yusuke1997/dblp-id-title huggingface dataset:
General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization
2D Conditional Random Fields for Web information extraction
Continual Learning Through Synaptic Intelligence
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE
Quantifying Prediction Consistency Under Fine-tuning Multiplicity in Tabular LLMs
Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction
KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning
Car-Traffic Forecasting: A Representation Learning Approach
Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks
Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback
Graph Generation with Diffusion Mixture
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
Semi-Supervised Learning via Compact Latent Space Clustering
SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers
The Computational Complexity of Finding Second-Order Stationary Points
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Position: What makes an image realistic?
Direct Uncertainty Prediction for Medical Second Opinions
Prototype-Anchored Learning for Learning with Imperfect Annotations
Beta Diffusion Trees
SpotEM: Efficient Video Search for Episodic Memory
Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms
It's Raw! Audio Generation with State-Space Models
The skew spectrum of graphs
Dynamic Sparse Training of Diagonally Sparse Networks
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy
AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion
Data-SUITE: Data-centric identification of in-distribution incongruous examples
EfficientNetV2: Smaller Models and Faster Training
MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent
Transformers as Algorithms: Generalization and Stability in In-context Learning
Learning Object Arrangements in 3D Scenes using Human Context
Towards Understanding Parametric Generalized Category Discovery on Graphs
Tightening Exploration in Upper Confidence Reinforcement Learning
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
Long Short-Term Memory Over Recursive Structures
Fast White-Box Adversarial Streaming Without a Random Oracle
A Discriminative Latent Variable Model for Online Clustering
Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games
Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots
Better Algorithms for Selective Sampling
Stereo Risk: A Continuous Modeling Approach to Stereo Matching
Efficient Stochastic Approximation of Minimax Excess Risk Optimization
Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL
Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
Selecting Representative Examples for Program Synthesis
Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
Optimization Equivalence of Divergences Improves Neighbor Embedding
An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective
Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise
Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation
Token-level Direct Preference Optimization
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds
Improving Out-of-Distribution Detection with Markov Logic Networks
Average Case Analysis of Learning kappa-CNF Concepts
Diversified Batch Selection for Training Acceleration
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Interchangeable Token Embeddings for Extendable Vocabulary and Alpha-Equivalence
More generality in efficient multiple kernel learning
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Gaussian process product models for nonparametric nonstationarity
Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Mutual Transfer Learning for Massive Data
Identifying Interpretable Subspaces in Image Representations
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions
Personalized Federated Learning using Hypernetworks
Learner-Private Convex Optimization
Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces
Foundations of Testing for Finite-Sample Causal Discovery
A Probabilistic Model for Dirty Multi-task Feature Selection
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Progress & Compress: A scalable framework for continual learning
Graph Minimum Factorization Distance and Its Application to Large-Scale Graph Data Clustering
Large margin training for hidden Markov models with partially observed states
Partial Optimality in the Linear Ordering Problem
Slice Sampling on Hamiltonian Trajectories
Variational Implicit Processes
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Efficient Algorithms for Robust One-bit Compressive Sensing
TextCenGen: Attention-Guided Text-Centric Background Adaptation for Text-to-Image Generation
Minimizing Trajectory Curvature of ODE-based Generative Models
Simple linear attention language models balance the recall-throughput tradeoff
What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?
The Emergence of Reproducibility and Consistency in Diffusion Models
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
Learning from Plausible Explanations
SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations
Generative Conditional Distributions by Neural (Entropic) Optimal Transport
Optimization with EM and Expectation-Conjugate-Gradient
Data Representations' Study of Latent Image Manifolds
Adaptive Sample Sharing for Multi Agent Linear Bandits
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
Implicit Regularization in Tensor Factorization
Detecting Motifs from Sequences
Learning Mixtures of Graphs from Epidemic Cascades
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG
Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot
How Do Adam and Training Strategies Help BNNs Optimization
Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm
Neural Network Poisson Models for Behavioural and Neural Spike Train Data
An Unsupervised Approach for Periodic Source Detection in Time Series
Discrete Neural Algorithmic Reasoning
Rethink DARTS Search Space and Renovate a New Benchmark
Large margin non-linear embedding
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Statistical Hypothesis Testing for Auditing Robustness in Language Models
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation
Action-Dependent Optimality-Preserving Reward Shaping
Infinite Action Contextual Bandits with Reusable Data Exhaust
A Deeper Look at Planning as Learning from Replay
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
A Theory of Justified Reformulations
LRA-QViT: Integrating Low-Rank Approximation and Quantization for Robust and Efficient Vision Transformers
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
Minimizing f-Divergences by Interpolating Velocity Fields
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
Language Models as Semantic Indexers
Multiclass core vector machine
Sample compression bounds for decision trees
TimeStacker: A Novel Framework with Multilevel Observation for Capturing Nonstationary Patterns in Time Series Forecasting
Nonparametric Embeddings of Sparse High-Order Interaction Events
Targeted control of fast prototyping through domain-specific interface
An Adaptive Regularization Criterion for Supervised Learning
On the Robustness of Randomized Ensembles to Adversarial Perturbations
SING: Spatial Context in Large Language Model for Next-Gen Wearables
Revisiting Non-Acyclic GFlowNets in Discrete Environments
Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints
Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
Is Feature Selection Secure against Training Data Poisoning?
INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
PAC Generalization via Invariant Representations
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis
Neural Collapse in Multi-label Learning with Pick-all-label Loss
Near-Optimal Sample Complexity for MDPs via Anchoring
Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems
Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement
Input Convex Neural Networks
On the Nonlinearity of Layer Normalization
Conceptual Set Covering: Improving Fit-And-Split Algorithms
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification
Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation
Exact Soft Confidence-Weighted Learning
Feature subset selection for learning preferences: a case study
Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization
Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
Temporal Misalignment in ANN-SNN Conversion and its Mitigation via Probabilistic Spiking Neurons
AST-T5: Structure-Aware Pretraining for Code Generation and Understanding
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry
Flow Matching for Few-Trial Neural Adaptation with Stable Latent Dynamics
Convolutional Sequence to Sequence Learning
Adapting to Delays and Data in Adversarial Multi-Armed Bandits
More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
The Diffusion Duality
Generated Graph Detection
Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
Modality-Agnostic Variational Compression of Implicit Neural Representations
Feature out! Let Raw Image as Your Condition for Blind Face Restoration
Hierarchical Refinement: Optimal Transport to Infinity and Beyond
Adversarial Collaborative Learning on Non-IID Features
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Spectral Compressed Sensing via Structured Matrix Completion
Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized
Position: Why Tabular Foundation Models Should Be a Research Priority
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
ReferSplat: Referring Segmentation in 3D Gaussian Splatting
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning
Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective
Prediction-Powered Adaptive Shrinkage Estimation
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
Learning Task Informed Abstractions
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Gamification of Pure Exploration for Linear Bandits
What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis
MMIHMM: Maximum Mutual Information Hidden Markov Models
Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
Robust and Conjugate Spatio-Temporal Gaussian Processes
Position: Insights from Survey Methodology can Improve Training Data
Symmetry-Robust 3D Orientation Estimation
One-sided Support Vector Regression for Multiclass Cost-sensitive Classification
KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies
Randomized Block-Diagonal Preconditioning for Parallel Learning
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
Identifying Cost Effective Boundaries of Operationality
An Information-Theoretic Analysis of Nonstationary Bandit Learning
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
Leveraging the margin more carefully
Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning
Online Learning for Active Cache Synchronization
Neural Message Passing for Quantum Chemistry
Action Elimination and Stopping Conditions for Reinforcement Learning
Learning the Dynamics of Sparsely Observed Interacting Systems
Out-of-sample extension of graph adjacency spectral embedding
The Illusion of Role Separation: Hidden Shortcuts in LLM Role Learning (and How to Fix Them)
On the Generalization Analysis of Adversarial Learning
Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view
Fast estimation of first-order clause coverage through randomization and maximum likelihood
Online Learning with Dependent Stochastic Feedback Graphs
Nested Sequential Monte Carlo Methods
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting
Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False
Complete-Tree Space Favors Data-Efficient Link Prediction
Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
Agnostic active learning
Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor Classifier and to Select Relevant Attributes
GLSearch: Maximum Common Subgraph Detection via Learning to Search
Neutral residues: revisiting adapters for model extension
Collaborative Causal Inference with Fair Incentives
The Performance Analysis of Generalized Margin Maximizers on Separable Data
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Bandits for BMO Functions
Optimal Survey Design for Private Mean Estimation
Off-policy Evaluation Beyond Overlap: Sharp Partial Identification Under Smoothness
SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator
Learning Stable Stochastic Nonlinear Dynamical Systems
Learnability of Augmented Naive Bayes in Nonimal Domains
Benchmarking Deletion Metrics with the Principled Explanations
Sparse Variational Inference for Generalized GP Models
Training Deep Convolutional Neural Networks to Play Go
Generalised Lipschitz Regularisation Equals Distributional Robustness
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
DFD: Distilling the Feature Disparity Differently for Detectors
Follow the Moving Leader in Deep Learning
Efficient Statistical Tests: A Neural Tangent Kernel Approach
Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features
Markov Network Estimation From Multi-attribute Data
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
A Parametric Contextual Online Learning Theory of Brokerage
Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions
Structured Policy Iteration for Linear Quadratic Regulator
Going Deeper into Permutation-Sensitive Graph Neural Networks
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem
Near-Optimal Design of Experiments via Regret Minimization
Regularization in directable environments with application to Tetris
A Quantification of Distance Bias Between Evaluation Metrics In Classification
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces
ESM All-Atom: Multi-Scale Protein Language Model for Unified Molecular Modeling
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
Improving Explanation-Based Indexing with Empirical Learning
VinePPO: Refining Credit Assignment in RL Training of LLMs
Poission Subsampled Rényi Differential Privacy
One-Pass AUC Optimization
Learning Extrapolative Sequence Transformations from Markov Chains
Diversified Adversarial Attacks based on Conjugate Gradient Method
Denoised MDPs: Learning World Models Better Than the World Itself
Integrating Learning in a Neural Network
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
StableMask: Refining Causal Masking in Decoder-only Transformer
The Implicit and Explicit Regularization Effects of Dropout
OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
Simple and Efficient Multiple Kernel Learning by Group Lasso
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time
Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
Statistical Theory of Generalization (Abstract)
Constrained Pareto Set Identification with Bandit Feedback
Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound
Geometry-aware metric learning
Fast Rate Analysis of Some Stochastic Optimization Algorithms
Calibrated Approximate Bayesian Inference
Enhancing Ligand Validity and Affinity in Structure-Based Drug Design with Multi-Reward Optimization
Short-lived High-volume Bandits
Bayesian Structural Adaptation for Continual Learning
Learning the Persistence of Actions in Reactive Control Rules
Energetic Natural Gradient Descent
Disentangling Trainability and Generalization in Deep Neural Networks
Choosing Between Two Learning Algorithms Based on Calibrated Tests
Tempering for Bayesian C&RT
Mutual Information in Learning Feature Transformations
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation
LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models
Generalization Analysis of Stochastic Weight Averaging with General Sampling
DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems
Least squares linear discriminant analysis
Training Neural Machines with Trace-Based Supervision
Bayesian regression with input noise for high dimensional data
Variational methods for the Dirichlet process
Deep Gaussian Markov Random Fields
Sort Before You Prune: Improved Worst-Case Guarantees of the DiskANN Family of Graphs
Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
TUMTraf VideoQA: Dataset and Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning
Automatic Identification of Mathematical Concepts
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value
Self-Interpretable Time Series Prediction with Counterfactual Explanations
Safety Certificate against Latent Variables with Partially Unidentifiable Dynamics
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
Generalization under Implication by Recursive Anti-unification
Hidden Markov Support Vector Machines
Tighter Analysis for ProxSkip
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
The power of first-order smooth optimization for black-box non-smooth problems
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes
Equivariant Priors for compressed sensing with unknown orientation
Mechanistic PDE Networks for Discovery of Governing Equations
Strategic Representation
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
SSL Enables Learning from Sparse Rewards in Image-Goal Navigation
A Boosting Approach to Topic Spotting on Subdialogues
PENCIL: Long Thoughts with Short Memory
LLark: A Multimodal Instruction-Following Language Model for Music
An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks
Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift
Towards Attributions of Input Variables in a Coalition
Deep Coherent Exploration for Continuous Control
Towards Compositionality in Concept Learning
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
Semi-supervised nonlinear dimensionality reduction
Adaptive Hamiltonian and Riemann Manifold Monte Carlo
Maximum Likelihood Estimation for Learning Populations of Parameters
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss
Sparse Gaussian Process Regression via L1 Penalization
Closed-form Solutions: A New Perspective on Solving Differential Equations
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry
Maximum margin planning
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks
Towards Neural Architecture Search through Hierarchical Generative Modeling
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Shortcut-connected Expert Parallelism for Accelerating Mixture of Experts
Riemannian Diffusion Adaptation for Distributed Optimization on Manifolds
Tensor Product Neural Networks for Functional ANOVA Model
Fast DPP Sampling for Nystrom with Application to Kernel Methods
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Understanding Instance-Level Label Noise: Disparate Impacts and Treatments
De novo mass spectrometry peptide sequencing with a transformer model
Structured agents for physical construction
Scaling Structured Inference with Randomization
Improving Viterbi is Hard: Better Runtimes Imply Faster Clique Algorithms
Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks
Unbiased Evaluation of Large Language Models from a Causal Perspective
Differentiable Top-k Classification Learning
Input Dependent Sparse Gaussian Processes
Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization
The Generality of Overgenerality
Differentiable Tree Operations Promote Compositional Generalization
Transformer Hawkes Process
CALM: Consensus-Aware Localized Merging for Multi-Task Learning