yusuke1997/dblp-id-title huggingface dataset:
Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games
Offline Policy Evaluation and Optimization Under Confounding
Kernel Single Proxy Control for Deterministic Confounding
Fast Marginal Likelihood Maximisation for Sparse Bayesian Models
Spiked Covariance Estimation from Modulo-Reduced Measurements
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization
Causal Temporal Regime Structure Learning
Unconstrained MAP Inference, Exponentiated Determinantal Point Processes, and Exponential Inapproximability
A Contrastive Approach to Online Change Point Detection
Private Protocols for U-Statistics in the Local Model and Beyond
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
An Anytime Algorithm for Causal Inference
Improved Complexity Bounds in Wasserstein Barycenter Problem
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis
Tree Structured Interpretable Regression
Efficient Estimation of Mutual Information for Strongly Dependent Variables
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models
Entropy Partial Transport with Tree Metrics: Theory and Practice
Wasserstein Style Transfer
A Topic Modeling Approach to Ranking
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization
Best of many worlds: Robust model selection for online supervised learning
Towards Understanding Biased Client Selection in Federated Learning
Efficient learning using constrained sufficient statistics
Consistent Validation for Predictive Methods in Spatial Settings
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Data-Driven Online Model Selection With Regret Guarantees
Flexible risk design using bi-directional dispersion
Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity
Sample Complexity of Sinkhorn Divergences
Best arm identification in multi-armed bandits with delayed feedback
A Study of Condition Numbers for First-Order Optimization
Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator
Efficient Computation and Analysis of Distributional Shapley Values
Goodness-of-Fit Test for Mismatched Self-Exciting Processes
Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency
One Sample Stochastic Frank-Wolfe
On the Nyström Approximation for Preconditioning in Kernel Machines
Information-Theoretic Causal Discovery in Topological Order
Safe Convex Learning under Uncertain Constraints
On the Troll-Trust Model for Edge Sign Prediction in Social Networks
Counterfactual Representation Learning with Balancing Weights
Deep Learning-Based Alternative Route Computation
Auto-Encoding Total Correlation Explanation
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data
An Online and Unified Algorithm for Projection Matrix Vector Multiplication with Application to Empirical Risk Minimization
Bayesian learning of joint distributions of objects
Budgeted and Non-Budgeted Causal Bandits
A Theoretical Characterization of Semi-supervised Learning with Self-training for Gaussian Mixture Models
Differentiating the Value Function by using Convex Duality
Topic Compositional Neural Language Model
On Multilevel Monte Carlo Unbiased Gradient Estimation for Deep Latent Variable Models
Structured Variational Distributions in VIBES
Conjugate Gradient Method for Generative Adversarial Networks
Learning to Optimize with Stochastic Dominance Constraints
Multi-task Representation Learning with Stochastic Linear Bandits
Delegating Data Collection in Decentralized Machine Learning
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry
SemlaFlow - Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching
A Witness Two-Sample Test
Solving Markov Random Fields using Semi Definite Programming
Another look at sensitivity of Bayesian networks to imprecise probabilities
Compress Then Test: Powerful Kernel Testing in Near-linear Time
Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits
Data modeling with the elliptical gamma distribution
A Spectral Algorithm for Inference in Hidden semi-Markov Models
Sparse and Faithful Explanations Without Sparse Models
Graph Spectral Embedding using the Geodesic Betweenness Centrality
Vanishing Curvature in Randomly Initialized Deep ReLU Networks
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models
Data-Driven Approach to Multiple-Source Domain Adaptation
Mixed Membership Word Embeddings for Computational Social Science
On Estimating L22 Divergence
Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE
Distance Covariance Analysis
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows
Plug-in Estimators for Conditional Expectations and Probabilities
M 2 AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding
BlitzMask: Real-Time Instance Segmentation Approach for Mobile Devices
Changepoint Detection over Graphs with the Spectral Scan Statistic
Symmetric Variational Autoencoder and Connections to Adversarial Learning
Parallel Markov Chain Monte Carlo via Spectral Clustering
Learning Causally Linked Markov Random Fields
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization
Stochastic Bandits with Linear Constraints
Average Case Analysis of High-Dimensional Block-Sparse Recovery and Regression for Arbitrary Designs
Consistent Amortized Clustering via Generative Flow Networks
Learning Dynamics in Linear VAE: Posterior Collapse Threshold, Superfluous Latent Space Pitfalls, and Speedup with KL Annealing
Active Exploration via Experiment Design in Markov Chains
Efficient Spectrum-Revealing CUR Matrix Decomposition
Robust Deep Learning from Crowds with Belief Propagation
On the Testability of Models with Missing Data
Koopman-Equivariant Gaussian Processes
Meta-Learning with Adjoint Methods
Variational Gaussian Process Diffusion Processes
Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty
Learning Under Random Distributional Shifts
Offline detection of change-points in the mean for stationary graph signals
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
On Global-view Based Defense via Adversarial Attack and Defense Risk Guaranteed Bounds
Benefits from Superposed Hawkes Processes
Variational Noise-Contrastive Estimation
Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo
A Fast and Robust Method for Global Topological Functional Optimization
Probabilistic Sequential Matrix Factorization
Noise-Aware Statistical Inference with Differentially Private Synthetic Data
Optimism and Delays in Episodic Reinforcement Learning
A Convex Surrogate Operator for General Non-Modular Loss Functions
EM Converges for a Mixture of Many Linear Regressions
A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration
Subsampled Renyi Differential Privacy and Analytical Moments Accountant
Finding a path is harder than finding a tree
Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding
Good Classifiers are Abundant in the Interpolating Regime
The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?
Conditional Restricted Boltzmann Machines for Multi-label Learning with Incomplete Labels
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search
Can 5th Generation Local Training Methods Support Client Sampling? Yes!
Deep Multi-Fidelity Active Learning of High-Dimensional Outputs
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
Two Algorithms for Inducing Structural Equation Models from Data
Encrypted Linear Contextual Bandit
On Distributional Discrepancy for Experimental Design with General Assignment Probabilities
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies
Lower-level Duality Based Reformulation and Majorization Minimization Algorithm for Hyperparameter Optimization
Minimax Estimation of Laplacian Constrained Precision Matrices
Regularized Autoencoders via Relaxed Injective Probability Flow
An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
On the Generalization Ability of Unsupervised Pretraining
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
Structured Factored Inference for Probabilistic Programming
Tractable contextual bandits beyond realizability
XBART: Accelerated Bayesian Additive Regression Trees
Consistent Online Optimization: Convex and Submodular
Evidence Estimation for Bayesian Partially Observed MRFs
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
Sparse Activations as Conformal Predictors
MLDemon: Deployment Monitoring for Machine Learning Systems
Nonparametric Gaussian Process Covariances via Multidimensional Convolutions
Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles
Active Learning for Parzen Window Classifier
Symmetric (Optimistic) Natural Policy Gradient for Multi-Agent Learning with Parameter Convergence
Implementable confidence sets in high dimensional regression
Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning
Improved dependence on coherence in eigenvector and eigenvalue estimation error bounds
Bayesian Online Learning for Consensus Prediction
Pure Exploration in Bandits with Linear Constraints
Inference in Sparse Graphs with Pairwise Measurements and Side Information
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives
Synthesis and Analysis of Data as Probability Measures With Entropy-Regularized Optimal Transport
Transfer Learning on fMRI Datasets
Lower Bounds on Active Learning for Graphical Model Selection
Factorial SDE for Multi-Output Gaussian Process Regression
Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias
Active Area Search via Bayesian Quadrature
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels
Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs
Dual Decomposition for Joint Discrete-Continuous Optimization
Aligned Multi-Task Gaussian Process
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Factorial HMMs with Collapsed Gibbs Sampling for Optimizing Long-term HIV Therapy
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks
Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with Ordered L1-Norm
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
ASAP: Architecture Search, Anneal and Prune
Multi-view Metric Learning in Vector-valued Kernel Spaces
Efficient Gradient Computation for Conditional Gaussian Models
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances
Local Saddle Point Optimization: A Curvature Exploitation Approach
On Truly Block Eigensolvers via Riemannian Optimization
Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
Learning Bijective Feature Maps for Linear ICA
Ivy: Instrumental Variable Synthesis for Causal Inference
Model updating after interventions paradoxically introduces bias
Deontological Ethics By Monotonicity Shape Constraints
Geometric modeling of a nuclear environment
Better Batch for Deep Probabilistic Time Series Forecasting
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Scalable Learning of Non-Decomposable Objectives
Fitting Spectral Decay with the k-Support Norm
Bilevel Reinforcement Learning via the Development of Hyper-gradient without Lower-Level Convexity
Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models
Precision/Recall on Imbalanced Test Data
Score-based Quickest Change Detection for Unnormalized Models
Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise
Reactive bandits with attitude
Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks
The Strong Product Model for Network Inference without Independence Assumptions
Scalable geometric density estimation
Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity
Conformalized Unconditional Quantile Regression
Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation
Privacy in Metalearning and Multitask Learning: Modeling and Separations
Permutation Equivariant Layers for Higher Order Interactions
Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning
Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection
Infinite-dimensional Diffusion Bridge Simulation via Operator Learning
Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
Algorithm for Constrained Markov Decision Process with Linear Convergence
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning
Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models
Learning from Conditional Distributions via Dual Embeddings
On Maximization of Weakly Modular Functions: Guarantees of Multi-stage Algorithms, Tractability, and Hardness
Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls
Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition
Distributed and Adaptive Darting Monte Carlo through Regenerations
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
Neighborhood Growth Determines Geometric Priors for Relational Representation Learning
Feedback Coding for Active Learning
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit
Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge
Learning Physics-Informed Neural Networks without Stacked Back-propagation
Thresholded linear bandits
Hyperbolic Prototypical Entailment Cones for Image Classification
Catalyst for Gradient-based Nonconvex Optimization
Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression
Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approach
Multi-Observation Regression
Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games
Sample Complexity of Robust Reinforcement Learning with a Generative Model
Understanding and Evaluating Sparse Linear Discriminant Analysis
Entropy Weighted Power k-Means Clustering
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
Finding First-Order Nash Equilibria of Zero-Sum Games with the Regularized Nikaido-Isoda Function
Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates
Task Shift: From Classification to Regression in Overparameterized Linear Models
Ideal Abstractions for Decision-Focused Learning
Conservative Exploration in Reinforcement Learning
Refining Kernels for Regression and Uneven Classification Problems
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
Online Learning Using Only Peer Prediction
The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy]
Fast Function to Function Regression
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation
Universal Agent Mixtures and the Geometry of Intelligence
Dimensionality Reduced $\ell^{0}$-Sparse Subspace Clustering
Implicitly Regularized RL with Implicit Q-values
An Online Bootstrap for Time Series
Pick-and-Mix Information Operators for Probabilistic ODE Solvers
Bayesian Strategy-Proof Facility Location via Robust Estimation
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates
Moment Matching Deep Contrastive Latent Variable Models
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
Large Covariance Matrix Estimation With Nonnegative Correlations
Temporal Quilting for Survival Analysis
The sample complexity of ERMs in stochastic convex optimization
Hypothesis Testing Interpretations and Renyi Differential Privacy
A 4-Approximation Algorithm for Min Max Correlation Clustering
Vector Quantized Time Series Generation with a Bidirectional Prior Model
Efficient Online Multiclass Prediction on Graphs via Surrogate Losses
A Framework for Sample Efficient Interval Estimation with Control Variates
Vector Quantile Regression on Manifolds
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization
Online k-means Clustering
A Manifold View of Adversarial Risk
Gaussian Processes on Distributions based on Regularized Optimal Transport
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning
Importance Sampling via Local Sensitivity
Efficient Bregman Projections onto the Permutahedron and Related Polytopes
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
Learning Efficient Anomaly Detectors from K-NN Graphs
Learning Structured Weight Uncertainty in Bayesian Neural Networks
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
A graphical global optimization framework for parameter estimation of statistical models with nonconvex regularization functions
Bayesian Optimization with Conformal Prediction Sets
Mode-constrained Model-based Reinforcement Learning via Gaussian Processes
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
Tree-based Bayesian Mixture Model for Competing Risks
RelatIF: Identifying Explanatory Training Samples via Relative Influence
Inverting Supervised Representations with Autoregressive Neural Density Models
How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem
Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data
Spatio-temporal alignments: Optimal transport through space and time
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
Automatic Differentiation of Sketched Regression
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
Exponential Stochastic Cellular Automata for Massively Parallel Inference
Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable
Fair k-center Clustering with Outliers
Kernel Conditional Density Operators
Retrospective Uncertainties for Deep Models using Vine Copulas
A new evaluation framework for topic modeling algorithms based on synthetic corpora
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation
Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity
A Level-set Hit-and-run Sampler for Quasi-Concave Distributions
Sketching for Latent Dirichlet-Categorical Models
On the Interplay between Information Loss and Operation Loss in Representations for Classification
Differentially Private Online Submodular Maximization
Distributional reinforcement learning with linear function approximation
On Theoretical Properties of Sum-Product Networks
Improved Algorithms for Misspecified Linear Markov Decision Processes
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization
Document Retrieval and Clustering: from Principal Component Analysis to Self-aggregation Networks
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning
Large-Scale Gaussian Processes via Alternating Projection
On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation
Safe Optimal Design with Applications in Off-Policy Learning
On double-descent in uncertainty quantification in overparametrized models
Sparse Hilbert-Schmidt Independence Criterion Regression
Learning Contact Dynamics using Physically Structured Neural Networks
Asymptotically Optimal Locally Private Heavy Hitters via Parameterized Sketches
A New Approach to Probabilistic Programming Inference
Adaptive Estimation for Approximate k -Nearest-Neighbor Computations
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods
Learning Relationships between Data Obtained Independently
On a Connection Between Fast and Sparse Oblivious Subspace Embeddings
Pan-sharpening with a Bayesian nonparametric dictionary learning model
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models
Data Reconstruction Attacks and Defenses: A Systematic Evaluation
Consensus Message Passing for Layered Graphical Models
Diagonal Orthant Multinomial Probit Models
Type Information-Assisted Self-Supervised Knowledge Graph Denoising
The emergence of spectral universality in deep networks
Momentum Improves Optimization on Riemannian Manifolds
Privacy Amplification by Subsampling in Time Domain
Thresholding Graph Bandits with GrAPL
Approximate Cross-Validation in High Dimensions with Guarantees
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains
Low-Rank Spectral Learning with Weighted Loss Functions
Spectral Augmentations for Graph Contrastive Learning
Mixed-Feature Logistic Regression Robust to Distribution Shifts
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Fairness Evaluation in Presence of Biased Noisy Labels
Robust Probabilistic Time Series Forecasting
A recurrent Markov state-space generative model for sequences
Empirical Error Estimates for Graph Sparsification
Accelerated Bayesian Optimisation through Weight-Prior Tuning