yusuke1997/dblp-id-title huggingface dataset:
OxonFair: A Flexible Toolkit for Algorithmic Fairness
The price of unfairness in linear bandits with biased feedback
Machine Learning for Health (ML4H) 2020: Advancing Healthcare for All
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Diffusing Differentiable Representations
Transforming Neural-Net Output Levels to Probability Distributions
Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms
Independent Component Analysis of Electroencephalographic Data
Geometric Descent Method for Convex Composite Minimization
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
Causal Effect Inference with Deep Latent-Variable Models
Non-Local Manifold Parzen Windows
An Improved Empirical Fisher Approximation for Natural Gradient Descent
Training Very Deep Networks
Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection
Probabilistic Anomaly Detection in Dynamic Systems
Exploration via Hindsight Goal Generation
Knowledge Distillation Performs Partial Variance Reduction
Masked Image Residual Learning for Scaling Deeper Vision Transformers
Conditioning Sparse Variational Gaussian Processes for Online Decision-making
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality
Random Cuts are Optimal for Explainable k-Medians
Network Generalization for Production: Learning and Producing Styled Letterforms
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
Feature Cross-Substitution in Adversarial Classification
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient
Harmony Networks Do Not Work
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC
FETA: Towards Specializing Foundational Models for Expert Task Applications
Ignorance is Bliss: Robust Control via Information Gating
k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
GO4Align: Group Optimization for Multi-Task Alignment
Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models
Bayesian Sampling Using Stochastic Gradient Thermostats
Estimating Multi-cause Treatment Effects via Single-cause Perturbation
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Parts of Speech-Grounded Subspaces in Vision-Language Models
Spectral Representations for Convolutional Neural Networks
SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks
Online Posterior Sampling with a Diffusion Prior
Finite Time Analysis of Stratified Sampling for Monte Carlo
An Efficient Streaming Algorithm for the Submodular Cover Problem
A Kernel-based Test of Independence for Cluster-correlated Data
Multi-Sample Training for Neural Image Compression
Adaptive Neural Net Preprocessing for Signal Detection in Non-Gaussian Noise
Newton Informed Neural Operator for Solving Nonlinear Partial Differential Equations
Scalable Early Childhood Reading Performance Prediction
Boosting and Maximum Likelihood for Exponential Models
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
Implicit Regularization of Decentralized Gradient Descent for Sparse Regression
Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs
Structured Generative Adversarial Networks
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data
Putting It All Together: Methods for Combining Neural Networks
On the Representation Collapse of Sparse Mixture of Experts
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks
Fast biped walking with a reflexive controller and real-time policy searching
Efficient and Stable Fully Dynamic Facility Location
A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose
Most Neural Networks Are Almost Learnable
Peer Prediction for Learning Agents
Isotonic Conditional Random Fields and Local Sentiment Flow
Continuous Mean-Covariance Bandits
Asynchronous Proportional Response Dynamics: Convergence in Markets with Adversarial Scheduling
Equilibrium Refinement for the Age of Machines: The One-Sided Quasi-Perfect Equilibrium
Understanding and Improving Ensemble Adversarial Defense
DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus
Quantum algorithm for large-scale market equilibrium computation
Semantic Visual Navigation by Watching YouTube Videos
Online Allocation and Learning in the Presence of Strategic Agents
A Unified Learning Scheme: Bayesian-Kullback Ying-Yang Machines
Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice
ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning
Least Square Calibration for Peer Reviews
Learning a Forward Model of a Reflex
SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis
Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation
Hyperbolic Embedding Inference for Structured Multi-Label Prediction
TrueSkill TM : A Bayesian Skill Rating System
An LP View of the M-best MAP problem
Reconciling meta-learning and continual learning with online mixtures of tasks
MOVE: Unsupervised Movable Object Segmentation and Detection
Enhancing vision-language models for medical imaging: bridging the 3D gap with innovative slice selection
Locality Sensitive Teaching
Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
Improved Error Bounds for Tree Representations of Metric Spaces
Rapid Quality Estimation of Neural Network Input Representations
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
How hard is my MDP?" The distribution-norm to the rescue"
Priors over Recurrent Continuous Time Processes
An Efficient and Robust Framework for Approximate Nearest Neighbor Search with Attribute Constraint
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
Robustness of Community Detection to Random Geometric Perturbations
Deep Learning, Dark Knowledge, and Dark Matter
Autonomous Reinforcement Learning via Subgoal Curricula
Interaction Among Ocularity, Retinotopy and On-center/Off-center Pathways
On the Sample Complexity of Subspace Learning
Spiking and saturating dendrites differentially expand single neuron computation capacity
Estimating and Explaining Model Performance When Both Covariates and Labels Shift
Optimal Neural Population Codes for High-dimensional Stimulus Variables
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
Belief Propagation Neural Networks
Reparameterization invariance in approximate Bayesian inference
Markov Models for Automated ECG Interval Analysis
Burst Synchronization without Frequency Locking in a Completely Solvable Neural Network Model
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems
General Nonlinearities in SO(2)-Equivariant CNNs
Top-Down Regularization of Deep Belief Networks
Communicating Natural Programs to Humans and Machines
Settling the Variance of Multi-Agent Policy Gradients
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
Learning Sparse Topographic Representations with Products of Student-t Distributions
Sparse Parameterization for Epitomic Dataset Distillation
Neural Methods for Point-wise Dependency Estimation
Sodium entry efficiency during action potentials: A novel single-parameter family of Hodgkin-Huxley models
Pareto Multi-Task Learning
Norm-Ranging LSH for Maximum Inner Product Search
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Uncertainty-Driven Loss for Single Image Super-Resolution
Universal Domain Adaptation through Self Supervision
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
Optimal prediction of Markov chains with and without spectral gap
AQuA: A Benchmarking Tool for Label Quality Assessment
Derandomizing Multi-Distribution Learning
Analyzing Auditory Neurons by Learning Distance Functions
LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery
Bayesian Bellman Operators
Bandits with many optimal arms
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods
Clustering Billions of Reads for DNA Data Storage
WaveFake: A Data Set to Facilitate Audio Deepfake Detection
Calibrated Self-Rewarding Vision Language Models
Spectral discontinuity design: Interrupted time series with spectral mixture kernels
Linear-Time Probabilistic Solution of Boundary Value Problems
Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
Recurrently Controlled Recurrent Networks
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees
Root Mean Square Layer Normalization
Self-Supervised Visual Acoustic Matching
Distributional Reinforcement Learning for Risk-Sensitive Policies
Cross-Linked Unified Embedding for cross-modality representation learning
Information-Geometric Decomposition in Spike Analysis
Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation
Statistical-Query Lower Bounds via Functional Gradients
Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches co-located with the 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), Montreal, Canada, December 11-12, 2015
Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation
Sample Complexity Bounds for Iterative Stochastic Policy Optimization
Solving a Class of Non-Convex Minimax Optimization in Federated Learning
Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints
ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field
Universality in Learning from Linear Measurements
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models
Efficient Bregman Range Search
A two-scale Complexity Measure for Deep Learning Models
Neural Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning
VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
Active Learning for Function Approximation
Bi-Level Offline Policy Optimization with Limited Exploration
On the Overlooked Structure of Stochastic Gradients
Transductive Zero-Shot Learning with Visual Structure Constraint
Conditional Density Estimation with Histogram Trees
Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild
Learning Invariances using the Marginal Likelihood
Time Trials on Second-Order and Variable-Learning-Rate Algorithms
A Direct Formulation for Sparse PCA Using Semidefinite Programming
Algorithm Selection for Deep Active Learning with Imbalanced Datasets
SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Words
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects
SOFT: Softmax-free Transformer with Linear Complexity
Mind the Gap: Assessing Temporal Generalization in Neural Language Models
Bubblewrap: Online tiling and real-time flow prediction on neural manifolds
Learning rule influences recurrent network representations but not attractor structure in decision-making tasks
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control
On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice
The challenge of realistic music generation: modelling raw audio at scale
Outlier-robust estimation of a sparse linear model using \ell_1-penalized Huber's M-estimator
Concavity of reweighted Kikuchi approximation
A Parallel Analog CCD/CMOS Signal Processor
Safe and Efficient: A Primal-Dual Method for Offline Convex CMDPs under Partial Data Coverage
Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning
Polynomial time guarantees for the Burer-Monteiro method
Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation
An Improved Decomposition Algorithm for Regression Support Vector Machines
Contracting with a Learning Agent
Pure Exploration with Multiple Correct Answers
Optimistic Meta-Gradients
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
The Error Coding and Substitution PaCTs
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization
Learning a Small Mixture of Trees
Navigable Graphs for High-Dimensional Nearest Neighbor Search: Constructions and Limits
Are sample means in multi-armed bandits positively or negatively biased?
Support Vector Method for Multivariate Density Estimation
Adversarial Training Helps Transfer Learning via Better Representations
Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information
Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions
White Functionals for Anomaly Detection in Dynamical Systems
Into the LAION's Den: Investigating Hate in Multimodal Datasets
Distributed Distillation for On-Device Learning
CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming
Is Your LiDAR Placement Optimized for 3D Scene Understanding?
KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization
Warped Gaussian Processes
NICE: NoIse-modulated Consistency rEgularization for Data-Efficient GANs
Stability Results for Neural Networks
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
On the Exploration of Local Significant Differences For Two-Sample Test
The CHIR Algorithm for Feed Forward Networks with Binary Weights
A Cost Function for Internal Representations
On higher-order perceptron algorithms
Understanding Transformers via N-Gram Statistics
Entropy-based adaptive Hamiltonian Monte Carlo
Asynchronous Parallel Coordinate Minimization for MAP Inference
SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection
Toward Multimodal Image-to-Image Translation
Eigenvoice Speaker Adaptation via Composite Kernel PCA
Logarithmic Time Online Multiclass prediction
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
A General Theory of Equivariant CNNs on Homogeneous Spaces
Optimal Algorithms for Online Convex Optimization with Adversarial Constraints
Learning to Align from Scratch
DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching
Fast Learning from Non-i.i.d. Observations
Implicit Reparameterization Gradients
AdaTune: Adaptive Tensor Program Compilation Made Efficient
Chaos as an interpretable benchmark for forecasting and data-driven modelling
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
Remote Sensing Image Analysis via a Texture Classification Neural Network
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs
Learning Attentional Communication for Multi-Agent Cooperation
Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization
LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
Embedding-Aligned Language Models
A Sample Complexity Measure with Applications to Learning Optimal Auctions
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
Relative Loss Bounds, the Minimum Relative Entropy Principle, and EM
Flexible Modeling of Diversity with Strongly Log-Concave Distributions
Iterative Value-Aware Model Learning
Private Distribution Learning with Public Data: The View from Sample Compression
Zap Q-Learning
Proximal Deep Structured Models
Pattern Class Degeneracy in an Unrestricted Storage Density Memory
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks
Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations
Scalable Laplacian K-modes
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks
Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions
Unbiased estimates for linear regression via volume sampling
What do Graph Neural Networks learn? Insights from Tropical Geometry
Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature
Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
A Revolution: Belief Propagation in Graphs with Cycles
Fighting Copycat Agents in Behavioral Cloning from Observation Histories
Sample Complexity of Learning Mixture of Sparse Linear Regressions
Dip-means: an incremental clustering method for estimating the number of clusters
Tracking Functional Changes in Nonstationary Signals with Evolutionary Ensemble Bayesian Model for Robust Neural Decoding
AdaPKC: PeakConv with Adaptive Peak Receptive Field for Radar Semantic Segmentation
Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator
Not All Tokens Are What You Need for Pretraining
Measuring model complexity with the prior predictive
Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models
Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging
Majorization for CRFs and Latent Likelihoods
Weisfeiler and Lehman Go Cellular: CW Networks
Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning
Optimal Prediction of the Number of Unseen Species with Multiplicity
Optimal Learners for Realizable Regression: PAC Learning and Online Learning
PromptFix: You Prompt and We Fix the Photo
Encoding Human Behavior in Information Design through Deep Learning
Variational Structured Semantic Inference for Diverse Image Captioning
Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs
Learning to Generate Visual Questions with Noisy Supervision
Robustness between the worst and average case
Performative Control for Linear Dynamical Systems
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies
Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset
Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser
Cycles: A Simulation Tool for Studying Cyclic Neural Networks
Handling Missing Data with Graph Representation Learning
Non-parametric Modeling of Partially Ranked Data
Local and Adaptive Mirror Descents in Extensive-Form Games
Can Large Language Model Agents Simulate Human Trust Behavior?
Gaussian process based nonlinear latent structure discovery in multivariate spike train data
Differentially Private Federated Bayesian Optimization with Distributed Exploration
Reranking Laws for Language Generation: A Communication-Theoretic Perspective
Mitigating Biases in Blackbox Feature Extractors for Image Classification Tasks
Sparse Multi-Task Reinforcement Learning
The Lottery Ticket Hypothesis for Pre-trained BERT Networks
Transformers on Markov data: Constant depth suffices
Reward Propagation Using Graph Convolutional Networks
LVD-2M: A Long-take Video Dataset with Temporally Dense Captions
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis
An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization
DSR: Dynamical Surface Representation as Implicit Neural Networks for Protein
Unified Segment-to-Segment Framework for Simultaneous Sequence Generation
Learning Distributions on Manifolds with Free-Form Flows
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
Fine-Grained Zero-Shot Learning with DNA as Side Information
Driving SMARTS Competition at NeurIPS 2022: Insights and Outcome
On Contrastive Representations of Stochastic Processes
Effects of Spike Timing Underlying Binocular Integration and Rivalry in a Neural Model of Early Visual Cortex
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm
Linear programming analysis of loopy belief propagation for weighted matching
Task Me Anything
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection
A Recurrent Neural Network for Multiple Language Acquisition: Starting with English and French
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Bootstrapping Apprenticeship Learning
Large-Scale Differentiable Causal Discovery of Factor Graphs
Rethinking Conditional Diffusion Sampling with Progressive Guidance
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators
Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction
A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings
On Invariance in Hierarchical Models
Beyond MLE: Convex Learning for Text Generation
Pruning neural network models for gene regulatory dynamics using data and domain knowledge
HitNet: Hybrid Ternary Recurrent Neural Network
Design of Experiments for Stochastic Contextual Linear Bandits
On the Optimality of Incremental Neural Network Algorithms
Active Classification based on Value of Classifier
Differentiable Multiple Shooting Layers
Provable Generalization of Overparameterized Meta-learning Trained with SGD
Node Dependent Local Smoothing for Scalable Graph Learning
K-Net: Towards Unified Image Segmentation
Dependent Dirichlet Process Spike Sorting
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data
Separations in the Representational Capabilities of Transformers and Recurrent Architectures
Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
Cold-Start Reinforcement Learning with Softmax Policy Gradient
Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis