yusuke1997/dblp-id-title huggingface dataset:
Hierarchical Explanation-Based Reinforcement Learning
The Elicitation Game: Evaluating Capability Elicitation Techniques
Explorations of Self-Repair in Language Models
Building Robust Ensembles via Margin Boosting
Channel Normalization for Time Series Channel Identification
Model-Targeted Poisoning Attacks with Provable Convergence
On the quantitative analysis of deep belief networks
Improved Discretization Complexity Analysis of Consistency Models: Variance Exploding Forward Process and Decay Discretization Scheme
Composable Planning with Attributes
Towards Constituting Mathematical Structures for Learning to Optimize
Agglomerative Bregman Clustering
Probabilistic Modeling of Interpersonal Coordination Processes
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Predictable Dual-View Hashing
The Mechanics of n-Player Differentiable Games
SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding
A smoothed boosting algorithm using probabilistic output codes
Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution
Less is More: on the Over-Globalizing Problem in Graph Transformers
Data-free Distillation of Diffusion Models with Bootstrapping
Scaling Laws for the Value of Individual Data Points in Machine Learning
Joint Inference of Multiple Label Types in Large Networks
On multi-view active learning and the combination with semi-supervised learning
Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
The multiple multiplicative factor model for collaborative filtering
A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization
Rapid Adaptation with Conditionally Shifted Neurons
Co-manifold learning with missing data
Distributed Parameter Estimation via Pseudo-likelihood
Binary Hypothesis Testing for Softmax Models and Leverage Score Models
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
Partial order embedding with multiple kernels
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs
Scalable Approximate Bayesian Inference for Particle Tracking Data
Policy-labeled Preference Learning: Is Preference Enough for RLHF?
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
Quantum Implicit Neural Representations
Stealing part of a production language model
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation
SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics
All-atom inverse protein folding through discrete flow matching
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs
Adversarial Option-Aware Hierarchical Imitation Learning
On Kinetic Optimal Probability Paths for Generative Models
Is Kernel Prediction More Powerful than Gating in Convolutional Neural Networks?
Some Greedy Algorithms for Sparse Nonlinear Regression
Correcting Noisy Data
Covariate balancing using the integral probability metric for causal inference
Model Function Based Conditional Gradient Method with Armijo-like Line Search
Improved Estimation in Time Varying Models
ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks
Simple Hardware-Efficient Long Convolutions for Sequence Modeling
Dataset Condensation with Contrastive Signals
Finito: A faster, permutable incremental gradient method for big data problems
Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Robust Learning from Untrusted Sources
Direct Prediction Set Minimization via Bilevel Conformal Classifier Training
On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms
Learning discontinuities with products-of-sigmoids for switching between local models
The Many Shapley Values for Model Explanation
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition
Efficiently sampling functions from Gaussian process posteriors
Online Mean Field Approximation for Automated Experimentation
Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation
Exactly Tight Information-theoretic Generalization Bounds via Binary Jensen-Shannon Divergence
Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters
DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics
CarpeDiem: an algorithm for the fast evaluation of SSL classifiers
How to Trace Latent Generative Model Generated Images without Artificial Watermark?
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
Towards Theoretical Understanding of Inverse Reinforcement Learning
Clustering Partially Observed Graphs via Convex Optimization
Amortised Learning by Wake-Sleep
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
Varieties of AI Explanations Under the Law. From the GDPR to the AIA, and Beyond
SADA: A General Framework to Support Robust Causation Discovery
A Language for Counterfactual Generative Models
Non-Autoregressive Neural Text-to-Speech
TimeX++: Learning Time-Series Explanations with Information Bottleneck
Unifying Themes in Empirical and Explanation-Based Learning
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
LLM-Empowered State Representation for Reinforcement Learning
Sparse Sinkhorn Attention
Test-time Adaptation on Graphs via Adaptive Subgraph-based Selection and Regularized Prototypes
Imitation Learning from a Single Temporally Misaligned Video
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Label-Free Explainability for Unsupervised Models
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures
Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression
HyperNear: Unnoticeable Node Injection Attacks on Hypergraph Neural Networks
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model
Differentiable and Transportable Structure Learning
The Uncertainty Bellman Equation and Exploration
Least-Squares Temporal Difference Learning
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase
Poolingformer: Long Document Modeling with Pooling Attention
Explanations of Empirically Derived Reactive Plans
Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation
Proper Scoring Rules for Survival Analysis
Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations
Compositional Curvature Bounds for Deep Neural Networks
The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
Matching Structure for Dual Learning
Subspace Embedding and Linear Regression with Orlicz Norm
Bayesian Generative Active Deep Learning
Knowledge-Guided Wasserstein Distributionally Robust Optimization
Nonparametric Teaching of Implicit Neural Representations
Counterfactual Graphical Models: Constraints and Inference
AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
Empirical Analysis of a Dynamic Social Network Built from PGP Keyrings
Conformalized Adaptive Forecasting of Heterogeneous Trajectories
A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents
3D-LMVIC: Learning-based Multi-View Image Compression with 3D Gaussian Geometric Priors
EFDTR: Learnable Elliptical Fourier Descriptor Transformer for Instance Segmentation
CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation
Reserve Price Optimization for First Price Auctions in Display Advertising
DeFoG: Discrete Flow Matching for Graph Generation
Statistical Test for Attention Maps in Vision Transformers
Certified Neural Network Watermarks with Randomized Smoothing
FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation
Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis
PHOG: Probabilistic Model for Code
Matrix Sketching for Secure Collaborative Machine Learning
Modeling Irregular Time Series with Continuous Recurrent Units
Learning and Sampling of Atomic Interventions from Observations
Reinforcement Learning within Tree Search for Fast Macro Placement
Large-Scale Cox Process Inference using Variational Fourier Features
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
On-line Learning for Humanoid Robot Systems
Optimization from Structured Samples for Coverage Functions
Conditioning by adaptive sampling for robust design
Structure compilation: trading structure for features
Generalizing Orthogonalization for Models with Non-Linearities
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits
Denoising Autoregressive Representation Learning
An Inductive Learning Approach to Prognostic Prediction
Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
Autoregressive Diffusion Model for Graph Generation
Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Human Cognition-Inspired Hierarchical Fuzzy Learning Machine
Associative Reinforcement Learning using Linear Probabilistic Concepts
Conditional Noise-Contrastive Estimation of Unnormalised Models
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory
Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain
Personalized Federated Learning through Local Memorization
Theory of Dual-sparse Regularized Randomized Reduction
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
Prompting a Pretrained Transformer Can Be a Universal Approximator
Probabilistic Evaluating of Bias for Learning Systems
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks
Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention
Efficient Label Propagation
Characterizing Model Erros and Differences
Topology-aware Neural Flux Prediction Guided by Physics
A Scalable Deterministic Global Optimization Algorithm for Clustering Problems
Neural Symbolic Regression that scales
Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
An Analysis of Functional Trees
Detecting Out-of-Distribution Examples with Gram Matrices
CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes
Steer LLM Latents for Hallucination Detection
Near-Optimal Decision Trees in a SPLIT Second
Minimum Width for Universal Approximation using Squashable Activation Functions
Borda Regret Minimization for Generalized Linear Dueling Bandits
SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation
Conditional Language Learning with Context
Staged and Physics-Grounded Learning Framework with Hyperintensity Prior for Pre-Contrast MRI Synthesis
Embodied CoT Distillation From LLM To Off-the-shelf Agents
Hypergraph-enhanced Dual Semi-supervised Graph Classification
TravelPlanner: A Benchmark for Real-World Planning with Language Agents
Toward Controlling Discrimination in Online Ad Auctions
Offline Model-based Optimization for Real-World Molecular Discovery
Investigating Why Contrastive Learning Benefits Robustness against Label Noise
ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior
MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series
Semi-Supervised StyleGAN for Disentanglement Learning
ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection
Link Prediction with Persistent Homology: An Interactive View
Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast
A Three-regime Model of Network Pruning
LSDS++ : Dual Sampling for Accelerated k-means++
Improved Online Confidence Bounds for Multinomial Logistic Bandits
Bottom-Up Learning of Markov Network Structure
Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization
Equivariant Deep Weight Space Alignment
Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy
Probing Emergent Semantics in Predictive Agents via Question Answering
The relationship between Precision-Recall and ROC curves
Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
TACO: Learning Task Decomposition via Temporal Alignment for Control
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization
Learning with Knowledge from Multiple Experts
Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
Unsupervised Learning Using MML
Feature Importance Disparities for Data Bias Investigations
CurBench: Curriculum Learning Benchmark
From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs
Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders
Sublinear Space Private Algorithms Under the Sliding Window Model
Agent Reviewers: Domain-specific Multimodal Agents with Shared Memory for Paper Review
Analyzing Relational Learning in the Phase Transition Framework
Feature Learning and Signal Propagation in Deep Neural Networks
Hierarchical Latent Knowledge Analysis for Co-occurrence Data
Inference of Inversion Transduction Grammars
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare
Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains
Persistent RNNs: Stashing Recurrent Weights On-Chip
Decentralized Exploration in Multi-Armed Bandits
Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing
Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities
Guarantees for Spectral Clustering with Fairness Constraints
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property
Ewald-based Long-Range Message Passing for Molecular Graphs
Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization
Learning kernels from indefinite similarities
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series
H-Consistency Guarantees for Regression
Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions
Boosting Adversarial Robustness with CLAT: Criticality Leveraged Adversarial Training
Catch Your Emotion: Sharpening Emotion Perception in Multimodal Large Language Models
Multistrategy Learning with Introspective Meta-Explanations
Combined Group and Exclusive Sparsity for Deep Neural Networks
Learning the RoPEs: Better 2D and 3D Position Encodings with STRING
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
Active Learning for Natural Language Parsing and Information Extraction
A Hierarchical Dirichlet Process Model with Multiple Levels of Clustering for Human EEG Seizure Modeling
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability
Grey-box Extraction of Natural Language Models
Bayesian Nonparametric Federated Learning of Neural Networks
Censor Dependent Variational Inference
Pareto optimal linear classification
FedNL: Making Newton-Type Methods Applicable to Federated Learning
Alternately Optimized Graph Neural Networks
Learning to Remove Cuts in Integer Linear Programming
Implicit Bias of Linear RNNs
Creative Text-to-Audio Generation via Synthesizer Programming
On Robust Mean Estimation under Coordinate-level Corruption
Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning
On the Random Conjugate Kernel and Neural Tangent Kernel
A Spectral Algorithm for Latent Tree Graphical Models
The Induction of Probabilistic Rule Sets - The Itrule Algorithm
Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination
Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift
Interactive Correlation Clustering with Existential Cluster Constraints
Enhancing Supervised Learning with Unlabeled Data
Cross-Space Active Learning on Graph Convolutional Networks
K * : An Instance-based Learner Using and Entropic Distance Measure
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
FRAG: Frequency Adapting Group for Diffusion Video Editing
MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents
Memory and Computation Efficient PCA via Very Sparse Random Projections
Learn Beneficial Noise as Graph Augmentation
Learning Mixtures of Gaussians with Censored Data
Distributional Reinforcement Learning for Efficient Exploration
Data-driven Rank Breaking for Efficient Rank Aggregation
Rhomboid Tiling for Geometric Graph Deep Learning
On the Equivalence between Herding and Conditional Gradient Algorithms
A Theory of Regularized Markov Decision Processes
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach
Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination's Impact on Machine Translation
Measuring dissimilarity with diffeomorphism invariance
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation
CHAI: Clustered Head Attention for Efficient LLM Inference
Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits
Cross-Entropy Directed Embedding of Network Data
Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD
Variational Learning of Fractional Posteriors
Learning nonlinear dynamic models
SGD with Large Step Sizes Learns Sparse Features
LoRA Training Provably Converges to a Low-Rank Global Minimum Or It Fails Loudly (But it Probably Won't Fail)
Stochastic Gradient and Langevin Processes
Learning to Infer Structures of Network Games
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
Modelling Sparse Dynamical Systems with Compressed Predictive State Representations
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables
Validating Causal Inference Methods
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps
Fair Densities via Boosting the Sufficient Statistics of Exponential Families
Density Ratio Estimation with Doubly Strong Robustness
COLA: Orchestrating Error Coding and Learning for Robust Neural Network Inference Against Hardware Defects
Explaining Probabilistic Models with Distributional Values
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Optimal Information Retention for Time-Series Explanations
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
Variable Skipping for Autoregressive Range Density Estimation
Black-box density function estimation using recursive partitioning
Optimal randomized multilevel Monte Carlo for repeatedly nested expectations
A Neural-Preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
MAGANet: Achieving Combinatorial Generalization by Modeling a Group Action
Position: Explain to Question not to Justify
BaWA: Automatic Optimizing Pruning Metric for Large Language Models with Balanced Weight and Activation
When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction
Stock Trading System Using Reinforcement Learning with Cooperative Agents
Multi-Turn Code Generation Through Single-Step Rewards
Learning with Whom to Share in Multi-task Feature Learning
Kernel selection forl semi-supervised kernel machines
Learning Discrete Structures for Graph Neural Networks
A Kernelized Stein Discrepancy for Goodness-of-fit Tests
A Co-training Approach for Multi-view Spectral Clustering
Delving into the Convergence of Generalized Smooth Minimax Optimization
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels
Logarithmic Time One-Against-Some
Decoding Layer Saliency in Language Transformers
Compositional Morphology for Word Representations and Language Modelling
Exact Exponent in Optimal Rates for Crowdsourcing
Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms
JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift
Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach
How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects
Semiparametric Contextual Bandits
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
Cones: Concept Neurons in Diffusion Models for Customized Generation
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
Pain-Free Random Differential Privacy with Sensitivity Sampling
Improved Online Conformal Prediction via Strongly Adaptive Online Learning
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Convergence Rates of Variational Inference in Sparse Deep Learning
Convex formulations of radius-margin based Support Vector Machines
Scale-free adaptive planning for deterministic dynamics & discounted rewards
Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions
PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation via Few-Shot Private Data and Generative APIs
Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent
Online learning over graphs
SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN
Rethinking Aleatoric and Epistemic Uncertainty
Test-Time Regret Minimization in Meta Reinforcement Learning
Self-taught learning: transfer learning from unlabeled data
EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
Hierarchically Optimal Average Reward Reinforcement Learning