge: a Stochastic Control Approach
Inference using Probabilistic Concept Trees
Robust Sparse Voting
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits
Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization
Towards a Theoretical Understanding of Hashing-Based Neural Nets
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions
Non-stochastic Best Arm Identification and Hyperparameter Optimization
Proximal Optimal Transport Modeling of Population Dynamics
Conditional Linear Regression for Heterogeneous Covariances
Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nyström Method
Sharp error bounds for imbalanced classification: how many examples in the minority class?
Firebolt: Weak Supervision Under Weaker Assumptions
Provable Safe Reinforcement Learning with Binary Feedback
Information-Theoretic Measures on Lattices for Higher-Order Interactions
Clustering above Exponential Families with Tempered Exponential Measures
Optimal Accounting of Differential Privacy via Characteristic Function
Multiway Spherical Clustering via Degree-Corrected Tensor Block Models
Learning spectral graph segmentation
Fast Near-GRID Gaussian Process Regression
Derivative Free Optimization Via Repeated Classification
Nonparametric Budgeted Stochastic Gradient Descent
Spectral Robustness for Correlation Clustering Reconstruction in Semi-Adversarial Models
Breaking Sticks and Ambiguities with Adaptive Skip-gram
Learned models for continuous planning
Discriminant Analysis on Dissimilarity Data : a New Fast Gaussian like Algorithm
Bayesian Markov Blanket Estimation
Performative Reinforcement Learning with Linear Markov Decision Process
Certifiably Robust Variational Autoencoders
Decentralized Gradient Tracking for Continuous DR-Submodular Maximization
Understanding Generalization in Deep Learning via Tensor Methods
Computational Complexity of Linear Large Margin Classification With Ramp Loss
Utility/Privacy Trade-off through the lens of Optimal Transport
A Distance Metric for Classification Trees
Near-optimal Per-Action Regret Bounds for Sleeping Bandits
Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration
Joint control variate for faster black-box variational inference
Provable Benefits of Task-Specific Prompts for In-context Learning
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach
A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models
Bayesian Image Classification with Deep Convolutional Gaussian Processes
Case-based Probability Factoring in Bayesian Belief Networks
Distributional Off-policy Evaluation with Bellman Residual Minimization
Bayesian Inference in the Presence of Determinism
Deeply-Supervised Nets
Maximum entropy GFlowNets with soft Q-learning
Globally Sparse Probabilistic PCA
Fair Decisions Despite Imperfect Predictions
Modelling Policies in MDPs in Reproducing Kernel Hilbert Space
Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classification
p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets
Dataset Cataloging Metadata for Machine Learning Applications Research
Clustering Context in Off-Policy Evaluation