yusuke1997/dblp-id-title huggingface dataset:
On the Clean Generalization and Robust Overfitting in Adversarial Training from Two Theoretical Views: Representation Complexity and Training Dynamics
Improving Memory Efficiency for Training KANs via Meta Learning
Learning a meta-level prior for feature relevance from multiple related tasks
Learning Output Kernels with Block Coordinate Descent
Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity
A distance model for rhythms
CLOVER: Cross-Layer Orthogonal Vectors Pruning
Simpler core vector machines with enclosing balls
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
KernelBench: Can LLMs Write Efficient GPU Kernels?
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Personalized Federated Learning with Inferred Collaboration Graphs
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
Event-Customized Image Generation
Componential Prompt-Knowledge Alignment for Domain Incremental Learning
A Tighter Analysis of Spectral Clustering, and Beyond
Commute Graph Neural Networks
Compressed Sensing using Generative Models
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
Efficient Dimensionality Reduction for High-Dimensional Network Estimation
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis
Scalable Equilibrium Sampling with Sequential Boltzmann Generators
Tuning LLM Judge Design Decisions for 1/1000 of the Cost
LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression
Predictive Performance Comparison of Decision Policies Under Confounding
Stable Function Approximation in Dynamic Programming
Learning Long Term Dependencies via Fourier Recurrent Units
Learning to Reach Goals via Diffusion
A Fast and Exact Energy Minimization Algorithm for Cycle MRFs
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation
Optimal Randomized First-Order Methods for Least-Squares Problems
Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation
Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning
Robust Representation Learning via Perceptual Similarity Metrics
Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
A Non-isotropic Time Series Diffusion Model with Moving Average Transitions
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Train faster, generalize better: Stability of stochastic gradient descent
FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees
Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers
Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond
Instance Correlation Graph-based Naive Bayes
Interferometric Graph Transform: a Deep Unsupervised Graph Representation
Unsupervised Image Representation Learning with Deep Latent Particles
Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning
Bayesian Active Learning for Bivariate Causal Discovery
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains
Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design
Nonparametric variational inference
Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic
Model Selection in Unsupervised Learning with Applications To Document Clustering
Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models
Balanced Learning for Domain Adaptive Semantic Segmentation
MERGE3: Efficient Evolutionary Merging on Consumer-grade GPUs
Accelerated Primal-Dual Methods for Convex-Strongly-Concave Saddle Point Problems
Learning with Abandonment
Complete Dictionary Recovery Using Nonconvex Optimization
An analysis of reinforcement learning with function approximation
Dissecting Supervised Constrastive Learning
Issues in the Justification-Based Diagnosis of Planning Failures
SLAMB: Accelerated Large Batch Training with Sparse Communication
Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
Learning Appropriate Abstractions for Planning in Formation Problems
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning
Surrogate regret bounds for proper losses
Function-Space Learning Rates
Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
Reinforcement learning with Gaussian processes
Transductive support vector machines for structured variables
Projective Preferential Bayesian Optimization
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
PointMC: Multi-instance Point Cloud Registration based on Maximal Cliques
Position: Optimization in SciML Should Employ the Function Space Geometry
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers
Adapting to Mixing Time in Stochastic Optimization with Markovian Data
DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks
Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Mixed-curvature decision trees and random forests
Self-similar Epochs: Value in arrangement
Variational Dropout Sparsifies Deep Neural Networks
Validating Causal Inference Models via Influence Functions
RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations
Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data
Two-Stage Learning Kernel Algorithms
Sample-based learning and search with permanent and transient memories
Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks
A Lens into Interpretable Transformer Mistakes via Semantic Dependency
Unsupervised Sequence Segmentation by a Mixture of Switching Variable Memory Markov Sources
Quasi-Monte Carlo Variational Inference
Convergence, Targeted Optimality, and Safety in Multiagent Learning
Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design
Variance Reduced Training with Stratified Sampling for Forecasting Models
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback
DCM Bandits: Learning to Rank with Multiple Clicks
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model
Rich Component Analysis
Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
Aligning LLMs by Predicting Preferences from User Writing Samples
Variable Resolution Dynamic Programming
Learning with Partial-Label and Unlabeled Data: A Uniform Treatment for Supervision Redundancy and Insufficiency
Concurrent Shuffle Differential Privacy Under Continual Observation
DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
Exploiting Presentative Feature Distributions for Parameter-Efficient Continual Learning of Large Language Models
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm
Generative Coarse-Graining of Molecular Conformations
Improved nearest neighbor search using auxiliary information and priority functions
Anarchic Federated Learning
Profile Reconstruction from Private Sketches
Autoencoding Under Normalization Constraints
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Proportionally Fair Clustering
Fast Online Node Labeling for Very Large Graphs
Towards efficient deep spiking neural networks construction with spiking activity based pruning
Noise2Self: Blind Denoising by Self-Supervision
The Consistent Concept Axiom
In-Context Principle Learning from Mistakes
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier
SquareχPO: Differentially Private and Robust χ2-Preference Optimization in Offline Direct Alignment
Markovian Gaussian Process Variational Autoencoders
In-Database Regression in Input Sparsity Time
Sparse Invariant Risk Minimization
Disambiguation of Weak Supervision leading to Exponential Convergence rates
Provable Smoothness Guarantees for Black-Box Variational Inference
Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits
Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data
SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models
BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization
The Power of Uniform Sampling for k-Median
SAAL: Sharpness-Aware Active Learning
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
Half-Hop: A graph upsampling approach for slowing down message passing
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference
A Unified Framework for Consistency of Regularized Loss Minimizers
An Efficient Semismooth Newton Based Algorithm for Convex Clustering
LineFlow: A Framework to Learn Active Control of Production Lines
Looped Transformers as Programmable Computers
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Zero-Shot Reinforcement Learning via Function Encoders
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms
Online Resource Allocation with Non-Stationary Customers
On the Within-Group Fairness of Screening Classifiers
Improving Factuality and Reasoning in Language Models through Multiagent Debate
BL-WoLF: A Framework For Loss-Bounded Learnability In Zero-Sum Games
Computational aspects of Bayesian partition models
Towards Understanding Ensemble Distillation in Federated Learning
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space
Position: The Reasonable Person Standard for AI
Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
What Makes a Good Feedforward Computational Graph?
Projection onto Minkowski Sums with Application to Constrained Learning
Convergence of Uncertainty Sampling for Active Learning
Predicting deliberative outcomes
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing
A simpler approach to accelerated optimization: iterative averaging meets optimism
Multi-Frequency Vector Diffusion Maps
A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars
Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis
Weakly Supervised Anomaly Detection via Dual-Tailed Kernel
Neuro-Symbolic Temporal Point Processes
Oracle-MoE: Locality-preserving Routing in the Oracle Space for Memory-constrained Large Language Model Inference
Overcoming Vocabulary Mismatch: Vocabulary-agnostic Teacher Guided Language Modeling
Co-Representation Network for Generalized Zero-Shot Learning
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
I-BERT: Integer-only BERT Quantization
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
Local vs. Global Interpretability: A Computational Complexity Perspective
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems
CSV-Occ: Fusing Multi-frame Alignment for Occupancy Prediction with Temporal Cross State Space Model and Central Voting Mechanism
Contrastive Learning with Boosted Memorization
Test-Time Graph Neural Dataset Search With Generative Projection
Online kernel selection for Bayesian reinforcement learning
Submodularity in Data Subset Selection and Active Learning
Latent Mamba Operator for Partial Differential Equations
Probabilistic Factorial Experimental Design for Combinatorial Interventions
Towards Modular LLMs by Building and Reusing a Library of LoRAs
Autoformalizing Euclidean Geometry
Theoretically Principled Trade-off between Robustness and Accuracy
Modeling Interaction via the Principle of Maximum Causal Entropy
PFNs4BO: In-Context Learning for Bayesian Optimization
On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks
A Tree-Structured Decoder for Image-to-Markup Generation
Exploring Vision Semantic Prompt for Efficient Point Cloud Understanding
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling
Decoupling Representation Learning from Reinforcement Learning
A DC-programming algorithm for kernel selection
Generating Private Synthetic Data with Genetic Algorithms
Transfer Learning for Prostate Cancer Mapping Based on Multicentric MR Imaging Databases
Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward
SIN: Selective and Interpretable Normalization for Long-Term Time Series Forecasting
LIV: Language-Image Representations and Rewards for Robotic Control
Variance Reduction for Faster Non-Convex Optimization
Undirected Graphical Models as Approximate Posteriors
Learning Linear-Quadratic Regulators Efficiently with only √T Regret
Permutation-Free High-Order Interaction Tests
Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
Randomized Confidence Bounds for Stochastic Partial Monitoring
Global graph kernels using geometric embeddings
First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems
In-Context Reinforcement Learning From Suboptimal Historical Data
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss
Meta-Reinforcement Learning Robust to Distributional Shift Via Performing Lifelong In-Context Learning
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
A Statistical Framework for Data-dependent Retrieval-Augmented Models
Demonstration Selection for In-Context Learning via Reinforcement Learning
Learning Stochastic Shortest Path with Linear Function Approximation
Amortizing Pragmatic Program Synthesis with Rankings
FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
The Value of Prediction in Identifying the Worst-Off
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute
Fast Stochastic Alternating Direction Method of Multipliers
Bellman goes relational
RMIB: Representation Matching Information Bottleneck for Matching Text Representations
Stochastic Sign Descent Methods: New Algorithms and Better Theory
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence
"Other-Play" for Zero-Shot Coordination
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Average Certified Radius is a Poor Metric for Randomized Smoothing
Automatic Speaker Recognition: An Application of Machine Learning
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Universal Matrix Completion
Sleeping Reinforcement Learning
Logarithmic Regret for Adversarial Online Control
Imitation Learning in Discounted Linear MDPs without exploration assumptions
Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
Accurate Quantization of Measures via Interacting Particle-based Optimization
FlipAttack: Jailbreak LLMs via Flipping
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
Best of both: a hybridized centroid-medoid clustering heuristic
Wasserstein of Wasserstein Loss for Learning Generative Models
Regularising Non-linear Models Using Feature Side-information
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games
SHARP-Distill: A 68× Faster Recommender System with Hypergraph Neural Networks and Language Models
Self-supervised Adversarial Purification for Graph Neural Networks
On Prompt-Driven Safeguarding for Large Language Models
Understanding Unimodal Bias in Multimodal Deep Linear Networks
Learning the Grammar of Dance
Random Scaling and Momentum for Non-smooth Non-convex Optimization
A Teaching Method for Reinforcement Learning
Trust-Region Twisted Policy Improvement
SGLB: Stochastic Gradient Langevin Boosting
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
The Relative Value of Prediction in Algorithmic Decision Making
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
Learning Dynamics and Generalization in Deep Reinforcement Learning
SurfPro: Functional Protein Design Based on Continuous Surface
Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning
Understanding Self-Predictive Learning for Reinforcement Learning
Training Normalizing Flows from Dependent Data
Towards Reliable Neural Specifications
Trust the Model Where It Trusts Itself - Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption
Latent Thought Models with Variational Bayes Inference-Time Computation
Extreme Multi-label Classification from Aggregated Labels
Pathwise Derivatives Beyond the Reparameterization Trick
Sample as you Infer: Predictive Coding with Langevin Dynamics
Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering
Convolutional Rectifier Networks as Generalized Tensor Decompositions
Approximating a Distribution Using Weight Queries
Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks
Uncertainty Principles of Encoding GANs
Adversarial Risk via Optimal Transport and Optimal Couplings
MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding
Transfer Learning for Nonparametric Contextual Dynamic Pricing
Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal
Learning Likelihood-Free Reference Priors
Active Learning for Multi-Task Adaptive Filtering
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
From Thousands to Billions: 3D Visual Language Grounding via Render-Supervised Distillation from 2D VLMs
Skill Discovery for Exploration and Planning using Deep Skill Graphs
Hierarchical Graph Tokenization for Molecule-Language Alignment
Language Models with Conformal Factuality Guarantees
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
Noisy Sparse Subspace Clustering
Deep Regression Representation Learning with Topology
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Information Flow in Self-Supervised Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
Breaking the Deadly Triad with a Target Network
A Global Geometric Analysis of Maximal Coding Rate Reduction
Tracking the Best Expert
A Probabilistic Approach to Neural Network Pruning
Transfer of Learning Across Compositions of Sequentail Tasks
Optimal Kernel Quantile Learning with Random Features
Multimodal oriented discriminant analysis
A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits
Learning Policies for Contextual Submodular Prediction
GenLabel: Mixup Relabeling using Generative Models
A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency
Importance Sampling for Nonlinear Models
Modeling Correlated Arrival Events with Latent Semi-Markov Processes
Submodular Cost Submodular Cover with an Approximate Oracle
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Compositional Text-to-Image Generation with Dense Blob Representations
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
Causal filter selection in microarray data
Grokking Beyond the Euclidean Norm of Model Parameters
A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Learning to Explore via Meta-Policy Gradient
Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning
Retraining-free Merging of Sparse MoE via Hierarchical Clustering
Normalizing Flows on Tori and Spheres
Learning the Linear Dynamical System with ASOS
Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation
Tensor Decomposition via Simultaneous Power Iteration
Teaching an Agent to Test Students
Binary Partitions with Approximate Minimum Impurity
Unified Scaling Laws for Routed Language Models
Retrieval-Augmented Language Model for Knowledge-aware Protein Encoding
Fast Sampling of Diffusion Models via Operator Learning
Inverse Bridge Matching Distillation
An Alternative View: When Does SGD Escape Local Minima?
Learning Bayesian Networks for Diverse and Varying numbers of Evidence Sets
The Price of Differential Privacy under Continual Observation
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning
Probabilistic Partial Canonical Correlation Analysis
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
Fast Algorithms for Segmented Regression
Secure Distributed Training at Scale
Statistical Properties of Robust Satisficing
Escaping Saddle Points with Adaptive Gradient Methods
Incremental Multiple Concept Learning Using Experiments
Neuro-algorithmic Policies Enable Fast Combinatorial Generalization
Improving Probabilistic Grammatical Inference Core Algorithms with Post-processing Techniques
Multi-class protein fold recognition using adaptive codes
Probabilistic Matrix Factorization with Non-random Missing Data
Predicting Ordinary Differential Equations with Transformers
Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Learning and Evaluating Contextual Embedding of Source Code
Robust Collaborative Learning with Linear Gradient Overhead
Planning Approximate Plans for Use in the Real World
Iterative Approximate Cross-Validation
When do neural networks learn world models?
Efficient Variance Reduction for Meta-learning
Generalized Neural Collapse for a Large Number of Classes
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
Towards Realistic Model Selection for Semi-supervised Learning
Celeste: Variational inference for a generative model of astronomical images
Probabilistic dyadic data analysis with local and global consistency