yusuke1997/dblp-id-title huggingface dataset:
Efficient Active Algorithms for Hierarchical Clustering
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
Coordinated Double Machine Learning
Disguised Copyright Infringement of Latent Diffusion Models
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
Junction Tree Variational Autoencoder for Molecular Graph Generation
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
Action-Constrained Imitation Learning
An Alternative Softmax Operator for Reinforcement Learning
Stochastic Wasserstein Barycenters
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Scalable Gaussian Processes with Latent Kronecker Structure
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits
A hierarchical method for multi-class support vector machines
Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation
Projection-free Online Learning
Hidden Markov Anomaly Detection
Margin-aware Adversarial Domain Adaptation with Optimal Transport
On the Expressive Power of Spectral Invariant Graph Neural Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
NewsWeeder: Learning to Filter Netnews
Distributional Offline Policy Evaluation with Predictive Error Guarantees
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search
Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert
High-dimensional Clustering onto Hamiltonian Cycle
Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
Geometric Generative Modeling with Noise-Conditioned Graph Networks
Evolutionary Topology Search for Tensor Network Decomposition
TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation
A Convergent and Dimension-Independent Min-Max Optimization Algorithm
Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models
Towards a Unified Analysis of Random Fourier Features
Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
Processing Megapixel Images with Deep Attention-Sampling Models
CodeIO: Condensing Reasoning Patterns via Code Input-Output Prediction
Learning Generalized Intersection Over Union for Dense Pixelwise Prediction
Prospector Heads: Generalized Feature Attribution for Large Models & Data
Learning-Order Autoregressive Models with Application to Molecular Graph Generation
Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing
Exponential Smoothing for Off-Policy Learning
Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind
Layered State Discovery for Incremental Autonomous Exploration
Graph Automorphism Group Equivariant Neural Networks
Rethink the Role of Deep Learning towards Large-scale Quantum Systems
Learning class-discriminative dynamic Bayesian networks
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering
Solving the Multiple-Instance Problem: A Lazy Learning Approach
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
Relations, Knowledge and Empirical Learning
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
Fairness and Bias in Online Selection
Stochastic Optimization for Non-convex Inf-Projection Problems
Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion
Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning
Variational Open-Domain Question Answering
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms
Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA
Differential Privacy Under Class Imbalance: Methods and Empirical Insights
Parameterless Transductive Feature Re-representation for Few-Shot Learning
Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points
Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search
Directional Graph Networks
Conditional Diffusion Model with Nonlinear Data Transformation for Time Series Forecasting
A martingale framework for concept change detection in time-varying data streams
Understanding the Limits of Deep Tabular Methods with Temporal Shift
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Efficient Euclidean projections in linear time
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning
Beyond Task-Specific Reasoning: A Unified Conditional Generative Framework for Abstract Visual Reasoning
Data Valuation using Reinforcement Learning
BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders
Shapley Based Residual Decomposition for Instance Analysis
Towards tight bounds for rule learning
History Compression via Language Models in Reinforcement Learning
Preference Optimization for Combinatorial Optimization Problems
Fairness without Harm: Decoupled Classifiers with Preference Guarantees
Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning
Moderated and Drifting Linear Dynamical Systems
Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers
Inferring Organizational Titles in Online Communication
On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games
Repairing Faulty Mixture Models using Density Estimation
Compositional Scene Understanding through Inverse Generative Modeling
Multi-Agent Learning from Learners
AutoAttend: Automated Attention Representation Search
iSurvive: An Interpretable, Event-time Prediction Model for mHealth
Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition
Robust ML Auditing using Prior Knowledge
Learning to Collaborate in Markov Decision Processes
Automated hierarchical mixtures of probabilistic principal component analyzers
Random features models: a way to study the success of naive imputation
Theory-Guideed Induction of Logic Programs by Inference of Regular Languages
Reasoning about Operationality for Explanation-Based Learning
Adaptive Task Assignment for Crowdsourced Classification
Gradient Boosted Decision Trees for High Dimensional Sparse Output
Differentiable Product Quantization for End-to-End Embedding Compression
Improving the Variance of Differentially Private Randomized Experiments through Clustering
One-Shot Segmentation in Clutter
Interference and Generalization in Temporal Difference Learning
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study
Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014
Hierarchical Reinforcement Learning with Targeted Causal Interventions
A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
Multiple instance ranking
Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data
Explaining Time Series Predictions with Dynamic Masks
Rethinking Graph Neural Networks for Anomaly Detection
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces
HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport
Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion
Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM
Knowledge Transfer with Jacobian Matching
Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
Label Partitioning For Sublinear Ranking
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Multi-Agent Determinantal Q-Learning
A Column Generation Algorithm For Boosting
Distributionally Robust Active Learning for Gaussian Process Regression
Collaborative Learning and Recommender Systems
Local likelihood modeling of temporal text streams
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
Infinite Dynamic Bayesian Networks
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals
Automated Curriculum Learning for Neural Networks
Confidence Aware Inverse Constrained Reinforcement Learning
Doubly Robust Conformalized Survival Analysis with Right-Censored Data
Practical Contextual Bandits with Regression Oracles
A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization
Multiple Kernel Learning from Noisy Labels by Stochastic Programming
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Quickly Boosting Decision Trees - Pruning Underachieving Features Early
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels
Strategyproof Mean Estimation from Multiple-Choice Questions
Natural Perturbations for Black-box Training of Neural Networks by Zeroth-Order Optimization
Semi-Amortized Variational Autoencoders
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
Learning Systems of First-Order Rules
GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting
Connected Subgraph Detection with Mirror Descent on SDPs
Open Category Detection with PAC Guarantees
Learning by Stretching Deep Networks
Decoupled Parallel Backpropagation with Convergence Guarantee
Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization
Mixtures of Experts Unlock Parameter Scaling for Deep RL
Copula-Nested Spectral Kernel Network
Taylor Expansion of Discount Factors
The Most Persistent Soft-Clique in a Set of Sampled Graphs
Position: Enforced Amnesia as a Way to Mitigate the Potential Risk of Silent Suffering in the Conscious AI
Uncovering shared structures in multiclass classification
Multi-channel Autobidding with Budget and ROI Constraints
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
Deep Latent Graph Matching
Robust Kernel Density Estimation with Median-of-Means principle
Omni-Angle Assault: An Invisible and Powerful Physical Adversarial Attack on Face Recognition
EARTH: Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph
From PAC-Bayes Bounds to Quadratic Programs for Majority Votes
Debiasing Model Updates for Improving Personalized Federated Training
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning
GAIN: Missing Data Imputation using Generative Adversarial Nets
Online Detection of LLM-Generated Texts via Sequential Hypothesis Testing by Betting
Generalization in Kernel Regression Under Realistic Assumptions
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks
Collaborative ordinal regression
Coordinate Descent Methods for Fractional Minimization
Deep Divergence Learning
GradPS: Resolving Futile Neurons in Parameter Sharing Network for Multi-Agent Reinforcement Learning
Eigen Analysis of Conjugate Kernel and Neural Tangent Kernel
A spatio-temporal extension to Isomap nonlinear dimension reduction
Diffusion Earth Mover's Distance and Distribution Embeddings
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
EditLord: Learning Code Transformation Rules for Code Editing
Tilt and Average : Geometric Adjustment of the Last Layer for Recalibration
How much does Initialization Affect Generalization?
Understanding the Dynamics of Gradient Flow in Overparameterized Linear models
Ranking Distributions based on Noisy Sorting
ROCK: Causal Inference Principles for Reasoning about Commonsense Causality
Gap-Dependent Bounds for Federated Q-Learning
From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering
Neural Diffusion Processes
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning
LLMs Can Reason Faster Only If We Let Them
Model-Independent Online Learning for Influence Maximization
Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
On Robustness and Regularization of Structural Support Vector Machines
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
On the Statistical Benefits of Temporal Difference Learning
Scalable modeling of real graphs using Kronecker multiplication
ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering
N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations
Representation Preserving Multiclass Agnostic to Realizable Reduction
Partially Observed Exchangeable Modeling
Context-based Unsupervised Data Fusion for Decision Making
Toward Efficient Kernel-Based Solvers for Nonlinear PDEs
On Modelling Non-linear Topical Dependencies
Exploration scavenging
Are Diffusion Models Vulnerable to Membership Inference Attacks?
PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting for Novel View Synthesis
Sparse higher order conditional random fields for improved sequence labeling
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models
NetGAN without GAN: From Random Walks to Low-Rank Approximations
Non-confusing Generation of Customized Concepts in Diffusion Models
An adaptation of Relief for attribute estimation in regression
Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning
Beyond the ROC Curve: Classification Trees Using Cost-Optimal Curves, with Application to Imbalanced Datasets
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control
Learning to Create Customized Authority Lists
Preserving Personalized Pagerank in Subgraphs
Herding dynamical weights to learn
OrcaLoca: An LLM Agent Framework for Software Issue Localization
Unified Robust Semi-Supervised Variational Autoencoder
Test-time Adaptation with Slot-Centric Models
A Deep and Tractable Density Estimator
CRVI: Convex Relaxation for Variational Inference
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
Kinetic Langevin Diffusion for Crystalline Materials Generation
A Deep Semi-NMF Model for Learning Hidden Representations
Learning a Universal Template for Few-shot Dataset Generalization
Sequential Facility Location: Approximate Submodularity and Greedy Algorithm
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
Random Forest Density Estimation
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
Clustered Federated Learning via Gradient-based Partitioning
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Dataset Condensation via Efficient Synthetic-Data Parameterization
Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency
Improving Transformer Optimization Through Better Initialization
Muse: Text-To-Image Generation via Masked Generative Transformers
Analytic moment-based Gaussian process filtering
On the Initialization of Graph Neural Networks
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible
A New Generalized Error Path Algorithm for Model Selection
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Active Testing: Sample-Efficient Model Evaluation
How Does Goal Relabeling Improve Sample Efficiency?
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
Learning Markov Logic Networks Using Structural Motifs
Online A-Optimal Design and Active Linear Regression
Tracking Adversarial Targets
Deep Bayesian Filter for Bayes-Faithful Data Assimilation
Theoretical Analysis of the Nearest Neighbor Classifier in Noisy Domains
Induction of Decision Trees from Inconclusive Data
Complementary Attention for Multi-Agent Reinforcement Learning
An Efficient Subsumption Algorithm for Inductive Logic Programming
Position: Amazing Things Come From Having Many Good Models
Inductive Two-layer Modeling with Parametric Bregman Transfer
Improving the Performance of Inconsistent Knowledge Bases via Combined Optimization Method
Preconditioning Kernel Matrices
Rethinking Fano's Inequality in Ensemble Learning
Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective
Fast Large Language Model Collaborative Decoding via Speculation
Competing Bandits in Matching Markets via Super Stability
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback
Generalized Value Functions for Large Action Sets
Decoupled SGDA for Games with Intermittent Strategy Communication
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
On a Neural Implementation of Brenier's Polar Factorization
Policy Gradient in Robust MDPs with Global Convergence Guarantee
Sliced Wasserstein with Random-Path Projecting Directions
Functional Models for Regression Tree Leaves
Occult: Optimizing Collaborative Communications across Experts for Accelerated Parallel MoE Training and Inference
Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling
Parameter-Dependent Competitive Analysis for Online Capacitated Coverage Maximization through Boostings and Attenuations
Optimal Auction Design in the Joint Advertising
Clustering Continuous Time Series
Refining Adaptive Zeroth-Order Optimization at Ease
Learning Decision Trees Using the Area Under the ROC Curve
Improving Context Understanding in Multimodal Large Language Models via Multimodal Composition Learning
Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport
Taming Rectified Flow for Inversion and Editing
Refined generalization analysis of the Deep Ritz Method and Physics-Informed Neural Networks
What Can Learned Intrinsic Rewards Capture?
Only tails matter: Average-Case Universality and Robustness in the Convex Regime
Causal Abstraction Learning based on the Semantic Embedding Principle
Discriminative Embeddings of Latent Variable Models for Structured Data
Maintaining Proportional Committees with Dynamic Candidate Sets
Feature Construction in Structural Decision Trees
Autoencoder Image Interpolation by Shaping the Latent Space
Feature Subset Selection and Inductive Logic Programming
I 2 SB: Image-to-Image Schrödinger Bridge
A Distribution Optimization Framework for Confidence Bounds of Risk Measures
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score
Asymptotics of Ridge Regression in Convolutional Models
Positive Concave Deep Equilibrium Models
Finite mixture models do not reliably learn the number of components
Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements
Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities
SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation
WyckoffDiff - A Generative Diffusion Model for Crystal Symmetry
ExtPose: Robust and Coherent Pose Estimation by Extending ViTs
A Unified Decomposition of Ensemble Loss for Predicting Ensemble Performance
Improving SAM Requires Rethinking its Optimization Formulation
Improved Off-policy Reinforcement Learning in Biological Sequence Design
Hierarchical Dirichlet model for document classification
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
High speed obstacle avoidance using monocular vision and reinforcement learning
How to set AdamW's weight decay as you scale model and dataset size
State-Frequency Memory Recurrent Neural Networks
Selective Voting for Perception-like Online Learning
How Tempering Fixes Data Augmentation in Bayesian Neural Networks
Action-Sufficient State Representation Learning for Control with Structural Constraints
Boosting with structural sparsity
A Process-Oriented Heuristic for Model Selection
Efficient Memory-Based Dynamic Programming
Sample Efficient Learning of Predictors that Complement Humans
Categorization in multiple category systems
Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
Formal Privacy for Functional Data with Gaussian Perturbations
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework
ΦFlow: Differentiable Simulations for PyTorch, TensorFlow and Jax
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
Model-Based Minimum Bayes Risk Decoding for Text Generation
PAC-Bayes Analysis for Recalibration in Classification
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
On the Impact of the Activation function on Deep Neural Networks Training
Constraint relaxation in approximate linear programs
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization
KV Shifting Attention Enhances Language Modeling
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them
Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices
On Dropout and Nuclear Norm Regularization
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
On Random Weights and Unsupervised Feature Learning
Just Sort It! A Simple and Effective Approach to Active Preference Learning
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training
Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing
GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs
Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification
Fast Variance Reduction Method with Stochastic Batch Size
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization