yusuke1997/dblp-id-title huggingface dataset:
Robust and Private Learning of Halfspaces
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain
Sampling from the Random Linear Model via Stochastic Localization Up to the AMP Threshold
Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
Inductive Inference of First-Order Models from Numeric-Symbolic Data
Frequentist Uncertainty Quantification in Semi-Structured Neural Networks
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
A Novel Convex Gaussian Min Max Theorem for Repeated Features
False Discovery Rates in Biological Networks
Regression Discontinuity Design under Self-selection
To Give or Not to Give? The Impacts of Strategically Withheld Recourse
Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms
Projection Free Online Learning over Smooth Sets
Optimal partition recovery in general graphs
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation
Exact Community Recovery over Signed Graphs
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
Active Learning with Maximum Margin Sparse Gaussian Processes
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Provable Lifelong Learning of Representations
Efficient Low-Dimensional Compression of Overparameterized Models
Influence Decompositions For Neural Network Attribution
Improving Attribution Methods by Learning Submodular Functions
The effect of Leaky ReLUs on the training and generalization of overparameterized networks
Online Nonnegative Matrix Factorization with General Divergences
Minimax Rank-$1$ Matrix Factorization
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization
Positivity-free Policy Learning with Observational Data
Probabilistic Soft Interventions in Conditional Gaussian Networks
Corruption-Robust Offline Two-Player Zero-Sum Markov Games
Multi-Resolution Active Learning of Fourier Neural Operators
A Bayesian nonparametric approach to count-min sketch under power-law data streams
Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization
Collaborative Classification from Noisy Labels
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Learning to Optimize under Non-Stationarity
Stochastic Neural Networks with Monotonic Activation Functions
TRADE: Transfer of Distributions between External Conditions with Normalizing Flows
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions
Change Point Detection in Hadamard Spaces by Alternating Minimization
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks
An Optimal Control Approach to Sequential Machine Teaching
Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process
Equivariance Discovery by Learned Parameter-Sharing
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players
Interpretable Deep Gaussian Processes with Moments
Random Forest for the Contextual Bandit Problem
Efficient Neural Architecture Design via Capturing Architecture-Performance Joint Distribution
Understanding Gradient Clipping In Incremental Gradient Methods
Density Ratio Estimation and Neyman Pearson Classification with Missing Data
Large-Margin Classification in Hyperbolic Space
Minimax Approach to Variable Fidelity Data Interpolation
Black-Box Inference for Non-Linear Latent Force Models
A nonasymptotic law of iterated logarithm for general M-estimators
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking
Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations
Adaptive Compression in Federated Learning via Side Information
Optimal Budgeted Rejection Sampling for Generative Models
Co-Regularized Adversarial Learning for Multi-Domain Text Classification
An Unpooling Layer for Graph Generation
Removing Phase Transitions from Gibbs Measures
Meta Learning MDPs with linear transition models
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
Trustworthy assessment of heterogeneous treatment effect estimator via analysis of relative error
Robust Linear Discriminant Trees
An Analytic Solution to Covariance Propagation in Neural Networks
Faithful Heteroscedastic Regression with Neural Networks
Clustering Markov States into Equivalence Classes using SVD and Heuristic Search Algorithms
Gaussianization Flows
Exploiting Strategy-Space Diversity for Batch Bayesian Optimization
Randomized Stochastic Gradient Descent Ascent
DiffRed: Dimensionality reduction guided by stable rank
Mitigating Bias in Calibration Error Estimation
Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis
Adaptive, Distribution-Free Prediction Intervals for Deep Networks
Optimal Compression of Locally Differentially Private Mechanisms
Mode-Seeking Divergences: Theory and Applications to GANs
Structured Robust Submodular Maximization: Offline and Online Algorithms
Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff
A Double Residual Compression Algorithm for Efficient Distributed Learning
Robust Vertex Enumeration for Convex Hulls in High Dimensions
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures
Matrix-normal models for fMRI analysis
Geometric Random Walk Graph Neural Networks via Implicit Layers
Conformal Off-Policy Prediction
A Robust Univariate Mean Estimator is All You Need
The Hardness of Validating Observational Studies with Experimental Data
Orbital MCMC
Improved Approximation Algorithms for Individually Fair Clustering
Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples
Particle algorithms for maximum likelihood training of latent variable models
Kernel Conditional Exponential Family
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models
Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Robust sketching for multiple square-root LASSO problems
Alternating Projected SGD for Equality-constrained Bilevel Optimization
Learning Time Series Detection Models from Temporally Imprecise Labels
Sample Complexity of Policy-Based Methods under Off-Policy Sampling and Linear Function Approximation
Gibbs-Based Information Criteria and the Over-Parameterized Regime
A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative Models
Probing Graph Representations
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality
Competing Bandits in Matching Markets
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency
Budget Learning via Bracketing
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, AISTATS 1997, Fort Lauderdale, Florida, USA, January, 4-7, 1997
Scalable Collaborative Bayesian Preference Learning
Learning Sparse Graphon Mean Field Games
Marginalized Operators for Off-policy Reinforcement Learning
Kernel Methods for Missing Variables
Adaptive Tuning for Metropolis Adjusted Langevin Trajectories
Models That Are Interpretable But Not Transparent
Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits
Active Learning under Label Shift
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport
Active Learning and Experimental Design workshop, In conjunction with AISTATS 2010, Sardinia, Italy, May 16, 2010
Graph Alignment Kernels using Weisfeiler and Leman Hierarchies
Copula Based Trainable Calibration Error Estimator of Multi-Label Classification with Label Interdependencies
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?
Mean Nyström Embeddings for Adaptive Compressive Learning
Conjugate-Computation Variational Inference: Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models
Sparse Multivariate Bernoulli Processes in High Dimensions
Learning with Feature Feedback: from Theory to Practice
A Simple Homotopy Algorithm for Compressive Sensing
Relational Neural Markov Random Fields
On denoising modulo 1 samples of a function
Variational Gaussian Processes: A Functional Analysis View
Towards Practical Non-Adversarial Distribution Matching
Optimizing Neural Network Training and Quantization with Rooted Logistic Objectives
Asynchronous Distributed Optimization with Stochastic Delays
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps
Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure
Sparse Spectral Bayesian Permanental Process with Generalized Kernel
Learning Bayesian Network Models from Incomplete Data using Importance Sampling
Adaptive A/B Test on Networks with Cluster Structures
Fundamental computational limits of weak learnability in high-dimensional multi-index models
A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case
On Target Shift in Adversarial Domain Adaptation
Sharp Asymptotics and Optimal Performance for Inference in Binary Models
Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures
No-Regret Algorithms for Private Gaussian Process Bandit Optimization
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
Decontamination of Mutually Contaminated Models
Bayesian Approaches to Distribution Regression
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
Optimal Deterministic Coresets for Ridge Regression
Identifying Groups of Strongly Correlated Variables through Smoothed Ordered Weighted L 1 -norms
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Near-Optimal Convex Simple Bilevel Optimization with a Bisection Method
Random Projections for Support Vector Machines
Is regularization unnecessary for boosting?
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
Learning with Gradient Descent and Weakly Convex Losses
Benchmarking Simulation-Based Inference
Restricted Isometry Property of Rank-One Measurements with Random Unit-Modulus Vectors
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
Ensemble Coupled Hidden Markov Models for Joint Characterisation of Dynamic Signals
On the Identifiability of Causal Abstractions
Approximate Regions of Attraction in Learning with Decision-Dependent Distributions
Co-Occurring Directions Sketching for Approximate Matrix Multiply
On the Statistical Efficiency of Compositional Nonparametric Prediction
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA
Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding
Active Positive Semidefinite Matrix Completion: Algorithms, Theory and Applications
Adjusting for Multiple Testing in Decision Tree Pruning
Primal-Dual Spectral Representation for Off-policy Evaluation
PAC Learning of Quantum Measurement Classes : Sample Complexity Bounds and Universal Consistency
Bandit algorithms: Letting go of logarithmic regret for statistical robustness
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate
Learning Deep Sigmoid Belief Networks with Data Augmentation
Implicit Graphon Neural Representation
Online Learning of Decision Trees with Thompson Sampling
Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring
Mixed Graphical Models via Exponential Families
Bias-Variance Decompositions for Margin Losses
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods
Accelerating Smooth Games by Manipulating Spectral Shapes
Likelihood-based Causal Inference
Time-Varying Gaussian Process Bandit Optimization
Synthetic Potential Outcomes and Causal Mixture Identifiability
Byzantine-Robust Online and Offline Distributed Reinforcement Learning
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness
Online probabilistic label trees
Scalable spectral representations for multiagent reinforcement learning in network MDPs
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: a regret lower bound for WSU-UX
Modeling decision tree performance with the power law
Learning Cartesian Product Graphs with Laplacian Constraints
A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows
RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization
INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Approximate Control for Continuous-Time POMDPs
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes
BOBA: Byzantine-Robust Federated Learning with Label Skewness
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
Sample Complexity Characterization for Linear Contextual MDPs
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
IHT dies hard: Provable accelerated Iterative Hard Thresholding
A Characterization of Bayesian Network Structures and its Application to Leaming
Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraints
Subsampling for Ridge Regression via Regularized Volume Sampling
Computing Tight Differential Privacy Guarantees Using FFT
Causal Representation Learning from General Environments under Nonparametric Mixing
Optimal Noise-Adding Mechanism in Additive Differential Privacy
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Towards Federated Bayesian Network Structure Learning with Continuous Optimization
Revisiting the Noise Model of Stochastic Gradient Descent
Neural Laplace Control for Continuous-time Delayed Systems
Competing AI: How does competition feedback affect machine learning?
Sparse Orthogonal Variational Inference for Gaussian Processes
Generalized Spectral Clustering via Gromov-Wasserstein Learning
Test without Trust: Optimal Locally Private Distribution Testing
Efficiently Enforcing Diversity in Multi-Output Structured Prediction
Coreset Markov chain Monte Carlo
Optimal query complexity for private sequential learning against eavesdropping
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Adaptive Federated Minimax Optimization with Lower Complexities
Decoupling Local and Global Representations of Time Series
Optimizing Percentile Criterion using Robust MDPs
On Structure Priors for Learning Bayesian Networks
Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures
Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks
Convergence Rate of Stochastic k-means
Model choice: A minimum posterior predictive loss approach
Detecting Activations over Graphs using Spanning Tree Wavelet Bases
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
Non-stationary Online Learning with Memory and Non-stochastic Control
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning
Visualizing token importance for black-box language models
Support recovery and sup-norm convergence rates for sparse pivotal estimation
Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms
Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation
ForestPrune: Compact Depth-Pruned Tree Ensembles
A Variational Approach to Bayesian Logistic Regression Models and their Extensions
Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality
Optimal Stochastic Trace Estimation in Generative Modeling
Conditional Generative Learning from Invariant Representations in Multi-Source: Robustness and Efficiency
On Lloyd's Algorithm: New Theoretical Insights for Clustering in Practice
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis
Meta-learning from Heterogeneous Tensors for Few-shot Tensor Completion
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model
Differentially Private Densest Subgraph
Predicting the impact of treatments over time with uncertainty aware neural differential equations
Non-Linear Dimensionality Reduction: A Comparative Performance Analysis
On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry
Information-Theoretic Characterization of Sparse Recovery
Graph Gamma Process Linear Dynamical Systems
Prediction Focused Topic Models via Feature Selection
Asessing and Improving Classification Rules
The Power Mean Laplacian for Multilayer Graph Clustering
The ALℓ 0 CORE Tensor Decomposition for Sparse Count Data
Greedy Bilateral Sketch, Completion & Smoothing
Optimal Algorithms for Multiplayer Multi-Armed Bandits
Bayesian Switching Interaction Analysis Under Uncertainty
Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit
Submodularity on Hypergraphs: From Sets to Sequences
Bethe Bounds and Approximating the Global Optimum
Efficient Quantum Agnostic Improper Learning of Decision Trees
Uncertainty Quantification for Sparse Deep Learning
A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage
Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios
Model-Based Best Arm Identification for Decreasing Bandits
Solving Hidden-Mode Markov Decision Problems
Distributed Multi-Task Learning
Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization
Diverse Neural Network Learns True Target Functions
A Robust Zero-Sum Game Framework for Pool-based Active Learning
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation
Exploring k out of Top $ρ$ Fraction of Arms in Stochastic Bandits
UNHaP: Unmixing Noise from Hawkes Processes
Bandit Pareto Set Identification in a Multi-Output Linear Model
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games
Can the Computer Learn to Play Music Expressively?
Persistence Enhanced Graph Neural Network
Taming False Positives in Out-of-Distribution Detection with Human Feedback
Adversarial De-confounding in Individualised Treatment Effects Estimation
Distributed Sparse Multicategory Discriminant Analysis
Stochastic Spectral Descent for Restricted Boltzmann Machines
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes
Improved Exploration in Factored Average-Reward MDPs
Causal Modeling with Stationary Diffusions
Towards Clustering High-dimensional Gaussian Mixture Clouds in Linear Running Time
Framework for a Generic Knowledge Discovery Toolkit
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
Approximate information maximization for bandit games
Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models
Structured Transforms Across Spaces with Cost-Regularized Optimal Transport
Classifier Calibration with ROC-Regularized Isotonic Regression
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
β-th order Acyclicity Derivatives for DAG Learning
Efficient Sampling for k-Determinantal Point Processes
Asymptotic Characterisation of the Performance of Robust Linear Regression in the Presence of Outliers
FLIPHAT: Joint Differential Privacy for High Dimensional Linear Bandits
Stochastic Tree Ensembles for Estimating Heterogeneous Effects
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon
Context-Specific Causal Discovery for Categorical Data Using Staged Trees
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses
Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition
Optimal Approximation of Doubly Stochastic Matrices
Online Distribution Learning with Local Privacy Constraints
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes
Sequential Multivariate Change Detection with Calibrated and Memoryless False Detection Rates
Linear Convergence of Gradient Descent For Finite Width Over-parametrized Linear Networks With General Initialization
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach
DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification
Learning Visual-Semantic Subspace Representations
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
Gaussian Copula Precision Estimation with Missing Values
Optimal estimation of Gaussian (poly)trees
Axiomatic Explainer Globalness via Optimal Transport
Transport Elliptical Slice Sampling
Linear predictor on linearly-generated data with missing values: non consistency and solutions
Information-theoretic limits of Bayesian network structure learning
Independent Learning in Performative Markov Potential Games
Multi-Manifold Modeling in Non-Euclidean spaces
Offline Multi-task Transfer RL with Representational Penalization
Convergence of Value Aggregation for Imitation Learning
Fast Statistical Leverage Score Approximation in Kernel Ridge Regression
Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates
A Faster Sampler for Discrete Determinantal Point Processes
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation
Unified Transfer Learning in High-Dimensional Linear Regression
Efficient Bayesian Optimization for Target Vector Estimation
Trading off Rewards and Errors in Multi-Armed Bandits
Self-training Converts Weak Learners to Strong Learners in Mixture Models
No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution
Sequential Graph Matching with Sequential Monte Carlo
Heavy Sets with Applications to Interpretable Machine Learning Diagnostics
Efficient Inference for Complex Queries on Complex Distributions
SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph
Posterior Mean Matching: Generative Modeling through Online Bayesian Inference
Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time
Efficient and passive learning of networked dynamical systems driven by non-white exogenous inputs
Strategic ranking