yusuke1997/dblp-id-title huggingface dataset:
Free Probability for predicting the performance of feed-forward fully connected neural networks
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations
On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor
How Prior Probability Influences Decision Making: A Unifying Probabilistic Model
Touch and Go: Learning from Human-Collected Vision and Touch
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization
Mesa-Extrapolation: A Weave Position Encoding Method for Enhanced Extrapolation in LLMs
Predicting a Protein's Stability under a Million Mutations
Applications of Common Entropy for Causal Inference
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis
PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks
Model evidence from nonequilibrium simulations
On the equivalence of molecular graph convolution and molecular wave function with poor basis set
ADOPT: Modified Adam Can Converge with Any β 2 with the Optimal Rate
Smoothed Online Classification can be Harder than Batch Classification
RISE: Robust Individualized Decision Learning with Sensitive Variables
The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better
PRUNE: Preserving Proximity and Global Ranking for Network Embedding
Progressive Coordinate Transforms for Monocular 3D Object Detection
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Modeling Dynamic Missingness of Implicit Feedback for Recommendation
EfficientCAPER: An End-to-End Framework for Fast and Robust Category-Level Articulated Object Pose Estimation
Empirical Bernstein Inequalities for U-Statistics
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
Paraphrasing Is All You Need for Novel Object Captioning
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
Understanding the Transferability of Representations via Task-Relatedness
Charting and Navigating the Space of Solutions for Recurrent Neural Networks
Evaluating Out-of-Distribution Performance on Document Image Classifiers
Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis
Amnesia as a Catalyst for Enhancing Black Box Pixel Attacks in Image Classification and Object Detection
An active learning framework for multi-group mean estimation
Self-Paced Learning for Latent Variable Models
A Geometric Interpretation of v-SVM Classifiers
Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks
EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas
Escaping Saddle Points with Compressed SGD
A Variational Perspective on Diffusion-Based Generative Models and Score Matching
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Learning to Accelerate Partial Differential Equations via Latent Global Evolution
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms
A Theory of Mean Field Approximation
Hierarchical Optimistic Region Selection driven by Curiosity
Best of Both Worlds Model Selection
Human spatiotemporal pattern learning as probabilistic program synthesis
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
Identifiability and Unmixing of Latent Parse Trees
Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games
Sparsity in Continuous-Depth Neural Networks
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model
Robust Federated Learning: The Case of Affine Distribution Shifts
Dense Unsupervised Learning for Video Segmentation
CASA: Category-agnostic Skeletal Animal Reconstruction
Counterfactual Explanations in Sequential Decision Making Under Uncertainty
Unsupervised Learning by Convex and Conic Coding
Dimension-Free Exponentiated Gradient
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Hierarchical Poset Decoding for Compositional Generalization in Language
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Formalizing Consistency and Coherence of Representation Learning
Goal-conditioned Imitation Learning
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory
Dynamic population-based meta-learning for multi-agent communication with natural language
Predicting Useful Neighborhoods for Lazy Local Learning
MVDoppler: Unleashing the Power of Multi-View Doppler for MicroMotion-based Gait Classification
Conditional Adversarial Domain Adaptation
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
Online Control in Population Dynamics
Reinforcement Learning with Fast and Forgetful Memory
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Parallel Sampling of HDPs using Sub-Cluster Splits
Learning Gaussian Process Kernels via Hierarchical Bayes
Building Predictive Models from Fractal Representations of Symbolic Sequences
Distributed Balanced Clustering via Mapping Coresets
ShareBoost: Efficient multiclass learning with feature sharing
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
WATTLE: A Trainable Gain Analogue VLSI Neural Network
Partially labeled classification with Markov random walks
What went wrong and when? Instance-wise feature importance for time-series black-box models
Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions
LOG: Active Model Adaptation for Label-Efficient OOD Generalization
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
A Bilinear Model for Sparse Coding
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering
VHELM: A Holistic Evaluation of Vision Language Models
Learning Affinity via Spatial Propagation Networks
Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different?
Sparse Filtering
Estimating the Unique Information of Continuous Variables
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging
On the difficulty of learning chaotic dynamics with RNNs
Quantum speedups for stochastic optimization
Information-based learning by agents in unbounded state spaces
Finding Naturally Occurring Physical Backdoors in Image Datasets
Proportional Fairness in Non-Centroid Clustering
StyleGAN knows Normal, Depth, Albedo, and More
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography
Convergence of Monte Carlo Tree Search in Simultaneous Move Games
CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics
Generalized Linear Model Regression under Distance-to-set Penalties
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
Exact differential equation population dynamics for integrate-and-fire neurons
RIM: Reliable Influence-based Active Learning on Graphs
Multi-task Causal Learning with Gaussian Processes
A General Theory of Correct, Incorrect, and Extrinsic Equivariance
Disentanglement via Latent Quantization
Global Convergence in Training Large-Scale Transformers
Ego4D Goal-Step: Toward Hierarchical Understanding of Procedural Activities
Universal Approximation Using Well-Conditioned Normalizing Flows
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Instance-dependent Label-noise Learning under a Structural Causal Model
Geometric Dirichlet Means Algorithm for topic inference
Distribution Guidance Network for Weakly Supervised Point Cloud Semantic Segmentation
A Neural Network that Learns to Interpret Myocardial Planar Thallium Scintigrams
Learning to compute Gröbner bases
A neurally plausible model for online recognition and postdiction in a dynamical environment
Nonlinear Markov Networks for Continuous Variables
Global Non-convex Optimization with Discretized Diffusions
Multi-time Models for Temporally Abstract Planning
Boosting on Manifolds: Adaptive Regularization of Base Classifiers
A Latent Variational Framework for Stochastic Optimization
BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping
Scaling White-Box Transformers for Vision
Informative Features for Model Comparison
Exact marginal prior distributions of finite Bayesian neural networks
Scalable Infomin Learning
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
Credit Assignment in Neural Networks through Deep Feedback Control
Task-Agnostic Graph Explanations
Towards Efficient Image Compression Without Autoregressive Models
HUMUS-Net: Hybrid Unrolled Multi-scale Network Architecture for Accelerated MRI Reconstruction
Improving Robustness with Adaptive Weight Decay
Explanations can be manipulated and geometry is to blame
A Unified Approach for Maximizing Continuous DR-submodular Functions
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition
Dynamic Revenue Sharing
Protecting Your LLMs with Information Bottleneck
All learning is Local: Multi-agent Learning in Global Reward Games
Decoupled Variational Gaussian Inference
Instant Quantization of Neural Networks using Monte Carlo Methods
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
LSDA: Large Scale Detection through Adaptation
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration
Learning Mixtures of Tree Graphical Models
Disentangling Transfer in Continual Reinforcement Learning
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Accurately Solving Rod Dynamics with Graph Learning
Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation
Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models
Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination
Is Long Horizon RL More Difficult Than Short Horizon RL?
Cascaded Classification Models: Combining Models for Holistic Scene Understanding
ToolQA: A Dataset for LLM Question Answering with External Tools
On Kernelized Multi-Armed Bandits with Constraints
An Approach to Bounded Rationality
A Comparison of Projection Pursuit and Neural Network Regression Modeling
Optimal Excess Risk Bounds for Empirical Risk Minimization on p-Norm Linear Regression
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters
Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation
A Computationally Robust Anatomical Model for Retinal Directional Selectivity
Privacy Odometers and Filters: Pay-as-you-Go Composition
Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression
Blackbox Attacks via Surrogate Ensemble Search
Fair and Welfare-Efficient Constrained Multi-Matchings under Uncertainty
Learning to Learn Graph Topologies
Heterogeneous Skill Learning for Multi-agent Tasks
Fixing the train-test resolution discrepancy
A Recurrent Neural Network for Generation of Occular Saccades
Equivariant Networks for Zero-Shot Coordination
Learning Provably Robust Estimators for Inverse Problems via Jittering
How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?
Is a Modular Architecture Enough?
Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases
Shooting Craps in Search of an Optimal Strategy for Training Connectionist Pattern Classifiers
ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
Population Decoding Based on an Unfaithful Model
A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization
Rate Distortion Codes in Sensor Networks
Analysis of Corrected Graph Convolutions
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Interactive Grounded Language Understanding in a Collaborative Environment: Retrospective on Iglu 2022 Competition
Nonlinear Image Interpolation using Manifold Learning
Towards Unbounded Machine Unlearning
Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements
Controllable Text-to-Image Generation
Categorization by Learning and Combining Object Parts
Estimating the Reliability of ICA Projections
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
Oblivious Sampling Algorithms for Private Data Analysis
On the Ability of Graph Neural Networks to Model Interactions Between Vertices
Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
Experimental Designs for Heteroskedastic Variance
IEBins: Iterative Elastic Bins for Monocular Depth Estimation
On Reversing Jensen's Inequality
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates
Deanonymization in the Bitcoin P2P Network
SEA: State-Exchange Attention for High-Fidelity Physics Based Transformers
Entrainment of Silicon Central Pattern Generators for Legged Locomotory Control
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
Optimal Scoring for Unsupervised Learning
Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Learning Superconductivity from Ordered and Disordered Material Structures
Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
Adversarial Resilience in Sequential Prediction via Abstention
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation
Online Structured Meta-learning
No-Regret Learning in Unknown Games with Correlated Payoffs
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
Shaping Level Sets with Submodular Functions
A Quantitative Model of Counterfactual Reasoning
Improved Coresets for Euclidean k-Means
Bayesian Map Learning in Dynamic Environments
Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Learning interaction rules from multi-animal trajectories via augmented behavioral models
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
Improved risk tail bounds for on-line algorithms
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning
Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanism
Taming Generative Diffusion Prior for Universal Blind Image Restoration
Structure Learning with Side Information: Sample Complexity
Exact Post Model Selection Inference for Marginal Screening
SimplE Embedding for Link Prediction in Knowledge Graphs
Exclusively Penalized Q-learning for Offline Reinforcement Learning
FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling
Fit for our purpose, not yours: Benchmark for a low-resource, Indigenous language
A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process
On Prior Distributions and Approximate Inference for Structured Variables
A Small World Threshold for Economic Network Formation
Maximum Class Separation as Inductive Bias in One Matrix
The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains
A Stochastic Composite Gradient Method with Incremental Variance Reduction
The Second NeurIPS Tournament of Reconnaissance Blind Chess
Beyond Single Stationary Policies: Meta-Task Players as Naturally Superior Collaborators
Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields
Algorithms for Infinitely Many-Armed Bandits
VIREL: A Variational Inference Framework for Reinforcement Learning
Policy Gradient With Value Function Approximation For Collective Multiagent Planning
SpeAr: A Spectral Approach for Zero-Shot Node Classification
Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling
Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels
TAPVid-3D: A Benchmark for Tracking Any Point in 3D
MExMI: Pool-based Active Model Extraction Crossover Membership Inference
SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation
Watermarking for Out-of-distribution Detection
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent
Questioning the Survey Responses of Large Language Models
Stereopsis by a Neural Network Which Learns the Constraints
Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms
Stratified Prediction-Powered Inference for Effective Hybrid Evaluation of Language Models
Information-Theoretic Connectivity-Based Cortex Parcellation
Hybrid NN/HMM-Based Speech Recognition with a Discriminant Neural Feature Extraction
Adversarial Music: Real world Audio Adversary against Wake-word Detection System
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
Monte-Carlo Tree Search for Constrained POMDPs
Propagation Filters in PDS Networks for Sequencing and Ambiguity Resolution
The Gamma MLP for Speech Phoneme Recognition
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity
Multi-modal Grouping Network for Weakly-Supervised Audio-Visual Video Parsing
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale
Committing Bandits
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
PG-means: learning the number of clusters in data
Consistent Plug-in Classifiers for Complex Objectives and Constraints
Kiki or Bouba? Sound Symbolism in Vision-and-Language Models
Optimal Sampling and Clustering in the Stochastic Block Model
Minimizing Sparse High-Order Energies by Submodular Vertex-Cover
Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)
Robust Anytime Learning of Markov Decision Processes
Optimal Movement Primitives
Design of Experiments via Information Theory
Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
Context-lumpable stochastic bandits
Spectral Learning of Mixture of Hidden Markov Models
A New Distribution on the Simplex with Auto-Encoding Applications
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
Can Push-forward Generative Models Fit Multimodal Distributions?
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
The Learning Dynamcis of a Universal Approximator
Hierarchical Eigensolver for Transition Matrices in Spectral Methods
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
Improving Decision Sparsity
Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times
High-probability complexity bounds for stochastic non-convex minimax optimization
The Product Cut
Cost Effective Active Search
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning
Robust Bayesian Regression via Hard Thresholding
A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models
Approximate Correspondences in High Dimensions
Reinforcement Learning with Latent Flow
Edit Distance Robust Watermarks via Indexing Pseudorandom Codes
AutoPSV: Automated Process-Supervised Verifier
Eliminating Domain Bias for Federated Learning in Representation Space
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling
Dynamic Foreground/Background Extraction from Images and Videos using Random Patches
Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks
Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization
Robustness of conditional GANs to noisy labels
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems
Estimating Learnability in the Sublinear Data Regime
Knowledge Extraction with No Observable Data
Scalable Neural Data Server: A Data Recommender for Transfer Learning
Model selection and velocity estimation using novel priors for motion patterns
Resolving Perceptual Aliasing In The Presence Of Noisy Sensors
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs
Latent Space Translation via Semantic Alignment
Fair Secretaries with Unfair Predictions
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution
Tight Risk Bounds for Gradient Descent on Separable Data
Learning to Mitigate AI Collusion on Economic Platforms
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
Adaptive Proximal Gradient Methods for Structured Neural Networks
Revisit Weakly-Supervised Audio-Visual Video Parsing from the Language Perspective
A^2-Nets: Double Attention Networks
Implicitly learning to reason in first-order logic
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models