yusuke1997/dblp-id-title huggingface dataset:
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter
AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs
The Wisdom of Crowds in the Recollection of Order Information
Closing the gap between the upper bound and lower bound of Adam's iteration complexity
Few-Shot Diffusion Models Escape the Curse of Dimensionality
Clustering Aggregation as Maximum-Weight Independent Set
Exponentiated Strongly Rayleigh Distributions
Emergent Communication under Varying Sizes and Connectivities
ReVideo: Remake a Video with Motion and Content Control
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
Efficient Hyper-parameter Optimization with Cubic Regularization
Optimal Transport of Classifiers to Fairness
Boosting the Potential of Large Language Models with an Intelligent Information Assistant
Langevin Quasi-Monte Carlo
Automatic Program Synthesis of Long Programs with a Learned Garbage Collector
Equivariant Networks for Hierarchical Structures
GALOPA: Graph Transport Learning with Optimal Plan Alignment
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Learning Curves, Model Selection and Complexity of Neural Networks
Learning from Logged Implicit Exploration Data
DropCov: A Simple yet Effective Method for Improving Deep Architectures
Delta-encoder: an effective sample synthesis method for few-shot object recognition
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks
Unsupervised Color Decomposition Of Histologically Stained Tissue Samples
UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Membership Inference Attacks against Large Vision-Language Models
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Scalable Kernel Methods via Doubly Stochastic Gradients
Boosting Adversarial Training with Hypersphere Embedding
Human-Aligned Calibration for AI-Assisted Decision Making
Model Agnostic Supervised Local Explanations
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
Generalized Unsupervised Manifold Alignment
3DB: A Framework for Debugging Computer Vision Models
Piecewise-Stationary Bandits with Knapsacks
Active Exploration for Learning Symbolic Representations
Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis
Where's Waldo: Diffusion Features For Personalized Segmentation and Retrieval
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Revisiting Implicit Differentiation for Learning Problems in Optimal Control
Complexity Matters: Rethinking the Latent Space for Generative Modeling
Co-Regularized Hashing for Multimodal Data
Recursive Inversion Models for Permutations
What Do Neural Networks Learn When Trained With Random Labels?
Nearly Horizon-Free Offline Reinforcement Learning
Sequential Neural Models with Stochastic Layers
Curriculum learning for multilevel budgeted combinatorial problems
Recursive Inference for Variational Autoencoders
Neural Attribution for Semantic Bug-Localization in Student Programs
Weakly Supervised Cell Instance Segmentation for Multi-Modality Microscopy
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
Generative Image Modeling Using Spatial LSTMs
Learning to Branch with Tree MDPs
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Self-Supervised Few-Shot Learning on Point Clouds
Adaptation Properties Allow Identification of Optimized Neural Codes
Learning to Pass Expectation Propagation Messages
Polynomial Uniform Convergence of Relative Frequencies to Probabilities
DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
Adaptation and Unsupervised Learning
Learning Interaction-aware 3D Gaussian Splatting for One-shot Hand Avatars
Rule Extrapolation in Language Modeling: A Study of Compositional Generalization on OOD Prompts
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization
A "Shape Aware" Model for semi-supervised Learning of Objects and its Context
Online Learning via the Differential Privacy Lens
Convolutional Dictionary Learning through Tensor Factorization
Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks
DiBS: Differentiable Bayesian Structure Learning
Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
Uncoupled Regression from Pairwise Comparison Data
Bayesian Optimization with Unknown Search Space
Towards a learning-theoretic analysis of spike-timing dependent plasticity
Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI
Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA
Humans in Kitchens: A Dataset for Multi-Person Human Motion Forecasting with Scene Context
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation
Localization, Convexity, and Star Aggregation
PCA-Pyramids for Image Compression
Data Selection for Language Models via Importance Resampling
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition
Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Note on Learning Rate Schedules for Stochastic Optimization
Discrete Affine Wavelet Transforms
Weighted model estimation for offline model-based reinforcement learning
Sparse Local Embeddings for Extreme Multi-label Classification
Inferring spike-timing-dependent plasticity from spike train data
M5HisDoc: A Large-scale Multi-style Chinese Historical Document Analysis Benchmark
Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery
DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
Learning to See Where and What: Training a Net to Make Saccades and Recognize Handwritten Characters
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation
Using natural language and program abstractions to instill human inductive biases in machines
Curriculum Design for Teaching via Demonstrations: Theory and Applications
Binarized Neural Machine Translation
The Efficient Learning of Multiple Task Sequences
Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems
Bandit Phase Retrieval
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models
STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers
Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors
Gradient and Hamiltonian Dynamics Applied to Learning in Neural Networks
A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization
f-GANs in an Information Geometric Nutshell
Gaussian Quadrature for Kernel Features
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules
Finding All $\epsilon$-Good Arms in Stochastic Bandits
Fast Algorithms for $L_\infty$-constrained S-rectangular Robust MDPs
Efficient Neural Codes under Metabolic Constraints
Faster Linear Algebra for Distance Matrices
On-Demand Sampling: Learning Optimally from Multiple Distributions
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction
Weakly-Supervised Audio-Visual Segmentation
Learning Diverse Policies in MOBA Games via Macro-Goals
Demystifying Softmax Gating Function in Gaussian Mixture of Experts
The Computation of Sound Source Evaluation in the Barn Owl
Predicting Lifetimes in Dynamically Allocated Memory
Crystal Structure Prediction by Joint Equivariant Diffusion
Inception Transformer
Clustering from Labels and Time-Varying Graphs
Signature Verification Using a Siamese Time Delay Neural Network
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning
Detecting Abrupt Changes in Sequential Pairwise Comparison Data
Temporal Continual Learning with Prior Compensation for Human Motion Prediction
Learning Disentangled Representations of Videos with Missing Data
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation
Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems
Recognizing Hand-Printed Letters and Digits
Dynamic-Depth Context Tree Weighting
A Spectral Energy Distance for Parallel Speech Synthesis
Toward Semantic Gaze Target Detection
Efficient Leverage Score Sampling for Tensor Train Decomposition
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network
Recurrent Autoencoder with Skip Connections and Exogenous Variables for Traffic Forecasting
The Scaling Limit of High-Dimensional Online Independent Component Analysis
Chest ImaGenome Dataset for Clinical Reasoning
Efficient Methods for Overlapping Group Lasso
Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity
The Diffusion-Limited Biochemical Signal-Relay Channel
Q8BERT: Quantized 8Bit BERT
LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low Resource and Extinct Languages
Inductive Logical Query Answering in Knowledge Graphs
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
An Input Output HMM Architecture
Application of Variational Bayesian Approach to Speech Recognition
MDPs with Non-Deterministic Policies
Differentiable Top-k with Optimal Transport
Pruning Randomly Initialized Neural Networks with Iterative Randomization
Masked Pre-training Enables Universal Zero-shot Denoiser
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization
Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning
Discovering Structure from Motion in Monkey, Man and Machine
Recovering a Feed-Forward Net From Its Output
EffiBench: Benchmarking the Efficiency of Automatically Generated Code
Gaussian Process Random Fields
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting
ProtGO: Function-Guided Protein Modeling for Unified Representation Learning
3D molecule generation by denoising voxel grids
Exploratory Feature Extraction in Speech Signals
Rate Distortion Function in the Spin Glass State: A Toy Model
GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection
Generalization Error and the Expected Network Complexity
Parallel Sampling of Diffusion Models
Spike Timing-Dependent Plasticity in the Address Domain
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
Bayesian Optimisation of Functions on Graphs
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate
Lift Yourself Up: Retrieval-augmented Text Generation with Self-Memory
Few-Shot Learning Through an Information Retrieval Lens
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
Fast Computation of Graph Kernels
Characterizing Bias in Classifiers using Generative Models
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
Covariance-adaptive best arm identification
A framework for studying synaptic plasticity with neural spike train data
Analyzing the Energy Landscapes of Distributed Winner-Take-All Networks
AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents
Statistically and Computationally Efficient Linear Meta-representation Learning
Sparsity of Data Representation of Optimal Kernel Machine and Leave-one-out Estimator
Sparse Bayesian structure learning with dependent relevance determination priors
GANSpace: Discovering Interpretable GAN Controls
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
The Role of Baselines in Policy Gradient Optimization
AutoGEL: An Automated Graph Neural Network with Explicit Link Information
Partial Multi-Label Learning with Probabilistic Graphical Disambiguation
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Learning Mixtures of Unknown Causal Interventions
Factorial Hidden Markov Models
InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory
Trans-Dimensional Generative Modeling via Jump Diffusion Models
Gradients of Functions of Large Matrices
DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions
Informed Projections
FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes
Replicator Equations, Maximal Cliques, and Graph Isomorphism
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent
Machine Learning for Health Workshop, ML4H@NeurIPS 2020, Virtual Event, 11 December 2020
Multiple Alignment of Continuous Time Series
Unsupervised Learning of Equivariant Structure from Sequences
Domain Invariant Representation Learning with Domain Density Transformations
Learning Nonparametric Latent Causal Graphs with Unknown Interventions
Deep Generative Video Compression
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Deep Lattice Networks and Partial Monotonic Functions
Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions
Efficient Methods for Privacy Preserving Face Detection
Active Learning with Oracle Epiphany
PAC-Bayesian AUC classification and scoring
Can We Leave Deepfake Data Behind in Training Deepfake Detector?
Classification on Pairwise Proximity Data
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations
Interposing an Ontogenetic Model Between Genetic Algorithms and Neural Networks
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
Unsupervised and Supervised Clustering: The Mutual Information between Parameters and Observations
Towards a Better Global Loss Landscape of GANs
ARTEX: A Self-organizing Architecture for Classifying Image Regions
WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
A Randomized Approach to Tight Privacy Accounting
Video Frame Interpolation without Temporal Priors
Regret Bounds for Multilabel Classification in Sparse Label Regimes
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients
Bayesian Unsupervised Learning of Higher Order Structure
Emergent Communication in Interactive Sketch Question Answering
Laplacian Score for Feature Selection
Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality
A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm
A Scalable Neural Network for DSIC Affine Maximizer Auction Design
From Regularization Operators to Support Vector Kernels
Expectation Alignment: Handling Reward Misspecification in the Presence of Expectation Mismatch
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
Neural mechanisms of contrast dependent receptive field size in V1
Q-learning with Nearest Neighbors
Learning Depth from Single Monocular Images
MetaCURL: Non-stationary Concave Utility Reinforcement Learning
DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric Finetuning
Catching Up Faster in Bayesian Model Selection and Model Averaging
Understanding Bias in Large-Scale Visual Datasets
Bootstrapping neural processes
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions
Off-policy Learning with Options and Recognizers
Reinforcement Learning with Lookahead Information
Expanding Holographic Embeddings for Knowledge Completion
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
NeurIPS 2022 Competition Track, November 28 - December 9, 2022, Online
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models
Optimal Design for Human Preference Elicitation
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem
Generalisation in Feedforward Networks
A coupled autoencoder approach for multi-modal analysis of cell types
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks
Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects
Automated State Abstraction for Options using the U-Tree Algorithm
AutoSurvey: Large Language Models Can Automatically Write Surveys
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem
Robust Streaming PCA
Absence of Cycles in Symmetric Neural Networks
Context Effects in Category Learning: An Investigation of Four Probabilistic Models
AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints
Bounds all around: training energy-based models with bidirectional bounds
Reinforced Cross-Domain Knowledge Distillation on Time Series Data
Detecting Interactions from Neural Networks via Topological Analysis
Building Socio-culturally Inclusive Stereotype Resources with Community Engagement
A Kernel Method for the Two-Sample-Problem
First-order methods almost always avoid saddle points: The case of vanishing step-sizes
A Conditional Multinomial Mixture Model for Superset Label Learning
PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning
Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency
Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences
From Deformations to Parts: Motion-based Segmentation of 3D Objects
KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Label-Only Model Inversion Attacks via Knowledge Transfer
Bayesian-guided Label Mapping for Visual Reprogramming
On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference
Space-Time Correspondence as a Contrastive Random Walk
Convex Multiple-Instance Learning by Estimating Likelihood Ratio
Synthesize Policies for Transfer and Adaptation across Tasks and Environments
On Effective Scheduling of Model-based Reinforcement Learning
The expressive power of pooling in Graph Neural Networks
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
On kernel-based statistical learning theory in the mean field limit
Reconstructing Stimulus Velocity from Neuronal Responses in Area MT
Real-Time Inference for a Gamma Process Model of Neural Spiking
Kernelized Infomax Clustering
Stochastic Newton Proximal Extragradient Method
Deep Active Learning by Leveraging Training Dynamics
A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
Distributed estimation of the inverse Hessian by determinantal averaging
Spiking Neural Network as Adaptive Event Stream Slicer
Distributed Personalized Empirical Risk Minimization
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
Learning from Group Comparisons: Exploiting Higher Order Interactions
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
On Sampling Strategies for Spectral Model Sharding
Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks
Hypothesis Set Stability and Generalization
Consequences of Misaligned AI
A concave regularization technique for sparse mixture models
Matrix Inference and Estimation in Multi-Layer Models
Image-aware Evaluation of Generated Medical Reports
Graph-Valued Regression
InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models
Statistical Topological Data Analysis - A Kernel Perspective
SIRIUS : Contexual Sparisty with Correction for Efficient LLMs
Maximising Sensitivity in a Spiking Network
Reconstructing Parameters of Spreading Models from Partial Observations
Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning
A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
Statistical Performance of Convex Tensor Decomposition
Beyond probability partitions: Calibrating neural networks with semantic aware grouping
I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing
Task-aware Distributed Source Coding under Dynamic Bandwidth
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
Feedforward Learning of Mixture Models
Partitioning of Sensory Data by a Cortical Network
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
Language models enable zero-shot prediction of the effects of mutations on protein function
Near-Optimal Multi-Agent Learning for Safe Coverage Control
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
The Entire Regularization Path for the Support Vector Machine
Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis
NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory
Multi-Winner Reconfiguration
Distance-Based Network Recovery under Feature Correlation
Rodeo: Sparse Nonparametric Regression in High Dimensions
Auditing Fairness by Betting
A kernel method for multi-labelled classification
Beyond Average Return in Markov Decision Processes
Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices
Efficient Neural Computation in the Laplace Domain
Adaptive stimulus selection for optimizing neural population responses
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems
Analytical Solution of Spike-timing Dependent Plasticity Based on Synaptic Biophysics
Stein Self-Repulsive Dynamics: Benefits From Past Samples
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization
Optimal Algorithms for Stochastic Contextual Preference Bandits
Neuron-MOS Temporal Winner Search Hardware for Fully-Parallel Data Processing
An Asynchronous Hidden Markov Model for Audio-Visual Speech Recognition