yusuke1997/dblp-id-title huggingface dataset:
SagaNet: A Small Sample Gated Network for Pediatric Cancer Diagnosis
Learning Dense Correspondences between Photos and Sketches
OneForecast: A Universal Framework for Global and Regional Weather Forecasting
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation
Stay on path: PCA along graph paths
Optimal Fair Learning Robust to Adversarial Distribution Shift
Understanding Noise Injection in GANs
Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm
Graying the black box: Understanding DQNs
Stable Offline Value Function Learning with Bisimulation-based Representations
Von Mises Mixture Distributions for Molecular Conformation Generation
Marginal Tail-Adaptive Normalizing Flows
Measures of diversity and space-filling designs for categorical data
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
Inverse problems with experiment-guided AlphaFold
A Variational Perspective on Generative Protein Fitness Optimization
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Learning Scale-Aware Spatio-temporal Implicit Representation for Event-based Motion Deblurring
Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
Spider: A Unified Framework for Context-dependent Concept Segmentation
Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning
Gossip-based distributed stochastic bandit algorithms
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
IT3: Idempotent Test-Time Training
Detecting Large-Scale System Problems by Mining Console Logs
Feature Selection as a One-Player Game
Delta Networks for Optimized Recurrent Network Computation
Analyzing Diffusion as Serial Reproduction
The Perception-Robustness Tradeoff in Deterministic Image Restoration
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations
Delaying the Choice of Bias: A Disjunctive Version Space Approach
Learning Policies with External Memory
Rethinking Addressing in Language Models via Contextualized Equivariant Positional Encoding
Challenges in Training PINNs: A Loss Landscape Perspective
Cooperative Open-ended Learning Framework for Zero-Shot Coordination
Pareto Optimal Streaming Unsupervised Classification
Understanding Int4 Quantization for Language Models: Latency Speedup, Composability, and Failure Cases
Towards Defending against Adversarial Examples via Attack-Invariant Features
Phase-aware Adversarial Defense for Improving Adversarial Robustness
Fast Peer Adaptation with Context-aware Exploration
On Mechanistic Knowledge Localization in Text-to-Image Generative Models
Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction
Second-order regression models exhibit progressive sharpening to the edge of stability
Query-level stability and generalization in learning to rank
Automatic variational inference with cascading flows
MiraGe: Editable 2D Images using Gaussian Splatting
OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable?
Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder
Efficient Bisection Projection to Ensure Neural-Network Solution Feasibility for Optimization over General Set
Safe Exploration in Markov Decision Processes
Visual and Domain Knowledge for Professional-level Graph-of-Thought Medical Reasoning
Multi-Session Budget Optimization for Forward Auction-based Federated Learning
Characterizing the Gap Between Actor-Critic and Policy Gradient
Learning Pseudo-Contractive Denoisers for Inverse Problems
AdvAgent: Controllable Blackbox Red-teaming on Web Agents
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination
Exploiting Shared Representations for Personalized Federated Learning
R2E: Turning any Github Repository into a Programming Agent Environment
Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble
Equivariance versus Augmentation for Spherical Images
Faster Algorithms for Binary Matrix Factorization
Generalized and Scalable Optimal Sparse Decision Trees
Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models
Textual Unlearning Gives a False Sense of Unlearning
Prior Knowledge Guided Neural Architecture Generation
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
Approximating Latent Manifolds in Neural Networks via Vanishing Ideals
FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
Retrieval-Augmented Score Distillation for Text-to-3D Generation
Offline Meta-Reinforcement Learning with Advantage Weighting
Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models
Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time
Accelerating Unbiased LLM Evaluation via Synthetic Feedback
Positive and Unlabeled Learning with Controlled Probability Boundary Fence
Adaptive Compositional Continual Meta-Learning
A Hybrid Algorithm for Convex Semidefinite Optimization
Adaptive Coordination in Social Embodied Rearrangement
Provably Scalable Black-Box Variational Inference with Structured Variational Families
Nonlinear independent component analysis with minimal nonlinear distortion
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Learning multivariate Gaussians with imperfect advice
DAVINZ: Data Valuation using Deep Neural Networks at Initialization
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning
Iterative Regularized Policy Optimization with Imperfect Demonstrations
Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition
Generalized Preference Optimization: A Unified Approach to Offline Alignment
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?
Variance Function Estimation in High-dimensions
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks
A recursive method for discriminative mixture learning
Understanding Mode Connectivity via Parameter Space Symmetry
Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
Thompson Sampling for Robust Transfer in Multi-Task Bandits
Learning Graph Models of Shape
Infinite-Horizon Distributionally Robust Regret-Optimal Control
Structured Learning from Partial Annotations
On Learning Invariant Representations for Domain Adaptation
Pairwise Alignment Improves Graph Domain Adaptation
Phasic Policy Gradient
A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference Learning
Improving Gradient Regularization using Complex-Valued Neural Networks
XOR-CD: Linearly Convergent Constrained Structure Generation
Making transport more robust and interpretable by moving data through a small number of anchor points
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications
Meta-Reinforcement Learning with Adaptation from Human Feedback via Preference-Order-Preserving Task Embedding
Informative Discriminant Analysis
Power Iteration Clustering
Training-Free Neural Active Learning with Initialization-Robustness Guarantees
Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
GALAXY: Graph-based Active Learning at the Extreme
Distributionally Robust Policy Learning under Concept Drifts
Towards Understanding Catastrophic Forgetting in Two-layer Convolutional Neural Networks
Memory bounded inference in topic models
Conversational Speech Transcription Using Context-Dependent Deep Neural Networks
From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining
PGODE: Towards High-quality System Dynamics Modeling
Learning to Optimize Combinatorial Functions
When Are Linear Stochastic Bandits Attackable?
Hybrid Spiking Vision Transformer for Object Detection with Event Cameras
A Deep Conjugate Direction Method for Iteratively Solving Linear Systems
Non-monotonic feature selection
Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Supervised Learning and Divide-and-Conquer: A Statistical Approach
Multi-View Clustering by Inter-cluster Connectivity Guided Reward
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Conditional Accelerated Lazy Stochastic Gradient Descent
Is Learning Summary Statistics Necessary for Likelihood-free Inference?
Correlated Quantization for Distributed Mean Estimation and Optimization
Learning Bayesian network classifiers by maximizing conditional likelihood
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
Confidence-aware Contrastive Learning for Selective Classification
Maximum-and-Concatenation Networks
High-Fidelity Simultaneous Speech-To-Speech Translation
Structured Chemistry Reasoning with Large Language Models
Mining Both Positive and Negative Association Rules
Greedy Column Subset Selection: New Bounds and Distributed Algorithms
Diffusion Models for Black-Box Optimization
Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
Statistical Test for Feature Selection Pipelines by Selective Inference
CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities
Adjustment for Confounding using Pre-Trained Representations
Ergodic Generative Flows
A Hill-Climbing Approach to Machine Discovery
Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees
Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization
Rectangular Tiling Process
Anytime Information Cascade Popularity Prediction via Self-Exciting Processes
ContPhy: Continuum Physical Concept Learning and Reasoning from Videos
Improving Graph Generation by Restricting Graph Bandwidth
Decoding-time Realignment of Language Models
Repository-Level Prompt Generation for Large Language Models of Code
Mixture of Mutually Exciting Processes for Viral Diffusion
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
Generative Adversarial Symmetry Discovery
Efficient hierarchical MCMC for policy search
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
Time Series Diffusion in the Frequency Domain
An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
Learning to Match Unpaired Data with Minimum Entropy Coupling
Privacy Preserving Adaptive Experiment Design
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
GuardAgent: Safeguard LLM Agents via Knowledge-Enabled Reasoning
The Information-Theoretic Requirements of Subspace Clustering with Missing Data
Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer
Magnetic Hamiltonian Monte Carlo
Learning Gaussian processes from multiple tasks
Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
Learning Representations of Instruments for Partial Identification of Treatment Effects
Neural Operators with Localized Integral and Differential Kernels
Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales
The graphlet spectrum
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
Fusing Reward and Dueling Feedback in Stochastic Bandits
Maximum Selection and Ranking under Noisy Comparisons
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
Self-supervised Masked Graph Autoencoder via Structure-aware Curriculum
Gaussian Mixture Flow Matching Models
D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples
Score-of-Mixture Training: One-Step Generative Model Training Made Simple via Score Estimation of Mixture Distributions
A Trichotomy for List Transductive Online Learning
The Interdependencies of Theory Formation, Revision, and Experimentation
Community Recovery in Graphs with Locality
Generalization of noisy SGD in unbounded non-convex settings
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes
Inducing, Detecting and Characterising Neural Modules: A Pipeline for Functional Interpretability in Reinforcement Learning
Generative Intervention Models for Causal Perturbation Modeling
Stochastic Variational Inference for Bayesian Time Series Models
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
ChaCha for Online AutoML
Learning Gradient Descent: Better Generalization and Longer Horizons
LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
On the Generalization Effects of Linear Transformations in Data Augmentation
Temporally Consistent Transformers for Video Generation
An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems
Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
Partially Stochastic Infinitely Deep Bayesian Neural Networks
The Bigraphical Lasso
Simple and sharp analysis of k-means||
Towards Robustness and Explainability of Automatic Algorithm Selection
Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation
Emphatic Algorithms for Deep Reinforcement Learning
Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Geometry Score: A Method For Comparing Generative Adversarial Networks
Joining datasets via data augmentation in the label space for neural networks
Distributed Differentially Private Data Analytics via Secure Sketching
A Computational Model of Acquisition for Children's Addtion Strategies
LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration
Parameterized Rate-Distortion Stochastic Encoder
Regularization-free Diffeomorphic Temporal Alignment Nets
Training High Performance Spiking Neural Network by Temporal Model Calibration
Learning to Share Distributed Probabilistic Beliefs
Unifying collaborative and content-based filtering
Explanations for Monotonic Classifiers
Autoencoding beyond pixels using a learned similarity metric
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Fast Adversarial Attacks on Language Models In One GPU Minute
Causal Isotonic Calibration for Heterogeneous Treatment Effects
Cross-Domain Policy Adaptation by Capturing Representation Mismatch
Learning approximate preconditions for methods in hierarchical plans
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Noise2Noise: Learning Image Restoration without Clean Data
What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities
Deep symbolic regression for recurrence prediction
Predictive Coding for Locally-Linear Control
New Oracle-Efficient Algorithms for Private Synthetic Data Release
Being Properly Improper
Learning Character-level Representations for Part-of-Speech Tagging
Learning Radial Basis Function Networks On-line
QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache
Safe Reinforcement Learning with Linear Function Approximation
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
ODS: Test-Time Adaptation in the Presence of Open-World Data Shift
Online Bayesian Moment Matching based SAT Solver Heuristics
Bucket Renormalization for Approximate Inference
Stochastic search using the natural gradient
PAPRIKA: Private Online False Discovery Rate Control
Geometric Conditions for Subspace-Sparse Recovery
Learning Convolutional Neural Networks for Graphs
A Persuasive Approach to Combating Misinformation
Do Multiple Instance Learning Models Transfer?
Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
On the Diversity of Adversarial Ensemble Learning
MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
BORE: Bayesian Optimization by Density-Ratio Estimation
A General Framework for Inference-time Scaling and Steering of Diffusion Models
Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions
Large-Scale Sparse Kernel Canonical Correlation Analysis
Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
Convergence guarantees for a class of non-convex and non-smooth optimization problems
Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
Reinforced Lifelong Editing for Language Models
Multi-objective Bandits: Optimizing the Generalized Gini Index
Collective Model Fusion for Multiple Black-Box Experts
ABC-boost: adaptive base class boost for multi-class classification
Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
Distribution Regression with Sliced Wasserstein Kernels
e-GAI: e-value-based Generalized α-Investing for Online False Discovery Rate Control
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Multi-Task Off-Policy Learning from Bandit Feedback
A theoretical characterization of linear SVM-based feature selection
Learning to Search Better than Your Teacher
Sharper Generalization Bounds for Clustering
Sampling from Binary Quadratic Distributions via Stochastic Localization
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
Residual-Based Sampling for Online Outlier-Robust PCA
When do Minimax-fair Learning and Empirical Risk Minimization Coincide?
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use
Differentiating through the Fréchet Mean
Locally Regularized Neural Differential Equations: Some Black Boxes were meant to remain closed!
Towards Cost-Effective Reward Guided Text Generation
MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
Few-Shot Learner Generalizes Across AI-Generated Image Detection
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems
Equivariant Diffusion for Crystal Structure Prediction
polybasic Speculative Decoding Through a Theoretical Perspective
Update Your Transformer to the Latest Release: Re-Basin of Task Vectors
Deep Counterfactual Regret Minimization
Scaling the Indian Buffet Process via Submodular Maximization
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formula
Stochastic Flows and Geometric Optimization on the Orthogonal Group
Learning Hawkes Processes Under Synchronization Noise
A Linear Dynamical System Model for Text
An Evaluation on Feature Selection for Text Clustering
Fast incremental proximity search in large graphs
On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective
PAC Learnability of Node Functions in Networked Dynamical Systems
Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
On the Sample Complexity of Reinforcement Learning with a Generative Model
SpeqNets: Sparsity-aware permutation-equivariant graph networks
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression
Benign Overfitting in Adversarial Training of Neural Networks
Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions
Zero-Shot Generalization of GNNs over Distinct Attribute Domains
Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures
On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting
Scaling Up Robust MDPs using Function Approximation
Two Tales of Single-Phase Contrastive Hebbian Learning
Model-Free Robust Average-Reward Reinforcement Learning
Subhomogeneous Deep Equilibrium Models
A Study on the Ramanujan Graph Property of Winning Lottery Tickets
Efficient Distributed Optimization under Heavy-Tailed Noise
Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity
Learning Predictive State Representations
AutoCATE: End-to-End, Automated Treatment Effect Estimation
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Quantile Regression for Large-scale Applications
Self-taught clustering
Lyapunov-Constrained Action Sets for Reinforcement Learning
Mimetic Initialization of Self-Attention Layers
Online Clustering of Bandits
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
STRODE: Stochastic Boundary Ordinary Differential Equation
Vision-Language Model Selection and Reuse for Downstream Adaptation
Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
PPFLOW: Target-Aware Peptide Design with Torsional Flow Matching
PAC learning of Probabilistic Automaton based on the Method of Moments
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Learning to Program by Examining and Modifying Cases
Hierarchical maximum entropy density estimation
Unified Screening for Multiple Diseases
Unifying Vision-and-Language Tasks via Text Generation
Mirror, Mirror of the Flow: How Does Regularization Shape Implicit Bias?
Optimal Thompson Sampling strategies for support-aware CVaR bandits
Linearized cluster assignment via spectral ordering
Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance
Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization
High-dimensional Robust Mean Estimation via Gradient Descent
Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience
FedMBridge: Bridgeable Multimodal Federated Learning
Function-space Inference with Sparse Implicit Processes
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
Adversarial Purification with Score-based Generative Models
Position: Do Not Explain Vision Models Without Context
A Reinforcement Learning Framework for Dynamic Mediation Analysis