yusuke1997/dblp-id-title huggingface dataset:
Learning to Solve PDE-constrained Inverse Problems with Graph Networks
Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective
Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries
Polyhedral outer approximations with application to natural language parsing
Evaluation of Trajectory Distribution Predictions with Energy Score
CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance
Representation Learning via Adversarially-Contrastive Optimal Transport
Pure Exploration and Regret Minimization in Matching Bandits
Balanced Resonate-and-Fire Neurons
A Functional Information Perspective on Model Interpretation
Distributed Conformal Prediction via Message Passing
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Projection Robust Wasserstein Barycenters
ParallelComp: Parallel Long-Context Compressor for Length Extrapolation
Adaptive Estimation and Learning under Temporal Distribution Shift
Sequential Changepoint Detection via Backward Confidence Sequences
Measuring Diversity in Synthetic Datasets
Understanding and Accelerating Particle-Based Variational Inference
Dynamic Planning and Learning under Recovering Rewards
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
Unifying Knowledge from Diverse Datasets to Enhance Spatial-Temporal Modeling: A Granularity-Adaptive Geographical Embedding Approach
Universal Morphology Control via Contextual Modulation
RUMs from Head-to-Head Contests
Invariance of Weight Distributions in Rectified MLPs
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search
Injecting Logical Constraints into Neural Networks via Straight-Through Estimators
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
Online Learning to Rank in Stochastic Click Models
When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Efficient Real-Time Pixelwise Object Class Labeling for Safe Human-Robot Collaboration in Industrial Domain
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Logic and Pragmatics in AI Explanation
SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models
Monge blunts Bayes: Hardness Results for Adversarial Training
Graph Clustering and Model Learning by Data Compression
An Advanced Evolution Should Not Repeat its Past Errors
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Learning disentangled representations via product manifold projection
Learning from Attribute Value Taxonomies and Partially Specified Instances
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms
Graph Contrastive Learning Automated
Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism
Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent
Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN
Pricing with Contextual Elasticity and Heteroscedastic Valuation
Visual Representation Learning with Stochastic Frame Prediction
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction
Optimizing NOTEARS Objectives via Topological Swaps
Bottom-up learning of Markov logic network structure
EasyInv: Toward Fast and Better DDIM Inversion
Unsupervised Deep Learning by Neighbourhood Discovery
A Reductions Approach to Fair Classification
An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization
Propagate and Inject: Revisiting Propagation-Based Feature Imputation for Graphs with Partially Observed Features
Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning
Stabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process
Sliding Down the Stairs: How Correlated Latent Variables Accelerate Learning with Neural Networks
Comparing Graph Transformers via Positional Encodings
FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks
Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators
Re-understanding Finite-State Representations of Recurrent Policy Networks
Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes
An Adaptive Test of Independence with Analytic Kernel Embeddings
Global-Local Dirichlet Processes for Clustering Grouped Data in the Presence of Group-Specific Idiosyncratic Variables
An SDE for Modeling SAM: Theory and Insights
Speeding Up Bellman Ford via Minimum Violation Permutations
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
A New Bayesian Rating System for Team Competitions
Learning Multiple Secrets in Mastermind
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
UAST: Uncertainty-Aware Siamese Tracking
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
Logits are All We Need to Adapt Closed Models
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models
OmniAudio: Generating Spatial Audio from 360-Degree Video
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
A Generic Family of Graphical Models: Diversity, Efficiency, and Heterogeneity
Adversarial Generation of Time-Frequency Features with application in audio synthesis
Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains
Towards Understanding Inductive Bias in Transformers: A View From Infinity
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes
Implicit Bias of AdamW: ℓ∞-Norm Constrained Optimization
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning
Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data
R.I.P.: Better Models by Survival of the Fittest Prompts
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
Multichannel End-to-end Speech Recognition
Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
Machine Learning for Subproblem Selection
Projecting Molecules into Synthesizable Chemical Spaces
Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation
Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
A Theory of Label Propagation for Subpopulation Shift
Locally Private Bayesian Inference for Count Models
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
Partial Multi-View Multi-Label Classification via Semantic Invariance Learning and Prototype Modeling
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
How Many Perturbations Break This Model? Evaluating Robustness Beyond Adversarial Accuracy
Video Prediction via Example Guidance
Sign Rank Limitations for Inner Product Graph Decoders
Robust and Conjugate Gaussian Process Regression
LAST SToP for Modeling Asynchronous Time Series
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models
Incremental Learning of Rules and Meta-rules
WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales
Improving Zero-Shot Adversarial Robustness in Vision-Language Models by Closed-form Alignment of Adversarial Path Simplices
Robust Learning-Augmented Dictionaries
Closing the convergence gap of SGD without replacement
Learning the Parameters of Determinantal Point Process Kernels
Generative Modeling for Multi-task Visual Learning
Optimizing DDPM Sampling with Shortcut Fine-Tuning
Post-hoc Part-Prototype Networks
A General Framework for Learning from Weak Supervision
Stochastic Gradient Succeeds for Bandits
Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger
A sampling theory perspective on activations for implicit neural representations
Equivariance with Learned Canonicalization Functions
ConfPO: Exploiting Policy Model Confidence for Critical Token Selection in Preference Optimization
The Lock-in Hypothesis: Stagnation by Algorithm
LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models
Calibrated Value-Aware Model Learning with Probabilistic Environment Models
Adversarial robustness of amortized Bayesian inference
Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving
SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
Babel: A Psychologically Plausible Cross-Linguistic Model of Lexical and Syntactic Acquisition
Light and Optimal Schrödinger Bridge Matching
Conceptual Clustering of Explanations
Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models
DeepReDuce: ReLU Reduction for Fast Private Inference
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence
Gaussian Plane-Wave Neural Operator for Electron Density Estimation
Parameter space exploration with Gaussian process trees
One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment
Efficient Federated Incomplete Multi-View Clustering
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Contextual Information-Directed Sampling
ButterflyFlow: Building Invertible Layers with Butterfly Matrices
Reason for Future, Act for Now: A Principled Architecture for Autonomous LLM Agents
Spectral-Aware Reservoir Computing for Fast and Accurate Time Series Classification
Adversarial Learning of Distributional Reinforcement Learning
AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions
Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks
On Statistical Learning Theory for Distributional Inputs
Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction
Lorentzian Distance Learning for Hyperbolic Representations
Learning Distributions over Quantum Measurement Outcomes
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Fast Algorithms for Distributed k-Clustering with Outliers
Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Info-Coevolution: An Efficient Framework for Data Model Coevolution
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models
Linear Complexity Randomized Self-attention Mechanism
Decoupling Learning and Decision-Making: Breaking the O(T) Barrier in Online Resource Allocation with First-Order Methods
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems
Explainable and Discourse Topic-aware Neural Language Understanding
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
TimeBase: The Power of Minimalism in Efficient Long-term Time Series Forecasting
Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver
Learning the Dependence Graph of Time Series with Latent Factors
Maximum Entropy Reinforcement Learning with Diffusion Policy
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Reducing Search and Learning Goal Preferences
PAC-Bayesian learning of linear classifiers
Discovering Mathematical Operation Definitions
On Pruning and Averaging Decision Trees
Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks
Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning
Learning Latent Space Hierarchical EBM Diffusion Models
Learning disconnected manifolds: a no GAN's land
Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup
UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis
Analyzing Federated Learning through an Adversarial Lens
MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models
Decision trees with minimal costs
Experimental Design on a Budget for Sparse Linear Models and Applications
Quantifying Human Priors over Social and Navigation Networks
KISA: A Unified Keyframe Identifier and Skill Annotator for Long-Horizon Robotics Demonstrations
MMInference: Accelerating Pre-filling for Long-Context Visual Language Models via Modality-Aware Permutation Sparse Attention
Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation
Variational Inference for Policy Search in changing situations
Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning
Structured Recurrent Temporal Restricted Boltzmann Machines
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction
Selecting Typical Instances in Instance-Based Learning
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
Online Learning under Budget and ROI Constraints via Weak Adaptivity
Towards "Large Margin" Speech Recognizers by Boosting and Discriminative Training
Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation
Identifying Knowledge Base Deficiencies by Observing User Behavior
Universal Asymptotic Optimality of Polyak Momentum
Structure-informed Risk Minimization for Robust Ensemble Learning
Clone-Robust AI Alignment
VITS : Variational Inference Thompson Sampling for contextual bandits
Optimal Estimation of Policy Gradient via Double Fitted Iteration
Age and Geographic Inferences of the LiveJournal Social Network
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
A Dual-module Framework for Counterfactual Estimation over Time
Reinforcement Learning in Configurable Continuous Environments
Which Training Methods for GANs do actually Converge?
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms
The Disparate Benefits of Deep Ensembles
Generative Moment Matching Networks
The Logical Options Framework
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Active reinforcement learning
Reliable Measures of Spread in High Dimensional Latent Spaces
Content-Based Image Retrieval Using Multiple-Instance Learning
Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions
Phrase-based Image Captioning
Machine-Learning Applications of Algorithmic Randomness
Online Learned Continual Compression with Adaptive Quantization Modules
Modular Duality in Deep Learning
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
ADDQ: Adaptive distributional double Q-learning
Vision Graph Prompting via Semantic Low-Rank Decomposition
AGNAS: Attention-Guided Micro and Macro-Architecture Search
An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming
Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?
Understanding Input Selectivity in Mamba: Impact on Approximation Power, Memorization, and Associative Recall Capacity
Large Language Models are Geographically Biased
Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
Variational Bayesian Quantization
Oscillation-Reduced MXFP4 Training for Vision Transformers
PromptBoosting: Black-Box Text Classification with Ten Forward Passes
EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction
Model Agnostic Sample Reweighting for Out-of-Distribution Learning
A Retrieval Model Using Feature Selection
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
A probabilistic model for text kernels
DRAG: Data Reconstruction Attack using Guided Diffusion
LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models
ACE: Explaining cluster from an adversarial perspective
Learning to Decouple Complex Systems
Heavy-tailed regression with a generalized median-of-means
Transformation-Based Regression
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis
Provable Meta-Learning of Linear Representations
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis
Incremental Reduced Error Pruning
RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
Do More Negative Samples Necessarily Hurt In Contrastive Learning?
Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning
Regression with Sensor Data Containing Incomplete Observations
Totally corrective boosting algorithms that maximize the margin
Inverse Contextual Bandits: Learning How Behavior Evolves over Time
No Free Prune: Information-Theoretic Barriers to Pruning at Initialization
Issues in Classifier Evaluation using Optimal Cost Curves
Distributionally Robust Optimization with Markovian Data
Disentangled Multi-Fidelity Deep Bayesian Active Learning
MILEAGE: Multiple Instance LEArning with Global Embedding
Learning Decision Policies with Instrumental Variables through Double Machine Learning
Graph Neural Architecture Search Under Distribution Shifts
On the Rate of Convergence and Error Bounds for LSTD(\(\lambda\))
Streaming Active Learning with Deep Neural Networks
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space
FAIRER: Fairness as Decision Rationale Alignment
FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization
DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
Disentangling and Integrating Relational and Sensory Information in Transformer Architectures
Flopping for FLOPs: Leveraging Equivariance for Computational Efficiency
Data-efficient Hindsight Off-policy Option Learning
Diffusion Model-Augmented Behavioral Cloning
Improving Interpretation Faithfulness for Vision Transformers
Composing Entropic Policies using Divergence Correction
DistiLLM: Towards Streamlined Distillation for Large Language Models
DynaMind: Reasoning over Abstract Video Dynamics for Embodied Decision-Making
Intriguing Properties of Input-Dependent Randomized Smoothing
OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
Low-density Parity Constraints for Hashing-Based Discrete Integration
Predicting Out-of-Distribution Error with the Projection Norm
A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization
Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates
State-Constrained Zero-Sum Differential Games with One-Sided Information
Training Deep Energy-Based Models with f-Divergence Minimization
The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
On the Convergence Rate of Gaussianization with Random Rotations
Prior Image-Constrained Reconstruction using Style-Based Generative Models
Active Learning with Logged Data
Negative Robust Learning Results from Horn Claus Programs
On Relaxing Determinism in Arithmetic Circuits
Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignment
GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation
Towards a Unified Framework of Clustering-based Anomaly Detection
Algorithm Selection using Reinforcement Learning
Causal Transformer for Estimating Counterfactual Outcomes
Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos
Efficiently learning linear-linear exponential family predictive representations of state
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
Learning Polynomial Functions by Feature Construction
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input
TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge
Mining Clustering Dimensions
Comparing Clusterings in Space
Taylor Expansion Policy Optimization
Knowledge Acquisition Planning: Results and Prospects
Constructing basis functions from directed graphs for value function approximation
Understanding and Mitigating Accuracy Disparity in Regression
Online Sparsification of Bipartite-Like Clusters in Graphs
Scaling Laws for Pre-training Agents and World Models
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
Correlation Clustering with Asymmetric Classification Errors
Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Graph Switching Dynamical Systems
Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval
Learning Binary Decision Trees by Argmin Differentiation
Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback
Multi-way distributional clustering via pairwise interactions
Convergence rate of Bayesian tensor estimator and its minimax optimality
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning
Fast Computation of Subpath Kernel for Trees
One-Shot Strategic Classification Under Unknown Costs