yusuke1997/dblp-id-title huggingface dataset:
Learning Hierarchical Structures with Linear Relational Embedding
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
Selecting causal brain features with a single conditional independence test per feature
Are Anchor Points Really Indispensable in Label-Noise Learning?
Double/Debiased Machine Learning for Dynamic Treatment Effects
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
Proving Theorems Recursively
Non-Asymptotic Analysis of a UCB-based Top Two Algorithm
Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers
Accelerating Exploration with Unlabeled Prior Data
Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups
Learning to Detect Objects with a 1 Megapixel Event Camera
Lazy and Fast Greedy MAP Inference for Determinantal Point Process
Variance Penalizing AdaBoost
Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
Topographic Map Formation by Silicon Growth Cones
Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation
On Tractable Φ-Equilibria in Non-Concave Games
Imitation Learning from Imperfection: Theoretical Justifications and Algorithms
Blocking Bandits
Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
PAC Generalization Bounds for Co-training
MetaGrad: Multiple Learning Rates in Online Learning
Improving Committee Diagnosis with Resampling Techniques
Learning Networks of Heterogeneous Influence
Searching for Better Spatio-temporal Alignment in Few-Shot Action Recognition
Universal Correspondence Network
On Power Laws in Deep Ensembles
Triangulation candidates for Bayesian optimization
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Piecewise deterministic generative models
Consistent Binary Classification with Generalized Performance Metrics
Harnessing Multiple Correlated Networks for Exact Community Recovery
Anticipating Performativity by Predicting from Predictions
Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Causality: Objectives and Assessment (NIPS 2008 Workshop), Whistler, Canada, December 12, 2008
Modeling Noisy Annotations for Crowd Counting
Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
Is Out-of-Distribution Detection Learnable?
Unadversarial Examples: Designing Objects for Robust Vision
Equivariant Neural Diffusion for Molecule Generation
Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding
A Lagrangian Formulation For Optical Backpropagation Training In Kerr-Type Optical Networks
Optimal Testing for Properties of Distributions
On the Convergence of Encoder-only Shallow Transformers
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
Adaptive Online Estimation of Piecewise Polynomial Trends
Efficient Sampling for Learning Sparse Additive Models in High Dimensions
A Fourier Approach to Mixture Learning
Differentially Private Generalized Linear Models Revisited
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
Neurobiology, Psychophysics, and Computational Models of Visual Attention
A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem
Beyond Slow Signs in High-fidelity Model Extraction
A neural network implementing optimal state estimation based on dynamic spike train decoding
VIBES: A Variational Inference Engine for Bayesian Networks
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering
Attentional Processing on a Spike-Based VLSI Neural Network
Learning rigid-body simulators over implicit shapes for large-scale scenes and vision
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning
Towards Tight Communication Lower Bounds for Distributed Optimisation
Dynamic Behavior of Constained Back-Propagation Networks
Policy-shaped prediction: avoiding distractions in model-based reinforcement learning
Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning
Dynamic Pricing and Learning with Bayesian Persuasion
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
Test-time Collective Prediction
AttCAT: Explaining Transformers via Attentive Class Activation Tokens
Coresets for Clustering with Missing Values
High-Rank Matrix Completion and Clustering under Self-Expressive Models
Theories of Access Consciousness
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
Group and Shuffle: Efficient Structured Orthogonal Parametrization
Designing Robust Transformers using Robust Kernel Density Estimation
Instance-based Learning for Knowledge Base Completion
MindMerger: Efficiently Boosting LLM Reasoning in non-English Languages
In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates
Does enforcing fairness mitigate biases caused by subpopulation shift?
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks
Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks
Lower Bounds on Adaptive Sensing for Matrix Recovery
Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting
PointMamba: A Simple State Space Model for Point Cloud Analysis
The Animal-AI Testbed and Competition
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
Defining and Characterizing Reward Gaming
Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs
Higher-Order Factorization Machines
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
Programmable Reinforcement Learning Agents
A realistic approach to generate masked faces applied on two novel masked face recognition data sets
Video Diffusion Models are Training-free Motion Interpreter and Controller
Source Separation as a By-Product of Regularization
Measuring Mathematical Problem Solving With the MATH Dataset
The computational and learning benefits of Daleian neural networks
The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes
Private learning implies quantum stability
Information Maximization Perspective of Orthogonal Matching Pursuit with Applications to Explainable AI
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models
Dynamic Time-Alignment Kernel in Support Vector Machine
Crowdsourced Clustering: Querying Edges vs Triangles
HARMONET: A Neural Net for Harmonizing Chorales in the Style of J. S. Bach
Sparse PCA with Oracle Property
Hyper-Skin: A Hyperspectral Dataset for Reconstructing Facial Skin-Spectra from RGB Images
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models
Understanding Partial Multi-Label Learning via Mutual Information
Generalized Denoising Auto-Encoders as Generative Models
Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Value-driven Hindsight Modelling
Graph Differentiable Architecture Search with Structure Learning
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser
Linear Multi-Resource Allocation with Semi-Bandit Feedback
Machine Learning Applied to Perception: Decision Images for Gender Classification
On Stochastic and Worst-case Models for Investing
A memory frontier for complex synapses
Littlestone Classes are Privately Online Learnable
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks
Salient Contour Extraction by Temporal Binding in a Cortically-based Network
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
Off-Policy Evaluation via the Regularized Lagrangian
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
Polynomial-Time Computation of Exact $\Phi$-Equilibria in Polyhedral Games
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions
Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations
DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translation
Deep Neural Networks for Object Detection
Orthogonal Transformer: An Efficient Vision Transformer Backbone with Token Orthogonalization
Diving into the shallows: a computational perspective on large-scale shallow learning
Partitioning Structure Learning for Segmented Linear Regression Trees
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification
When are Offline Two-Player Zero-Sum Markov Games Solvable?
Robust Imitation of a Few Demonstrations with a Backwards Model
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
Optimal Rates for Bandit Nonstochastic Control
Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment
Independent Components Analysis through Product Density Estimation
Experimental Design for Cost-Aware Learning of Causal Graphs
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
OpenProteinSet: Training data for structural biology at scale
Kernel-ARMA for Hand Tracking and Brain-Machine interfacing During 3D Motor Control
People Tracking with the Laplacian Eigenmaps Latent Variable Model
Only H is left: Near-tight Episodic PAC RL
Efficient Low-rank Backpropagation for Vision Transformer Adaptation
Moser Flow: Divergence-based Generative Modeling on Manifolds
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Self-Supervised Graph Transformer on Large-Scale Molecular Data
Sequential Harmful Shift Detection Without Labels
Calibration tests in multi-class classification: A unifying framework
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
Can Adversarial Training Be Manipulated By Non-Robust Features?
Trimmed Density Ratio Estimation
Image Segmentation with Networks of Variable States
Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
Recommender Forest for Efficient Retrieval
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance
Linear Program Approximations for Factored Continuous-State Markov Decision Processes
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels
Object-aware Contrastive Learning for Debiased Scene Representation
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex
Sparse Approximate Manifolds for Differential Geometric MCMC
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis
Mapping distinct timescales of functional interactions among brain networks
ReFIR: Grounding Large Restoration Models with Retrieval Augmentation
NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping
Batch Bayesian Optimization via Simulation Matching
DPIC: Decoupling Prompt and Intrinsic Characteristics for LLM Generated Text Detection
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
Benchmarking LLMs via Uncertainty Quantification
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
Approximation Rate of the Transformer Architecture for Sequence Modeling
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games
RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar
Bellman-consistent Pessimism for Offline Reinforcement Learning
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach
Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation
Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation
Compositional Sculpting of Iterative Generative Processes
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
Stein Variational Gradient Descent as Moment Matching
BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models
Learning with Bandit Feedback in Potential Games
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Non-geodesically-convex optimization in the Wasserstein space
Near Neighbor: Who is the Fairest of Them All?
A Comparison between a Neural Network Model for the Formation of Brain Maps and Experimental Data
Self-Supervised Motion Magnification by Backpropagating Through Optical Flow
A Neural Probabilistic Language Model
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut
Adaptive Labeling for Efficient Out-of-distribution Model Evaluation
Hard Negative Mixing for Contrastive Learning
SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
Adversarial Robustness through Random Weight Sampling
COSMIC: Compress Satellite Image Efficiently via Diffusion Compensation
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
Parametric Mixture Models for Multi-Labeled Text
Stationarity and Stability of Autoregressive Neural Network Processes
Does the Neuron "Learn" Like the Synapse?
Exploring and measuring non-linear correlations: Copulas, Lightspeed Transportation and Clustering
The Effect of Eligibility Traces on Finding Optimal Memoryless Policies in Partially Observable Markov Decision Processes
Non-Linear Coordination Graphs
DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis
Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
Neural Collapse To Multiple Centers For Imbalanced Data
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
Reproducibility of predictive networks for mouse visual cortex
Progressive mixture rules are deviation suboptimal
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
Efficient Streaming Algorithms for Graphlet Sampling
Discriminative Keyword Selection Using Support Vector Machines
From Bayesian Sparsity to Gated Recurrent Nets
Learning sparse neural networks via sensitivity-driven regularization
An Efficient Clustering Algorithm Using Stochastic Association Model and Its Implementation Using Nanostructures
The Role of Lateral Cortical Competition in Ocular Dominance Development
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
Fairness constraints can help exact inference in structured prediction
Revisiting ResNets: Improved Training and Scaling Strategies
Basis refinement strategies for linear value function approximation in MDPs
Exact and Stable Recovery of Pairwise Interaction Tensors
Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events
Sample Selection for Fair and Robust Training
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
Language Models Meet World Models: Embodied Experiences Enhance Language Models
The streaming rollout of deep networks - towards fully model-parallel execution
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting
Decoupling Semantic Similarity from Spatial Alignment for Neural Networks
Reinforcement Learning for Control with Multiple Frequencies
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning
Log-Linear Models for Label Ranking
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
Tracking Without Re-recognition in Humans and Machines
Learning Bounds for a Generalized Family of Bayesian Posterior Distributions
DeepPCR: Parallelizing Sequential Operations in Neural Networks
Kernel Functional Optimisation
Benchmark of Machine Learning Force Fields for Semiconductor Simulations: Datasets, Metrics, and Comparative Analysis
Online Learning of Quantum States
Algorithms and hardness results for parallel large margin learning
Selective Prediction of Financial Trends with Hidden Markov Models
Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals
GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization
Multi-modal Cell Segmentation based on U-Net++ and Attention Gate
One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
A Neural Network Autoassociator for Induction Motor Failure Prediction
Fast Partitioned Learned Bloom Filter
Stochastic Expectation Maximization with Variance Reduction
p-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison
Robust Full Bayesian Methods for Neural Networks
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization
Sharing Knowledge for Meta-learning with Feature Descriptions
Deep Learning with Kernel Regularization for Visual Recognition
Persistent Homology for Learning Densities with Bounded Support
Federated Model Heterogeneous Matryoshka Representation Learning
One Fits All: Power General Time Series Analysis by Pretrained LM
Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective
Topology-Imbalance Learning for Semi-Supervised Node Classification
Asynchronous Stochastic Optimization Robust to Arbitrary Delays
Reconsidering Generative Objectives For Counterfactual Reasoning
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
Domain Generalization without Excess Empirical Risk
One-Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Quadratic-Type Lyapunov Functions for Competitive Neural Networks with Different Time-Scales
Self-Supervised Generative Adversarial Compression
Low-dimensional models of neural population activity in sensory cortical circuits
Flat Seeking Bayesian Neural Networks
Certifiable Robustness to Graph Perturbations
Learning Partitions from Context
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems
Prediction with Corrupted Expert Advice
Generative Semi-supervised Graph Anomaly Detection
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
Bounding the Cost of Search-Based Lifted Inference
What Planning Problems Can A Relational Neural Network Solve?
Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 6th International Conference on Genetic Algorithms, Pittsburgh, PA, USA, July 15-19, 1995
Double Selection vs. Single Selection in Diffusion Model GAs
Learning to Detect Targets Using Scale-Space and Genetic Search
A Connectionist Algorithm for Genetic Search
Genetic Operators for High-Level Knowledge Representations
Adaptive Combustion Balancing in Multiple Burner Boiler Using a Genetic Algorithm with Variable Range of Local Search
A Hybrid Genetic Algorithm for Task Allocation in Multicomputers
Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming
A Mathematical Analysis of Tournament Selection
A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems
Solving the Multiple Resource Constrained Project Scheduling Problem with a Hybrid Genetic Algorithm
Effectiveness of Genetic Local Search Algorithms
Cellular Genetic Algorithms
Solving Constraint Satisfaction Problems Using a Genetic/Systematic Search Hybrid That Realizes When to Quit
Adaptive Selection Methods for Genetic Algorithms
Parametric Connectivity: Training of Constrained Networks using Genetic Algorithms
Optimal Determination of User-Oriented Clusters: An Application for the Reproductive Plan
Granularity Evolution
A Comparative Evaluation of Search Methods Applied to Parametric Design of Aircraft
A Genetic Approach to Stable Matching
An Approach to the Analysis of the Basins of the Associative Memory Model Using Genetic Algorithms
An Analysis of Selection Procedures with Particular Attention Paid to Proportional and Boltzmann Selection
Schema Disruption
Job Shop Scheduling with Genetic Algorithms
Modeling Simple Human Category Learning with a Classifier System
Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic Algorithm
Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy
A Two-Dimensional Embedding of Graphs for Genetic Algorithms
Mapping Neural Networks into Classifier Systems
Classifier Systems with Long Term Memory
Genetic Algorithms in Asynchronous Teams
A Parallel Genetic Algorithm for Network-Diagram Layout
Mixing in Genetic Algorithms