yusuke1997/dblp-id-title huggingface dataset:
SDMG: Smoothing Your Diffusion Models for Powerful Graph Representation Learning
Combining Diverse Feature Priors
Bayesian Learning via Stochastic Gradient Langevin Dynamics
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Deep Structured Energy Based Models for Anomaly Detection
Simple Path Structural Encoding for Graph Transformers
RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Nonextensive entropic kernels
The DUCTOR: A Theory Revision System for Propositional Domains
EigenTransfer: a unified framework for transfer learning
Should Decision-Makers Reveal Classifiers in Online Strategic Classification?
Polynomial-Time Approximability of Constrained Reinforcement Learning
Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models
SDP-CROWN: Efficient Bound Propagation for Neural Network Verification with Tightness of Semidefinite Programming
Rectify Heterogeneous Models with Semantic Mapping
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models
Low-Precision Stochastic Gradient Langevin Dynamics
Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations
Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance
A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models
CW Complex Hypothesis for Image Data
A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance
DeepLayout: Learning Neural Representations of Circuit Placement Layout
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Piecewise pseudolikelihood for efficient training of conditional random fields
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
Planning in the Presence of Cost Functions Controlled by an Adversary
A Dynamic Algorithm for Weighted Submodular Cover Problem
Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis
Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations
Nearest Neighbour Score Estimators for Diffusion Generative Models
Dynamic Facility Location in High Dimensional Euclidean Spaces
Procurement Auctions via Approximately Optimal Submodular Optimization
Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics
Learning with Marginalized Corrupted Features
Deep Asymmetric Multi-task Feature Learning
Distributed Mean Estimation with Limited Communication
GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing
Improved Algorithms for Agnostic Pool-based Active Classification
A Theoretical Framework For Overfitting In Energy-based Modeling
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
An LLM Compiler for Parallel Function Calling
A duality view of spectral methods for dimensionality reduction
Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs
Weisfeiler-Leman at the margin: When more expressivity matters
On the Query Complexity of Verifier-Assisted Language Generation
Robust one-class clustering using hybrid global and local search
Don't Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget
Generative Flow Networks for Discrete Probabilistic Modeling
Open-Domain Text Evaluation via Contrastive Distribution Methods
Learning Latent Graph Structures and their Uncertainty
On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions
Healing the relevance vector machine through augmentation
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?
Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration
Random Spanning Trees and the Prediction of Weighted Graphs
Inductive Moment Matching
Benign Overfitting in Token Selection of Attention Mechanism
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning
The Infinite Regionalized Policy Representation
AAAR-1.0: Assessing AI's Potential to Assist Research
Learning Perturbations to Explain Time Series Predictions
Low-Rank Adapting Models for Sparse Autoencoders
From Transformation-Based Dimensionality Reduction to Feature Selection
Diffusion Adversarial Post-Training for One-Step Video Generation
On Handling Tree-Structured Attributed in Decision Tree Learning
Multi-View Stochastic Block Models
Hypothesis Testing for Generalized Thurstone Models
Online Control of the False Coverage Rate and False Sign Rate
Bi-level path following for cross validated solution of kernel quantile regression
An Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity
Database Consistency via Inductive Learning
Do RNN and LSTM have Long Memory?
Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise
Constructive Induction of M-of-N Terms
Optimizing Test-Time Compute via Meta Reinforcement Finetuning
Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors
A Joint Exponential Mechanism For Differentially Private Top-k
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique
Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes
Adapting to misspecification in contextual bandits with offline regression oracles
Design Considerations in Offline Preference-based RL
Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy Forecasting
Effective and Efficient Structural Inference with Reservoir Computing
Source Separation with Deep Generative Priors
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks
Nested Elimination: A Simple Algorithm for Best-Item Identification From Choice-Based Feedback
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees
Workshop summary: Seventh annual workshop on Bayes applications
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks
Neural FIM for learning Fisher information metrics from point cloud data
Scaling SVM and Least Absolute Deviations via Exact Data Reduction
VerbalTS: Generating Time Series from Texts
Online Ranking/Collaborative Filtering Using the Perceptron Algorithm
Feature Selection as a Preprocessing Step for Hierarchical Clustering
Re-evaluating Word Mover's Distance
Revisiting the Role of Language Priors in Vision-Language Models
Diffusion Models are Minimax Optimal Distribution Estimators
Domain Aggregation Networks for Multi-Source Domain Adaptation
Equivariant Neural Tangent Kernels
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
Predictive Linear Online Tracking for Unknown Targets
Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimization
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
No-Regret Learning in Extensive-Form Games with Imperfect Recall
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods
Predictive Performance of Deep Quantum Data Re-uploading Models
Stochastic Proximal Algorithms for AUC Maximization
A Nullspace Property for Subspace-Preserving Recovery
A Practical Algorithm for Topic Modeling with Provable Guarantees
Involutive MCMC: a Unifying Framework
Uncertainty Quantification for LLM-Based Survey Simulations
Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-Loop and Hessian-Free Solution Strategy
A rate-distortion one-class model and its applications to clustering
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
Geometric Hyena Networks for Large-scale Equivariant Learning
FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning
Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism
Workshop summary: The fourth workshop on evaluation methods for machine learning
Private Outsourced Bayesian Optimization
Efficient Learning of CNNs using Patch Based Features
Fair and Accurate Decision Making through Group-Aware Learning
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM
Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale
Constrained Variational Policy Optimization for Safe Reinforcement Learning
A Bayesian Wilcoxon signed-rank test based on the Dirichlet process
PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
Adversarial Attacks on Copyright Detection Systems
Calibrating Multimodal Learning
An Effective Dynamic Gradient Calibration Method for Continual Learning
Differentiable Quadratic Optimization For the Maximum Independent Set Problem
Abstracting from Robot Sensor Data using Hidden Markov Models
TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning
Multiply Robust Estimation for Local Distribution Shifts with Multiple Domains
Improving Compositional Generation with Diffusion Models Using Lift Scores
False Coverage Proportion Control for Conformal Prediction
Optimal LP Rounding and Linear-Time Approximation Algorithms for Clustering Edge-Colored Hypergraphs
Getting the most out of your tokenizer for pre-training and domain adaptation
Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy
Towards the Causal Complete Cause of Multi-Modal Representation Learning
Scaling Laws for Upcycling Mixture-of-Experts Language Models
On Some Misbehaviour of Back-Propagation with Non-Normalized RBFNs and a Solution
CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation
Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration
Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
HetSSNet: Spatial-Spectral Heterogeneous Graph Learning Network for Panchromatic and Multispectral Images Fusion
Understanding and Diagnosing Deep Reinforcement Learning
A Mixed-Curvature based Pre-training Paradigm for Multi-Task Vehicle Routing Solver
A Proxy Variable View of Shared Confounding
EvIL: Evolution Strategies for Generalisable Imitation Learning
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Regularizing Towards Soft Equivariance Under Mixed Symmetries
Scalable Structure Discovery in Regression using Gaussian Processes
Time-Aware World Model for Adaptive Prediction and Control
Generalization and Robustness of the Tilted Empirical Risk
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields
On the Use of Variational Inference for Learning Discrete Graphical Model
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data
Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner
Testing Conditional Mean Independence Using Generative Neural Networks
Risk and Regret of Hierarchical Bayesian Learners
Unscented Autoencoder
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum
Scalable and Efficient Comparison-based Search without Features
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
Deep Coordination Graphs
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
Integrating Prior Knowledge in Contrastive Learning with Kernel
Approximate Differential Privacy of the ℓ2 Mechanism
A Touch, Vision, and Language Dataset for Multimodal Alignment
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
Asymmetric Multi-task Learning based on Task Relatedness and Confidence
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Quasi-Monte Carlo Features for Kernel Approximation
Atari-5: Distilling the Arcade Learning Environment down to Five Games
Efficient lazy elimination for averaged one-dependence estimators
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization
Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language
Personalized Federated Learning under Mixture of Distributions
Cost-sensitive multi-class classification from probability estimates
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention
Polynomial Time Learning Augmented Algorithms for NP-hard Permutation Problems
Solving Zero-Sum Convex Markov Games
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring
GAODE and HAODE: two proposals based on AODE to deal with continuous variables
ELMO : Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces
Self-Damaging Contrastive Learning
Leveraging Good Representations in Linear Contextual Bandits
On Exact Bit-level Reversible Transformers Without Changing Architecture
AutoCoreset: An Automatic Practical Coreset Construction Framework
Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
PAK-UCB Contextual Bandit: An Online Learning Approach to Prompt-Aware Selection of Generative Models and LLMs
Adversarial Online Learning with noise
General Loss Bounds for Universal Sequence Prediction
Provable Benefit of Mixup for Finding Optimal Decision Boundaries
Enforcing Idempotency in Neural Networks
Discrete Latent Perspective Learning for Segmentation and Detection
S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction
Robust Policy Learning over Multiple Uncertainty Sets
Multi-task compressive sensing with Dirichlet process priors
X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
XAttention: Block Sparse Attention with Antidiagonal Scoring
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
Relative Loss Bounds for Temporal-Difference Learning
Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
Optimal Off-Policy Evaluation from Multiple Logging Policies
Controllable Prompt Tuning For Balancing Group Distributional Robustness
Completing Visual Objects via Bridging Generation and Segmentation
Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech
Using Determinations in EBL: A Solution to the incomplete Theory Problem
Learning nonparametric kernel matrices from pairwise constraints
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
Tilted Sparse Additive Models
Strongly Adaptive Online Learning
A New Branch-and-Bound Pruning Framework for ℓ0-Regularized Problems
Fixing the Loose Brake: Exponential-Tailed Stopping Time in Best Arm Identification
Transductive Learning via Spectral Graph Partitioning
Domain Generalisation via Imprecise Learning
A new Q(lambda) with interim forward view and Monte Carlo equivalence
Error Estimation for Sketched SVD via the Bootstrap
(How) Can Transformers Predict Pseudo-Random Numbers?
Online Linear Regression in Dynamic Environments via Discounting
Dynamic Regret of Strongly Adaptive Methods
Product of Experts with LLMs: Boosting Performance on ARC Is a Matter of Perspective
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Lenient Regret and Good-Action Identification in Gaussian Process Bandits
Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability
PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models
Deep Residual Output Layers for Neural Language Generation
Deep IV: A Flexible Approach for Counterfactual Prediction
Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?
STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving
Coresets for Vector Summarization with Applications to Network Graphs
Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling
The Well-Tempered Lasso
DMTG: One-Shot Differentiable Multi-Task Grouping
Transfer Learning In Differential Privacy's Hybrid-Model
Refining Minimax Regret for Unsupervised Environment Design
Distance Weighted Supervised Learning for Offline Interaction Data
Non-autoregressive Machine Translation with Disentangled Context Transformer
Distributed Bilevel Optimization with Communication Compression
Towards a Better Theoretical Understanding of Independent Subnetwork Training
Leveraging Offline Data in Linear Latent Contextual Bandits
Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning
Information-Theoretic Considerations in Batch Reinforcement Learning
Learning to Search with MCTSnets
StrassenNets: Deep Learning with a Multiplication Budget
Dealing with small data: On the generalization of context trees
Adaptive Gaussian Process Change Point Detection
Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
Deep Edge-Aware Filters
Learning from incomplete data with infinite imputations
Kernelizing PLS, degrees of freedom, and efficient model selection
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks
General Sequential Episodic Memory Model
Gaussian Processes on Cellular Complexes
Symbiosis in Multimodal Concept Learning
Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster L-/L ♮ -Convex Function Minimization
Linear Q-Learning Does Not Diverge in L2: Convergence Rates to a Bounded Set
Generalized Approximate Survey Propagation for High-Dimensional Estimation
Non-Linear Motor Control by Local Learning in Spiking Neural Networks
Understanding the Impact of Entropy on Policy Optimization
On Certifying Non-Uniform Bounds against Adversarial Attacks
Differentiable Linearized ADMM
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection
Towards Tight Bounds on the Sample Complexity of Average-reward MDPs
Towards Coherent and Consistent Use of Entities in Narrative Generation
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
Learning to Hash Robustly, Guaranteed
Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Provable Contrastive Continual Learning
Unifying the error-correcting and output-code AdaBoost within the margin framework
Improved Active Multi-Task Representation Learning via Lasso
DeepBach: a Steerable Model for Bach Chorales Generation
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Text-to-CAD Generation Through Infusing Visual Feedback in Large Language Models
Transformers Learn In-Context by Gradient Descent
Hierarchical Multi-Label Classification Networks
A permutation-augmented sampler for DP mixture models
Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads
Sum-of-Squares Polynomial Flow
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach
Automatic Construction of a Hierarchical Generate-and-Test Algorithm
Simple Black-box Adversarial Attacks
Learning Mean Field Control on Sparse Graphs
History-Guided Video Diffusion
Mapping the Multiverse of Latent Representations
MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking
Invariant-Equivariant Representation Learning for Multi-Class Data
Understanding the difficulties of posterior predictive estimation
Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design
Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs
Scribble-Supervised Semantic Segmentation with Prototype-based Feature Augmentation
Experiments with Noise Filtering in a Medical Domain
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Reinforcement Learning with Action-Free Pre-Training from Videos
Learning Independent Causal Mechanisms
Spectral feature selection for supervised and unsupervised learning
FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
Robust and Scalable Models of Microbiome Dynamics
K-Means Clustering with Distributed Dimensions
DetKDS: Knowledge Distillation Search for Object Detectors
Combining Multiple Learning Strategies for Effective Cross Validation
Discriminative structure and parameter learning for Markov logic networks
Offline Multi-Objective Optimization
HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking
Discovering Dynamics
Dimensionality Reduction on Complex Vector Spaces for Euclidean Distance with Dynamic Weights
Nonparametric Teaching for Graph Property Learners
Analyzing Dα seeding for k-means
Online Learning for Min Sum Set Cover and Pandora's Box
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Fenrir: Physics-Enhanced Regression for Initial Value Problems
Integer Programming for Causal Structure Learning in the Presence of Latent Variables
BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly
Massively Parallel k-Means Clustering for Perturbation Resilient Instances
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization
Flexible and Efficient Long-Range Planning Through Curious Exploration
Superhuman Fairness