yusuke1997/dblp-id-title huggingface dataset:
Variational Information Maximization for Feature Selection
Relative Entropic Optimal Transport: a (Prior-aware) Matching Perspective to (Unbalanced) Classification
Message Errors in Belief Propagation
A Smoothing Regularizer for Recurrent Neural Networks
Using Voice Transformations to Create Additional Training Talkers for Word Spotting
Optimal Architectures in a Solvable Model of Deep Networks
CosAE: Learnable Fourier Series for Image Restoration
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding
Provable Tensor Factorization with Missing Data
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor Segmentation
Non-Adversarial Mapping with VAEs
K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms
NeurIPS 2022 Competition Track Revised Selected Papers
Learning to Configure Computer Networks with Neural Algorithmic Reasoning
Learning dynamics of deep linear networks with multiple pathways
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings
EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning
Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models
Transformers are Minimax Optimal Nonparametric In-Context Learners
Sharp Recovery Thresholds of Tensor PCA Spectral Algorithms
Reinforcement Learning in Reward-Mixing MDPs
Clustered factor analysis of multineuronal spike data
DELIFFAS: Deformable Light Fields for Fast Avatar Synthesis
Differentially Private Learning of Structured Discrete Distributions
Static and Dynamic Error Propagation Networks with Application to Speech Coding
S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces
Direct Optimization through arg max for Discrete Variational Auto-Encoder
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Beyond task diversity: provable representation transfer for sequential multitask linear bandits
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
Thompson Sampling for Multinomial Logit Contextual Bandits
DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees
Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Poses
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning
MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
Single-Iteration Threshold Hamming Networks
Harmonizing Visual Text Comprehension and Generation
AutoBalance: Optimized Loss Functions for Imbalanced Data
Improving Generalization of Dynamic Graph Learning via Environment Prompt
Batch Value Function Approximation via Support Vectors
Point process latent variable models of larval zebrafish behavior
Greedy Algorithms for Structurally Constrained High Dimensional Problems
Hippocampal Model of Rat Spatial Abilities Using Temporal Difference Learning
Linear dynamical neural population models through nonlinear embeddings
Learning Successor Features the Simple Way
ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition
Adversarial Self-Defense for Cycle-Consistent GANs
Preference Learning Algorithms Do Not Learn Preference Rankings
Tracking People with 3D Representations
Deep learning with kernels through RKHM and the Perron-Frobenius operator
When Does Group Invariant Learning Survive Spurious Correlations?
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
Incorporating Contextual Information in White Blood Cell Identification
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity
Weakly-supervised Discovery of Visual Pattern Configurations
Three Iterations of (d - 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points
Optimal Weak to Strong Learning
On 1/n neural representation and robustness
Code-specific policy gradient rules for spiking neurons
Towards Efficient Pre-Trained Language Model via Feature Correlation Distillation
Towards Multi-dimensional Explanation Alignment for Medical Classification
Direct Diffusion Bridge using Data Consistency for Inverse Problems
Manifold-regression to predict from MEG/EEG brain signals without source modeling
Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees
Outlier-Robust Sparse Estimation via Non-Convex Optimization
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Data Generation as Sequential Decision Making
Reinforcement Learning Applied to Linear Quadratic Regulation
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
Multiple timescales and uncertainty in motor adaptation
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
A Geometric Perspective on Optimal Representations for Reinforcement Learning
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
Graphcode: Learning from multiparameter persistent homology using graph neural networks
Inverse Dynamics of Speech Motor Control
SAM-Guided Masked Token Prediction for 3D Scene Understanding
PTQ4DiT: Post-training Quantization for Diffusion Transformers
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Unsupervised Text Generation by Learning from Search
Policy Learning from Tutorial Books via Understanding, Rehearsing and Introspecting
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator
Hierarchical Optimal Transport for Document Representation
Adaptive Online Packing-guided Search for POMDPs
Mask Matching Transformer for Few-Shot Segmentation
Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem
Residual2Vec: Debiasing graph embedding with random graphs
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering
Lo-Hi: Practical ML Drug Discovery Benchmark
A dynamic excitatory-inhibitory network in a VLSI chip for spiking information reregistration
Riemannian Residual Neural Networks
Distribution-Independent PAC Learning of Halfspaces with Massart Noise
Faster DBSCAN via subsampled similarity queries
Variational Interaction Information Maximization for Cross-domain Disentanglement
Uni3DETR: Unified 3D Detection Transformer
A Variational Baysian Framework for Graphical Models
Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds
Constant Acceleration Flow
Kernel Expansions with Unlabeled Examples
FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner
Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024
Learning Fast-Inference Bayesian Networks
Wild-GS: Real-Time Novel View Synthesis from Unconstrained Photo Collections
Unsupervised Co-Learning on G-Manifolds Across Irreducible Representations
SatLM: Satisfiability-Aided Language Models Using Declarative Prompting
ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation
Non-convex Statistical Optimization for Sparse Tensor Graphical Model
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Models to Pre-trained Machine Reader
Uni[MASK]: Unified Inference in Sequential Decision Problems
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks
Sampling Methods for Unsupervised Learning
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
A Harmonic Excitation State-Space Approach to Blind Separation of Speech
MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples
Interpretable Sequence Learning for Covid-19 Forecasting
Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing
A Bifurcation Theory Approach to the Programming of Periodic Attractors in Network Models of Olfactory Cortex
Backward-Compatible Prediction Updates: A Probabilistic Approach
Supervised autoencoders: Improving generalization performance with unsupervised regularizers
Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction
Stochastic Mirror Descent in Variationally Coherent Optimization Problems
Adapting Codes and Embeddings for Polychotomies
Multi-task Learning of Order-Consistent Causal Graphs
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
Backpropagation Convergence via Deterministic Nonmonotone Perturbed Minimization
Fast Pruning Using Principal Components
Leveraging the two-timescale regime to demonstrate convergence of neural networks
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
Context and Geometry Aware Voxel Transformer for Semantic Scene Completion
Tight Bounds on Profile Redundancy and Distinguishability
Grouping and dimensionality reduction by locally linear embedding
Discriminative Topic Modeling with Logistic LDA
MetricFormer: A Unified Perspective of Correlation Exploring in Similarity Learning
GP Kernels for Cross-Spectrum Analysis
ExPT: Synthetic Pretraining for Few-Shot Experimental Design
Transition Constrained Bayesian Optimization via Markov Decision Processes
MADiff: Offline Multi-agent Learning with Diffusion Models
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
Schur Nets: exploiting local structure for equivariance in higher order graph neural networks
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Poseidon: Efficient Foundation Models for PDEs
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Data-Dependence of Plateau Phenomenon in Learning with Neural Network - Statistical Mechanical Analysis
Variational Policy Search via Trajectory Optimization
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer
Information Theoretic Analysis of Connection Structure from Spike Trains
Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks
Generalized One-shot Domain Adaptation of Generative Adversarial Networks
Multi-task Learning for Aggregated Data using Gaussian Processes
Multi-Digit Recognition Using a Space Displacement Neural Network
CIFD: Controlled Information Flow to Enhance Knowledge Distillation
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces
Heuristics for Ordering Cue Search in Decision Making
Matrix Completion with Hierarchical Graph Side Information
Barking up the right tree: an approach to search over molecule synthesis DAGs
Location-based activity recognition
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions
A Probabilistic Graph Coupling View of Dimension Reduction
Label-Alignment-Based Multi-Task Feature Selection for Multimodal Classification of Brain Disease
Probabilistic Image Sensor Fusion
A Causal Lens for Controllable Text Generation
Mass Meta-analysis in Talairach Space
Sample Adaptive MCMC
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
Unsupervised Learning of Disentangled Representations from Video
Sequential Transfer in Multi-armed Bandit with Finite Set of Models
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
Greedy Sampling for Approximate Clustering in the Presence of Outliers
Stochastic Optimal Control for Diffusion Bridges in Function Spaces
Measures of distortion for machine learning
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points
CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
The Utility of "Even if" Semifactual Explanation to Optimise Positive Outcomes
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition
Dynamical Modeling with Kernels for Nonlinear Time Series Prediction
Prediction with Action: Visual Policy Learning via Joint Denoising Process
Prototypical Networks for Few-shot Learning
Probabilistic Deterministic Infinite Automata
The Implicit Bias of AdaGrad on Separable Data
Deep Hyperspherical Learning
Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages
On the Adversarial Robustness of Benjamini Hochberg
Differentiable rendering with perturbed optimizers
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Navigating the Effect of Parametrization for Dimensionality Reduction
Policy Learning Using Weak Supervision
Observation-Free Attacks on Stochastic Bandits
Linear Time Approximation Algorithm for Column Subset Selection with Local Search
Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity
A Massively-Parallel {SIMD} Processor for Neural Network and Machine Vision Applications
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control
Graph-ResNets for short-term traffic forecasts in almost unknown cities
Real Robot Challenge: A Robotics Competition in the Cloud
Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT
Online Estimation via Offline Estimation: An Information-Theoretic Framework
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition
Online Learning in The Manifold of Low-Rank Matrices
Inductive reasoning about chimeric creatures
A Stability-based Validation Procedure for Differentially Private Machine Learning
Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction
Exponentially Convergent Algorithms for Supervised Matrix Factorization
Visualizing the Emergence of Intermediate Visual Patterns in DNNs
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Top-Down Control of Visual Attention: A Rational Account
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning
Sequential Causal Imitation Learning with Unobserved Confounders
Random Reshuffling is Not Always Better
Online Linear Optimization with Many Hints
Probabilistic Line Searches for Stochastic Optimization
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data
Consensus Propagation
Algorithms for CVaR Optimization in MDPs
Stochastic Gradient MCMC with Stale Gradients
A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Complexity of Finite Precision Neural Network Classifier
Physion: Evaluating Physical Prediction from Vision in Humans and Machines
Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model
Learning and Covering Sums of Independent Random Variables with Unbounded Support
Estimating Equivalent Kernels for Neural Networks: A Data Perturbation Approach
The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging
Optimal Treatment Regimes for Proximal Causal Learning
Contrastive Training of Complex-Valued Autoencoders for Object Discovery
Random Utility Theory for Social Choice
Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage
A mathematical model for automatic differentiation in machine learning
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning
Multi-armed Bandits with Compensation
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization
Towards Convergence Rate Analysis of Random Forests for Classification
Active Comparison of Prediction Models
Average Individual Fairness: Algorithms, Generalization and Experiments
Neural Conditional Probability for Uncertainty Quantification
WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
Variational Consensus Monte Carlo
Error Correction Code Transformer
Partial Optimal Tranport with applications on Positive-Unlabeled Learning
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
Learning Deep Embeddings with Histogram Loss
Where are they looking?
Fair regression via plug-in estimator and recalibration with statistical guarantees
Dopaminergic Neuromodulation Brings a Dynamical Plasticity to the Retina
Learning Deep Disentangled Embeddings With the F-Statistic Loss
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks
An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints
Where Does the Population Vector of Motor Cortical Cells Point during Reaching Movements?
Modelling Genetic Variations using Fragmentation-Coagulation Processes
Convergence Analysis of Split Federated Learning on Heterogeneous Data
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions
projUNN: efficient method for training deep networks with unitary matrices
Bridging the Imitation Gap by Adaptive Insubordination
Learning Deterministic Weighted Automata with Queries and Counterexamples
Learning Semantic Similarity
IPM-LSTM: A Learning-Based Interior Point Method for Solving Nonlinear Programs
ActFusion: a Unified Diffusion Model for Action Segmentation and Anticipation
Shared Independent Component Analysis for Multi-Subject Neuroimaging
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
Reinforcement Learning and Time Perception -- a Model of Animal Experiments
Exact Shape Correspondence via 2D graph convolution
Geodesic Optimization for Predictive Shift Adaptation on EEG data
QGFN: Controllable Greediness with Action Values
Integrated Segmentation and Recognition of Hand-Printed Numerals
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
Supervised Topic Models
Improved Dynamic Regret for Non-degenerate Functions
Bayesian Kernel Shaping for Learning Control
Conditional Models of Identity Uncertainty with Application to Noun Coreference
Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization
Bayes-Adaptive POMDPs
Revisiting motion information for RGB-Event tracking with MOT philosophy
Learning Where to Edit Vision Transformers
Dropout Training as Adaptive Regularization
Sliding Window Algorithms for k-Clustering Problems
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games
Homeostasis in a Silicon Integrate and Fire Neuron
A Unifying Normative Framework of Decision Confidence
Means, Correlations and Bounds
Prediction and Control in Continual Reinforcement Learning
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps
Sub-Microwatt Analog VLSI Support Vector Machine for Pattern Classification and Sequence Estimation
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Regularized linear autoencoders recover the principal components, eventually
Scaling-up Importance Sampling for Markov Logic Networks
Meta-Learning for Relative Density-Ratio Estimation
Musical Speech: A Transformer-based Composition Tool
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
Learning Bounded Treewidth Bayesian Networks
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes
Optimal Brain Damage
Use of Multi-Layered Networks for Coding Speech with Phonetic Features
AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
Noisy Neural Networks and Generalizations
Moment-based Uniform Deviation Bounds for k -means and Friends
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices
Learning GANs and Ensembles Using Discrepancy
Computer Simulation of Oscillatory Behavior in Cerebral Cortical Networks
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
Parallel and Efficient Hierarchical k-Median Clustering
Neural Processes with Stability
Robust Calibration with Multi-domain Temperature Scaling
How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions
ARTree: A Deep Autoregressive Model for Phylogenetic Inference
Learning with Relaxed Supervision
DataMUX: Data Multiplexing for Neural Networks
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening
Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022
The Importance of Communities for Learning to Influence
Neurally Inspired Plasticity in Oculomotor Processes
Bridging semantics and pragmatics in information-theoretic emergent communication
Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language Models
Necessary Intransitive Likelihood-Ratio Classifiers
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood
Tensor decompositions of higher-order correlations by nonlinear Hebbian plasticity
CNNpack: Packing Convolutional Neural Networks in the Frequency Domain
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery
Zero-shot Knowledge Transfer via Adversarial Belief Matching
Selecting Receptive Fields in Deep Networks
Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation