yusuke1997/dblp-id-title huggingface dataset:
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Representer Theorems for Metric and Preference Learning: Geometric Insights and Algorithms
Online Optimization of Smoothed Piecewise Constant Functions
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
Sketch-and-Project Meets Newton Method: Global O(1/k 2) Convergence with Low-Rank Updates
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning
Hodge-Compositional Edge Gaussian Processes
High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
Differential Privacy in Distributed Learning: Beyond Uniformly Bounded Stochastic Gradients
A Stochastic Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
Multi-Level Cause-Effect Systems
Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams
Think Global, Adapt Local: Learning Locally Adaptive K-Nearest Neighbor Kernel Density Estimators
Avoiding pathologies in very deep networks
From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach
Learning in high dimensions: modular mixture models
Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go?
Integrating Signal and Language Context to Improve Handwritten Phrase Recognition: Alternative Approaches
Generalized belief propagation for approximate inference in hybrid Bayesian networks
A Forward Monte Carlo Method for Solving Influence Diagrams using local Computation
Learning in Hybrid Noise Environments Using Statistical Queries
A Targeted Accuracy Diagnostic for Variational Approximations
Dual perturb and combine algorithm
A Bayesian Approach to Bergman's Minimal Model
Rao-Blackwellised parallel MCMC
Fully Gap-Dependent Bounds for Multinomial Logit Bandit
Partial Information Decomposition for Data Interpretability and Feature Selection
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation
Binary Space Partitioning Forest
An experiment in causal discovery using a pneumona database
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification
Rethinking Initialization of the Sinkhorn Algorithm
Gamma Processes, Stick-Breaking, and Variational Inference
Adaptive and non-adaptive minimax rates for weighted Laplacian-Eigenmap based nonparametric regression
Group invariance principles for causal generative models
Offline Policy Selection under Uncertainty
Fairness Constraints: Mechanisms for Fair Classification
A Random Walks View of Spectral Segmentation
Surrogate Bayesian Networks for Approximating Evolutionary Games
Randomized Iterative Solver as Iterative Refinement: A Simple Fix Towards Backward Stability
Rk-means: Fast Clustering for Relational Data
Multi-marginal Schrödinger Bridges with Iterative Reference Refinement
Federated Functional Gradient Boosting
A Stein-Papangelou Goodness-of-Fit Test for Point Processes
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms
Faster Convergence with MultiWay Preferences
Risk-aware linear bandits with convex loss
Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
Calibrating Deep Convolutional Gaussian Processes
Monitoring machine learning-based risk prediction algorithms in the presence of performativity
Dual-Level Adaptive Information Filtering for Interactive Image Segmentation
Submodular Minimax Optimization: Finding Effective Sets
Adapting to Latent Subgroup Shifts via Concepts and Proxies
An Optimal Reduction of TV-Denoising to Adaptive Online Learning
Models for Conditional Probability Tables in Educational Assessment
Interpretability Guarantees with Merlin-Arthur Classifiers
Local Group Invariant Representations via Orbit Embeddings
Gauged Mini-Bucket Elimination for Approximate Inference
Uncertainty Quantification for Bayesian Optimization
Efficient Variational Sequential Information Control
Robust Linear Regression for General Feature Distribution
Improved Strongly Adaptive Online Learning using Coin Betting
Correcting the bias in least squares regression with volume-rescaled sampling
Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning
Learning Structural Weight Uncertainty for Sequential Decision-Making
Causally motivated shortcut removal using auxiliary labels
Conditional Importance Sampling for Off-Policy Learning
On the Consistency of Max-Margin Losses
Monotonic Gaussian Process Flows
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression
Solving Influence Diagrams Using Gibbs Sampling
Randomized geometric tools for anomaly detection in stock markets
Pricing against a Budget and ROI Constrained Buyer
Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection
Private optimization without constraint violations
Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis
How Good is a Single Basin?
Optimal Exploration is no harder than Thompson Sampling
Faster Unbalanced Optimal Transport: Translation invariant Sinkhorn and 1-D Frank-Wolfe
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
An Alternate Policy Gradient Estimator for Softmax Policies
A maximum-mean-discrepancy goodness-of-fit test for censored data
A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games
Distribution-Aware Mean Estimation under User-level Local Differential Privacy
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives
On Facility Location Problem in the Local Differential Privacy Model
A Unified Perspective on Regularization and Perturbation in Differentiable Subset Selection
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers
Variational Learning for Multi-Layer Networks of Linear Threshold Units
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics
Online Learning in Kernelized Markov Decision Processes
SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity
Monte-Carlo Algorithms for the Improvement of Finite-State Stochastic Controllers: Application to Bayes-Adaptive Markov Decision Processes
Quadric Hypersurface Intersection for Manifold Learning in Feature Space
On Combining Bags to Better Learn from Label Proportions
Complexities in Projection-Free Stochastic Non-convex Minimization
Analysis of Using Sigmoid Loss for Contrastive Learning
LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads
Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods
Categorical Generative Model Evaluation via Synthetic Distribution Coarsening
Estimating the Partition Function of Graphical Models Using Langevin Importance Sampling
On the Generalization Properties of Adversarial Training
$β^3$-IRT: A New Item Response Model and its Applications
Learning Determinantal Point Processes by Corrective Negative Sampling
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Iterative Teaching by Data Hallucination
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs
Leveraging Time Irreversibility with Order-Contrastive Pre-training
An object-oriented Bayesian network for estimating mutation rates
Precision-Recall Curves Using Information Divergence Frontiers
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates
One-shot Coresets: The Case of k-Clustering
Conditioning diffusion models by explicit forward-backward bridging
Robustness and Reliability When Training With Noisy Labels
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
Misspecification in Prediction Problems and Robustness via Improper Learning
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence
Balls-and-Bins Sampling for DP-SGD
Conditional Prediction ROC Bands for Graph Classification
Expressiveness and Learning of Hidden Quantum Markov Models
Robust Maximization of Non-Submodular Objectives
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Equivalence Testing: The Power of Bounded Adaptivity
An Iterative Algorithm for Rescaled Hyperbolic Functions Regression
Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators
Kantorovich Mechanism for Pufferfish Privacy
Risk-Averse Stochastic Convex Bandit
Variational Sequential Monte Carlo
Robust Stackelberg buyers in repeated auctions
Oblivious near-optimal sampling for multidimensional signals with Fourier constraints
Online Ranking with Top-1 Feedback
Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process
Deep Layer-wise Networks Have Closed-Form Weights
Stein Variational Inference for Discrete Distributions
Bayesian Hybrid Matrix Factorisation for Data Integration
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
A Hill-Climbing Approach for Optimizing Classification Trees
DYNOTEARS: Structure Learning from Time-Series Data
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks
Delayed Feedback in Generalised Linear Bandits Revisited
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers
Generating Efficient MCMC Kernels from Probabilistic Programs
Majorization-Minimization for Manifold Embedding
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent
Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits
Huber-robust confidence sequences
Curriculum Learning by Optimizing Learning Dynamics
Parameter Estimation of Generalized Linear Models without Assuming their Link Function
Testing Conditional Independence on Discrete Data using Stochastic Complexity
Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
Exploration-Exploitation in MDPs with Options
On the Consistency of Metric and Non-Metric K-Medoids
On the optimality of kernels for high-dimensional clustering
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints
Interactive Learning with Pricing for Optimal and Stable Allocations in Markets
Bagging and the Bayesian Bootstrap
Multi-Agent congestion cost minimization with linear function approximations
Protecting Global Properties of Datasets with Distribution Privacy Mechanisms
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach
Vector Optimization with Stochastic Bandit Feedback
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
A simple criterion for controlling selection bias
Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
Efficient SAGE Estimation via Causal Structure Learning
A Competitive Test for Uniformity of Monotone Distributions
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning
Acceleration in Distributed Optimization under Similarity
Feature Selection for Linear SVM with Provable Guarantees
Multi-armed Bandit Algorithm against Strategic Replication
Forward Looking Best-Response Multiplicative Weights Update Methods for Bilinear Zero-sum Games
Thompson Sampling in Switching Environments with Bayesian Online Change Detection
Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear Activations
Markov chain Monte Carlo methods for decision analysis
Pareto Front Identification from Stochastic Bandit Feedback
Approximate Slice Sampling for Bayesian Posterior Inference
Iterative regularization for convex regularizers
Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)
Locally Private Estimation with Public Features
Revisiting the Landscape of Matrix Factorization
Consistent and Efficient Nonparametric Different-Feature Selection
Causal Discovery-Driven Change Point Detection in Time Series
Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding
Theory of Agreement-on-the-Line in Linear Models and Gaussian Data
Mixed Models with Multiple Instance Learning
Contextual Directed Acyclic Graphs
Global Group Fairness in Federated Learning via Function Tracking
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI
A fully adaptive algorithm for pure exploration in linear bandits
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization
Nonlinear Weighted Finite Automata
No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities
Probabilistic Modeling for Sequences of Sets in Continuous-Time
Minimum Empirical Divergence for Sub-Gaussian Linear Bandits
LMEraser: Large Model Unlearning via Adaptive Prompt Tuning
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Multi-Order Information for Working Set Selection of Sequential Minimal Optimization
Bridging Multiple Worlds: Multi-marginal Optimal Transport for Causal Partial-identification Problem
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Active Bipartite Ranking with Smooth Posterior Distributions
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2015, San Diego, California, USA, May 9-12, 2015
Variational Marginal Particle Filters
Multitask Metric Learning: Theory and Algorithm
Communication-Efficient Federated Learning With Data and Client Heterogeneity
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
Computing epidemic metrics with edge differential privacy
Geometry Aware Mappings for High Dimensional Sparse Factors
Entropy Regularized Optimal Transport Independence Criterion
f-PO: Generalizing Preference Optimization with f-divergence Minimization
Learning with Maximum A-Posteriori Perturbation Models
On Anomaly Ranking and Excess-Mass Curves
Stochastic Approximation with Biased MCMC for Expectation Maximization
The Expressive Power of a Class of Normalizing Flow Models
Exploiting Categorical Structure Using Tree-Based Methods
ClusterSC: Advancing Synthetic Control with Donor Selection
Nearly second-order optimality of online joint detection and estimation via one-sample update schemes
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
Causal Discovery on Dependent Binary Data
Faster Recalibration of an Online Predictor via Approachability
Cross-domain recommendation without shared users or items by sharing latent vector distributions
Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees
Online Boosting Algorithms for Multi-label Ranking
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
Unsupervised Change Point Detection in Multivariate Time Series
Building an EDA Assistant: A Progress Report
Tight bounds for minimum ℓ 1 -norm interpolation of noisy data
Graph fission and cross-validation
On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior
Spectral risk-based learning using unbounded losses
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
On-Demand Communication for Asynchronous Multi-Agent Bandits
Fast Algorithms for Sparse Reduced-Rank Regression
No time to waste: practical statistical contact tracing with few low-bit messages
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
Sparse Solutions to Nonnegative Linear Systems and Applications
Efficient Bayes Risk Estimation for Cost-Sensitive Classification
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients
Importance Matching Lemma for Lossy Compression with Side Information
Distributionally Robust Bayesian Optimization
Surrogate Active Subspaces for Jump-Discontinuous Functions
Online Learning for Non-monotone DR-Submodular Maximization: From Full Information to Bandit Feedback
Prior-aware Composition Inference for Spectral Topic Models
Regularized spectral learning
Scalable Out-of-Distribution Robustness in the Presence of Unobserved Confounders
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, AISTATS 2003, Key West, Florida, USA, January 3-6, 2003
Causal Bandits with General Causal Models and Interventions
A Statistical Perspective on Coreset Density Estimation
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Differentiable Causal Structure Learning with Identifiability by NOTIME
Benign overfitting of non-smooth neural networks beyond lazy training
Causal Autoregressive Flows
BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
A Spectral Perspective of DNN Robustness to Label Noise
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
InfoNCE: Identifying the Gap Between Theory and Practice
Lifted Weight Learning of Markov Logic Networks Revisited
NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning
From Data Imputation to Data Cleaning - Automated Cleaning of Tabular Data Improves Downstream Predictive Performance
Differentiable Rendering with Reparameterized Volume Sampling
Fast and Adversarial Robust Kernelized SDU Learning
Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap
Learning the Truth From Only One Side of the Story
Tamed Langevin sampling under weaker conditions
Optimistic Safety for Online Convex Optimization with Unknown Linear Constraints
Are they really neighbors? A statistical analysis of the SOM algorithm output
New Bounds on Compressive Linear Least Squares Regression
Minimax Optimization: The Case of Convex-Submodular
Federated Communication-Efficient Multi-Objective Optimization
Semi-supervised clustering for de-duplication
Intelligent Assistant for Computational Scientists: Integrated Modelling, Experimentation and Analysis
PAC-Bayesian Theory for Transductive Learning
CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC
An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications
Robust Locally-Linear Controllable Embedding
Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization
Fast Feature Selection with Fairness Constraints
Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces
A Scalable Algorithm for Structured Kernel Feature Selection
Power-Law Graph Cuts
Stochastic Neural Network with Kronecker Flow
Non-asymptotic Performance Guarantees for Neural Estimation of f-Divergences
Optimization as Estimation with Gaussian Processes in Bandit Settings
Attenuating Bias in Word vectors
Modeling and Monitoring Dynamic Systems by Chain Graphs
Bandit Convex Optimization in Non-stationary Environments
Direct Inference of Effect of Treatment (DIET) for a Cookieless World
Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Why Adaptively Collected Data Have Negative Bias and How to Correct for It
A General Theoretical Paradigm to Understand Learning from Human Preferences
Scalable Unbalanced Sobolev Transport for Measures on a Graph
Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies
On Local Posterior Structure in Deep Ensembles
Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models
Algorithms for Fairness in Sequential Decision Making
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity
HAR-former: Hybrid Transformer with an Adaptive Time-Frequency Representation Matrix for Long-Term Series Forecasting
Sample complexity bounds for localized sketching
Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens
Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces
Mode estimation on matrix manifolds: Convergence and robustness
A Likelihood Based Approach for Watermark Detection
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
Unconditional Coresets for Regularized Loss Minimization
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection
A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop
Super-Acceleration with Cyclical Step-sizes
Inference in Stochastic Epidemic Models via Multinomial Approximations
Variational Autoencoders for Sparse and Overdispersed Discrete Data
Hierarchical mixtures-of-experts for generalized linear models: some results on denseness and consistency
Semi-Supervised Learning with Competitive Infection Models
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
Graphical Normalizing Flows
Finding the bandit in a graph: Sequential search-and-stop
Efficient Bayesian Experimental Design for Implicit Models
Meta Learning in Bandits within shared affine Subspaces
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
Accelerating Gradient Boosting Machines
Distributed Inexact Newton-type Pursuit for Non-convex Sparse Learning
Computing Abductive Explanations for Boosted Trees
Cluster Canonical Correlation Analysis
Probability and Statistics in the Law
AlleNoise - large-scale text classification benchmark dataset with real-world label noise
Block Regularized Lasso for Multivariate Multi-Response Linear Regression
On Estimating Causal Effects based on Supplemental Variables
Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning