yusuke1997/dblp-id-title huggingface dataset:
Modelling sequential branching dynamics with a multivariate branching Gaussian process
Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information
Self-supervised Color Generalization in Reinforcement Learning
Disappearance of Timestep Embedding: A Case Study on Neural ODE and Diffusion Models
Bi-Mamba: Towards Accurate 1-Bit State Space Model
2SSP: A Two-Stage Framework for Structured Pruning of LLMs
Fast Computation of Leave-One-Out Cross-Validation for k -NN Regression
Risk‑Seeking Reinforcement Learning via Multi‑Timescale EVaR Optimization
Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach
Defending Against Unknown Corrupted Agents: Reinforcement Learning of Adversarially Robust Nash Equilibria
Identifiable Deep Generative Models via Sparse Decoding
Error bounds and dynamics of bootstrapping in actor-critic reinforcement learning
Deep Tabular Learning via Distillation and Language Guidance
We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline
Learning domain-specific causal discovery from time series
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
Federated Sampling with Langevin Algorithm under Isoperimetry
SELU: Self-Learning Embodied Multimodal Large Language Models in Unknown Environments
Fair Online Influence Maximization
Generalization bounds for Kernel Canonical Correlation Analysis
A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity
Neural Implicit Manifold Learning for Topology-Aware Density Estimation
Proximal Curriculum for Reinforcement Learning Agents
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize Range
HoSNNs: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
DeepRRTime: Robust Time-series Forecasting with a Regularized INR Basis
Implicit Ensemble Training for Efficient and Robust Multiagent Reinforcement Learning
G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize
MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training
Soft Merging of Experts with Adaptive Routing
Ask Your Distribution Shift if Pre-Training is Right for You
LTL-Constrained Policy Optimization with Cycle Experience Replay
Convergences for Minimax Optimization Problems over Infinite-Dimensional Spaces Towards Stability in Adversarial Training
Fairness-Aware Dense Subgraph Discovery
Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization
Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations
Spaced Scheduling for Large Language Model Training
Transfer Learning for High-dimensional Quantile Regression with Statistical Guarantee
CoDe: Blockwise Control for Denoising Diffusion Models
Learning few-step posterior samplers by unfolding and distillation of diffusion models
Learning to Look by Self-Prediction
Differentiated Aggregation to Improve Generalization in Federated Learning
B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability
PolyViT: Co-training Vision Transformers on Images, Videos and Audio
Controlling Neural Network Smoothness for Neural Algorithmic Reasoning
Practical Synthesis of Mixed-Tailed Data with Normalizing Flows
Introducing "Forecast Utterance" for Conversational Data Science
Learning Algorithms for Markovian Bandits: Is Posterior Sampling more Scalable than Optimism?
UnSTAR: Unlearning with Self-Taught Anti-Sample Reasoning for LLMs
Node Feature Forecasting in Temporal Graphs: an Interpretable Online Algorithm
Schauder Bases for $C[0, 1]$ Using ReLU, Softplus and Two Sigmoidal Functions
Population Priors for Matrix Factorization
An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration
Test-Time Fairness and Robustness in Large Language Models
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance
Improving Continual Learning by Accurate Gradient Reconstructions of the Past
The Geometry of Mixability
FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction
Contrastive Graph Autoencoder for Shape-based Polygon Retrieval from Large Geometry Datasets
Calibrated Probabilistic Forecasts for Arbitrary Sequences
FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection
Personalized Layer Selection for Graph Neural Networks
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images
Invariant Feature Coding using Tensor Product Representation
Efficient Object-Centric Representation Learning using Masked Generative Modeling
Improve Certified Training with Signal-to-Noise Ratio Loss to Decrease Neuron Variance and Increase Neuron Stability
Studying memorization of large language models using answers to Stack Overflow questions
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs
[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
Temporal horizons in forecasting: a performance-learnability trade-off
On the Data Heterogeneity in Adaptive Federated Learning
Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text
FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data
State space models can express n -gram languages
Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
Soft Diffusion: Score Matching with General Corruptions
Walking on the Fiber: A Simple Geometric Approximation for Bayesian Neural Networks
Optimal Client Sampling for Federated Learning
Directed Exploration in Reinforcement Learning from Linear Temporal Logic
The Fundamental Limits of Neural Networks for Interval Certified Robustness
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
On Robustness to Missing Video for Audiovisual Speech Recognition
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy
♣ CLOVER ♣: Probabilistic Forecasting with Coherent Learning Objective Reparameterization
A Survey of Reinforcement Learning from Human Feedback
Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
Evaluating Spatial Understanding of Large Language Models
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
Statistical Mechanics of Min-Max Problems
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
A Scalable Approach for Mapper via Efficient Spatial Search
CLImage: Human-Annotated Datasets for Complementary-Label Learning
Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis
OmniPred: Language Models as Universal Regressors
Revisiting Contrastive Divergence for Density Estimation and Sample Generation
A Survey on Transformers in Reinforcement Learning
ODNet: Opinion Dynamics-Inspired Neural Message Passing for Graphs and Hypergraphs
MMD Two-sample Testing in the Presence of Arbitrarily Missing Data
Adaptive Physics-informed Neural Networks: A Survey
Generalizing Neural Additive Models via Statistical Multimodal Analysis
Input Normalized Stochastic Gradient Descent Training for Deep Neural Networks
Scalable Deep Compressive Sensing
Detecting incidental correlation in multimodal learning via latent variable modeling
Rethinking Robustness in Machine Learning: A Posterior Agreement Approach
Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence
RLHF Workflow: From Reward Modeling to Online RLHF
Testing with Non-identically Distributed Samples
Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
Is Value Functions Estimation with Classification Plug-and- play for Offline Reinforcement Learning?
Towards Minimal Targeted Updates of Language Models with Targeted Negative Training
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation
Dependency-aware Maximum Likelihood Estimation for Active Learning
Adversarial Feature Augmentation and Normalization for Visual Recognition
In-context Learning for Mixture of Linear Regression: Existence, Generalization and Training Dynamics
Online Selective Conformal Inference: Errors and Solutions
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
Agent-State Construction with Auxiliary Inputs
Self-Supervised Learning on Molecular Graphs: A Systematic Investigation of Masking Design
Wasserstein Modality Alignment Makes Your Multimodal Transformer More Robust
A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior
Multi-Bellman operator for convergence of Q -learning with linear function approximation
A general framework for formulating structured variable selection
Stochastic Constrained DRO with a Complexity Independent of Sample Size
Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation
Cumulative Reasoning with Large Language Models
A Unified View of Double-Weighting for Marginal Distribution Shift
Offline Learning and Forgetting for Reasoning with Large Language Models
Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization
Ranking Recovery under Privacy Considerations
Transductive Decoupled Variational Inference for Few-Shot Classification
Fourier Features in Reinforcement Learning with Neural Networks
Improving and generalizing flow-based generative models with minibatch optimal transport
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?
Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences
GMAgent: A Graph-oriented Multi-agent Collaboration Framework for Text-attributed Graph Analysis
Tree Structure for the Categorical Wasserstein Weisfeiler-Lehman Graph Kernel
An Improved Federated Clustering Algorithm with Model-based Clustering
Cooperative Minibatching in Graph Neural Networks
Mixture of Latent Experts Using Tensor Products
Understanding and Robustifying Sub-domain Alignment for Domain Adaptation
Federated 𝒳-armed Bandit with Flexible Personalisation
Faster Diffusion Through Temporal Attention Decomposition
Numerically Robust Fixed-Point Smoothing Without State Augmentation
Spurious Privacy Leakage in Neural Networks
Persistent Local Homology in Graph Learning
Multi-intention Inverse Q-learning for Interpretable Behavior Representation
Multimodal Language Learning for Object Retrieval in Low Data Regimes in the Face of Missing Modalities
Monotone Missing Data: A Blessing and a Curse
Unsupervised 3D Scene Representation Learning via Movable Object Inference
Approximation, Estimation and Optimization Errors for a Deep Neural Network
A noise-corrected Langevin algorithm and sampling by half-denoising
DiffCLIP: Differential Attention Meets CLIP
InfoNCE is variational inference in a recognition parameterised model
DORA: Exploring Outlier Representations in Deep Neural Networks
RANa: Retrieval-Augmented Navigation
ILPO-MP: Mode Priors Prevent Mode Collapse when Imitating Latent Policies from Observations
Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation under Symmetry
Differentially Private Source-Target Clustering
Partial-Label Learning with a Reject Option
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective
Learning State Reachability as a Graph in Translation Invariant Goal-based Reinforcement Learning Tasks
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
Homogenizing Non-IID Datasets via In-Distribution Knowledge Distillation for Decentralized Learning
MaskMA: Towards Zero-Shot Multi-Agent Decision Making with Mask-Based Collaborative Learning
Bayesian Transformed Gaussian Processes
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning
Classifier-Free Guidance is a Predictor-Corrector
TFAR: A Training-Free Framework for Autonomous Reliable Reasoning in Visual Question Answering
Object-Centric Relational Representations for Image Generation
Modeling Object Dissimilarity for Deep Saliency Prediction
ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment
LLaVA-OneVision: Easy Visual Task Transfer
ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method
Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler
Adapting Chat Language Models Using Only Target Unlabeled Language Data
Risk Bounds for Mixture Density Estimation on Compact Domains via the h-Lifted Kullback-Leibler Divergence
A Measure of the Complexity of Neural Representations based on Partial Information Decomposition
Unsupervised Discovery of Object-Centric Neural Fields
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
On Inherent Adversarial Robustness of Active Vision Systems
Fixed Budget Best Arm Identification in Unimodal Bandits
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data
Illustrated Landmark Graphs for Long-horizon Policy Learning
Optimal Inference in Contextual Stochastic Block Models
On Safety in Safe Bayesian Optimization
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
Attention Beats Concatenation for Conditioning Neural Fields
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection
σ-PCA: a building block for neural learning of identifiable linear transformations
ASTRA: A Scene-aware Transformer-based Model for Trajectory Prediction
DafnyBench: A Benchmark for Formal Software Verification
Spatio-temporal Partial Sensing Forecast of Long-term Traffic
Communication-Efficient Distributionally Robust Decentralized Learning
AttentionSmithy: A Modular Framework for Rapid Transformer Development
Vision-Language Models Provide Promptable Representations for Reinforcement Learning
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning
SE3Set: Harnessing Equivariant Hypergraph Neural Networks for Molecular Representation Learning
Revisiting B2T: Discovering and Mitigating Visual Biases through Keyword Explanations
Uncertainty-Based Active Learning for Reading Comprehension
Restricted Random Pruning at Initialization for High Compression Range
Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation
Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?
Hybrid Federated Learning for Feature & Sample Heterogeneity: Algorithms and Implementation
MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth
KD-BIRL: Kernel Density Bayesian Inverse Reinforcement Learning
Beyond ordinary Lipschitz constraints: Differentially Private optimization with TNC
RedMotion: Motion Prediction via Redundancy Reduction
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks
Geometric Optimal Transport for Unsupervised Domain Adaptation
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning
Multiple Kronecker RLS fusion-based link propagation for drug-side effect prediction
Archetypal Analysis++: Rethinking the Initialization Strategy
Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation
Metalearning Continual Learning Algorithms
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
How Expressive are Transformers in Spectral Domain for Graphs?
Continuous Language Model Interpolation yields Dynamic and Controllable Text Generation
Modular Quantization-Aware Training for 6D Object Pose Estimation
Global Safe Sequential Learning via Efficient Knowledge Transfer
Thompson Sampling For Bandits With Cool-Down Periods
Leveraging Function Space Aggregation for Federated Learning at Scale
Understanding the Role of Invariance in Transfer Learning
Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation
MaskOCR: Scene Text Recognition with Masked Vision-Language Pre-training
lo-fi: distributed fine-tuning without communication
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training
LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
LitLLMs, LLMs for Literature Review: Are we there yet?
Representation Learning Dynamics of Self-Supervised Models
Adaptive Conformal Regression with Split-Jackknife+ Scores
Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers
MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path
Remembering to Be Fair Again: Reproducing Non-Markovian Fairness in Sequential Decision Making
Enhancing Physics-Informed Neural Networks Through Feature Engineering
Solving the Tree Containment Problem Using Graph Neural Networks
Personalized Federated Learning with Spurious Features: An Adversarial Approach
Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation
MoReact: Generating Reactive Motion from Textual Descriptions
Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes
On Space Folds of ReLU Neural Networks
Sketch and shift: a robust decoder for compressive clustering
Diffusion Model Predictive Control
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models
Koopman Spectrum Nonlinear Regulators and Efficient Online Learning
Fast and Accurate Spreading Process Temporal Scale Estimation
One-Round Active Learning through Data Utility Learning and Proxy Models
Exploring Format Consistency for Instruction Tuning
Locally Adaptive Federated Learning
A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science
Controlled Training Data Generation with Diffusion Models
FlowBench: Benchmarking Optical Flow Estimation Methods for Reliability and Generalization
TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks
POMRL: No-Regret Learning-to-Plan with Increasing Horizons
Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
Adaptive patch foraging in deep reinforcement learning agents
Zero-Shot Learning with Common Sense Knowledge Graphs
Why is constrained neural language generation particularly challenging?
Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representations
Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics
RIGNN: A Rationale Perspective for Semi-supervised Open-world Graph Classification
CR-MoE: Consistent Routed Mixture-of-Experts for Scaling Contrastive Learning
A unifying framework for generalised Bayesian online learning in non-stationary environments
Generalized Tangent Kernel: A Unified Geometric Foundation for Natural Gradient and Standard Gradient
Bridging Causality, Individual Fairness, and Adversarial Robustness in the Absence of Structural Causal Model
ZerO Initialization: Initializing Neural Networks with only Zeros and Ones
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization
Gaussian Pre-Activations in Neural Networks: Myth or Reality?
Disobeying Directions: Switching Random Walk Filters for Unsupervised Node Embedding Learning on Directed Graphs
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning
Finetuning CLIP to Reason about Pairwise Differences
A Fully Decentralized Surrogate for Multi-Agent Policy Optimization
Tracking the Median of Gradients with a Stochastic Proximal Point Method
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons
Hidden Heterogeneity: When to Choose Similarity-Based Calibration
MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning
Efficient Parallelized Simulation of Cyber-Physical Systems
MiniFold: Simple, Fast, and Accurate Protein Structure Prediction
Real-Time Privacy Preservation for Robot Visual Perception
A Lean Dataset for International Math Olympiad: Small Steps towards Writing Math Proofs for Hard Problems
Your Classifier Can Be Secretly a Likelihood-Based OOD Detector
Learning the essential in less than 2k additional weights - a simple approach to improve image classification stability under corruptions
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
Fingerprints of Super Resolution Networks
A Theoretical Study of Neural Network Expressive Power via Manifold Topology
Strengthening Interpretability: An Investigative Study of Integrated Gradient Methods
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment
Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism
Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces
Representation Alignment in Neural Networks
Reliable Active Learning via Influence Functions
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees
A Snapshot of the Frontiers of Client Selection in Federated Learning
BaSIS-Net: From Point Estimate to Predictive Distribution in Neural Networks - A Bayesian Sequential Importance Sampling Framework
Training DNNs Resilient to Adversarial and Random Bit-Flips by Learning Quantization Ranges
DRAGON: Distributional Rewards Optimize Diffusion Generative Models
Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building
Lifelong Reinforcement Learning with Modulating Masks
A Gold Standard Dataset for the Reviewer Assignment Problem
PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training
Provable Membership Inference Privacy
Scaling Laws for Imitation Learning in Single-Agent Games
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
Chimera: State Space Models Beyond Sequences
Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud
Personalized Federated Learning with Communication Compression