yusuke1997/dblp-id-title huggingface dataset:
Learning to Optimize in Swarms
Kernelized Bayesian Softmax for Text Generation
Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures
S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Learning Classification with Unlabeled Data
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
Variable Binding through Assemblies in Spiking Neural Networks
Compressible-composable NeRF via Rank-residual Decomposition
On Feature Learning in the Presence of Spurious Correlations
Neural Discrete Representation Learning
Graph Neural Network Bandits
CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation
Central and Pairwise Data Clustering by Competitive Neural Networks
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Is one annotation enough? - A data-centric image classification benchmark for noisy and ambiguous label estimation
Domain Adaptation for Large-Vocabulary Object Detectors
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively
Stochastic and Adversarial Online Learning without Hyperparameters
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization
A permutation-free kernel two-sample test
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
Towards Calibrated Robust Fine-Tuning of Vision-Language Models
Sample Complexity of Forecast Aggregation
VTC-LFC: Vision Transformer Compression with Low-Frequency Components
Relaxation Networks for Large Supervised Learning Problems
Learning the optimal Tikhonov regularizer for inverse problems
Credit Assignment For Collective Multiagent RL With Global Rewards
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks
RADAR: Robust AI-Text Detection via Adversarial Learning
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space
Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets
Connecting to the Past
Generator Born from Classifier
BNS: Building Network Structures Dynamically for Continual Learning
Retrosynthesis Prediction with Conditional Graph Logic Network
Recurrent neural network dynamical systems for biological vision
On the Expressive Power of Deep Polynomial Neural Networks
Satformer: Accurate and Robust Traffic Data Estimation for Satellite Networks
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery
Meta-Gaussian Information Bottleneck
Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Universality and Limitations of Prompt Tuning
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards
Predicting Action Content On-Line and in Real Time before Action Onset - an Intracranial Human Study
Classification Calibration Dimension for General Multiclass Losses
Autonomous Agents for Collaborative Task under Information Asymmetry
Smoothed Geometry for Robust Attribution
LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation
Active Support Vector Machine Classification
Model Fusion via Optimal Transport
Generalization of Reinforcement Learners with Working and Episodic Memory
Face Detection - Efficient and Rank Deficient
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
An Empirical Study of Adder Neural Networks for Object Detection
SOLOv2: Dynamic and Fast Instance Segmentation
Automating Bayesian optimization with Bayesian optimization
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features
Multiply Robust Federated Estimation of Targeted Average Treatment Effects
Newscast EM
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions
Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
Improving black-box optimization in VAE latent space using decoder uncertainty
IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS
Opponent Modeling based on Subgoal Inference
Distribution Matching for Crowd Counting
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
From Online to Batch Learning with Cutoff-Averaging
The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains
Efficient Large Multi-modal Models via Visual Context Compression
Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context
MC-DiT: Contextual Enhancement via Clean-to-Clean Reconstruction for Masked Diffusion Models
Mitigating Object Hallucination via Concentric Causal Attention
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Sparse Structure Search for Delta Tuning
An empirical analysis of compute-optimal large language model training
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
Connectionist Implementation of a Theory of Generalization
BCORLE(λ): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market
Maxing and Ranking with Few Assumptions
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
Deep Learning Frameworks for Weakly-Supervised Indoor Localization
A Nonparametric Approach to Bottom-Up Visual Saliency
Composite Feature Selection Using Deep Ensembles
Prior-Free Dynamic Auctions with Low Regret Buyers
MagNet: A Neural Network for Directed Graphs
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration
Seeing Beyond the Crop: Using Language Priors for Out-of-Bounding Box Keypoint Prediction
Discovering and Achieving Goals via World Models
Teaching an Active Learner with Contrastive Examples
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data
Learning convolution filters for inverse covariance estimation of neural network connectivity
Topic Modeling Revisited: A Document Graph-based Neural Network Perspective
Robust Recognition of Noisy and Superimposed Patterns via Selective Attention
Neuron Shapley: Discovering the Responsible Neurons
HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors
Detecting and Measuring Confounding Using Causal Mechanism Shifts
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding
Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis
Parametric Bandits: The Generalized Linear Case
DeViSE: A Deep Visual-Semantic Embedding Model
Partially-Supervised Image Captioning
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching
An equivalence between high dimensional Bayes optimal inference and M-estimation
A Primal Dual Formulation For Deep Learning With Constraints
A Simple Image Segmentation Framework via In-Context Examples
Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes
A spatially varying two-sample recombinant coalescent, with applications to HIV escape response
Optimization or Architecture: How to Hack Kalman Filtering
Variable Importance Using Decision Trees
Toward a Single-Cell Account for Binocular Disparity Tuning: An Energy Model May Be Hiding in Your Dendrites
Product Ranking for Revenue Maximization with Multiple Purchases
Budgeting Counterfactual for Offline RL
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes
Parallelized Stochastic Gradient Descent
Non-monotone Submodular Maximization in Exponentially Fewer Iterations
Unsupervised Domain Adaptation for Semantic Segmentation using Depth Distribution
Lie Point Symmetry and Physics-Informed Networks
Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Randomized Strategic Facility Location with Predictions
Information-theoretic Generalization Analysis for Expected Calibration Error
Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning
Multi-Reward Best Policy Identification
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
EM Distillation for One-step Diffusion Models
Fast Embedding of Sparse Similarity Graphs
A Convex Upper Bound on the Log-Partition Function for Binary Distributions
Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
An Apobayesian Relative of Winnow
VOCE: Variational Optimization with Conservative Estimation for Offline Safe Reinforcement Learning
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models
RL for Latent MDPs: Regret Guarantees and a Lower Bound
Modern Hopfield Networks and Attention for Immune Repertoire Classification
AMDP: An Adaptive Detection Procedure for False Discovery Rate Control in High-Dimensional Mediation Analysis
Curriculum Learning by Dynamic Instance Hardness
Integration Methods and Optimization Algorithms
Traffic4cast at NeurIPS 2020 ? yet more on theunreasonable effectiveness of gridded geo-spatial processes
The Wasserstein Proximal Gradient Algorithm
DominoSearch: Find layer-wise fine-grained N: M sparse schemes from dense neural networks
A simple model of recognition and recall memory
Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition
Conformal PID Control for Time Series Prediction
Metric Learning for Adversarial Robustness
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective
Post-hoc estimators for learning to defer to an expert
ZeroMark: Towards Dataset Ownership Verification without Disclosing Watermark
A Combinatorial Perspective on Transfer Learning
MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval
Tanimoto Random Features for Scalable Molecular Machine Learning
Extensions of a Theory of Networks for Approximation and Learning
Combined Neural Networks for Time Series Analysis
Speech-T: Transducer for Text to Speech and Beyond
Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares
Sparse Prediction with the $k$-Support Norm
Barrier Frank-Wolfe for Marginal Inference
Not Just Object, But State: Compositional Incremental Learning without Forgetting
Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]
Predictive State Temporal Difference Learning
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning
Off-Team Learning
Finding the M Most Probable Configurations in Arbitrary Graphical Models
Focus of Attention Improves Information Transfer in Visual Features
M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data
Provably tuning the ElasticNet across instances
A Benchmark for Interpretability Methods in Deep Neural Networks
Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
On the distance between two neural networks and the stability of learning
Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting
A Metadata-Driven Approach to Understand Graph Neural Networks
SS1: Accelerating Inference with Fast and Expressive Sketch Structured Transform
Algebraic tests of general Gaussian latent tree models
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
A Local Temporal Difference Code for Distributional Reinforcement Learning
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks
Adversarial Attacks on Linear Contextual Bandits
Complexity of Inference in Latent Dirichlet Allocation
SHDocs: A dataset, benchmark, and method to efficiently generate high-quality, real-world specular highlight data with near-perfect alignment
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis
Measuring Dejavu Memorization Efficiently
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Handling Missing Data with Variational Bayesian Learning of ICA
Extended ICA Removes Artifacts from Electroencephalographic Recordings
Directed Probabilistic Watershed
Probabilistic Joint Image Segmentation and Labeling
Towards Universal Mesh Movement Networks
Obstacle Avoidance through Reinforcement Learning
Learning to Mutate with Hypergradient Guided Population
Self-Supervised Relationship Probing
Distribution-free inference for regression: discrete, continuous, and in between
Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union
Group Robust Preference Optimization in Reward-free RLHF
RANet: Region Attention Network for Semantic Segmentation
Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery
Improving Deep Learning Optimization through Constrained Parameter Regularization
Enhancing Domain Adaptation through Prompt Gradient Alignment
Connecting Multi-modal Contrastive Representations
Smoothed Embeddings for Certified Few-Shot Learning
A Bayesian Framework for Modeling Confidence in Perceptual Decision Making
GraphVis: Boosting LLMs with Visual Knowledge Graph Integration
Using Backpropagation with Temporal Windows to Learn the Dynamics of the CMU Direct-Drive Arm II
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
Cycle-Contrast for Self-Supervised Video Representation Learning
Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving
Spatial distance dependent Chinese restaurant processes for image segmentation
On the Dirichlet Prior and Bayesian Regularization
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time
SnAKe: Bayesian Optimization with Pathwise Exploration
Modeling Acoustic Correlations by Factor Analysis
Deep Mean-Shift Priors for Image Restoration
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation
SLAYER: Spike Layer Error Reassignment in Time
Leveraging Sparsity for Efficient Submodular Data Summarization
Zero-shot recognition with unreliable attributes
On learning sparse vectors from mixture of responses
Alignment Attention by Matching Key and Query Distributions
Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities
High-recall causal discovery for autocorrelated time series with latent confounders
ZIN: When and How to Learn Invariance Without Environment Partition?
Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
Diversity Matters When Learning From Ensembles
Mixture Modeling by Affinity Propagation
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression
MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes
Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
From Predictions to Decisions: Using Lookahead Regularization
Online Active Learning with Surrogate Loss Functions
Retiring Adult: New Datasets for Fair Machine Learning
Joint Data-Task Generation for Auxiliary Learning
Learning Gaussian Graphical Models with Observed or Latent FVSs
Neural Relational Inference with Fast Modular Meta-learning
The Multiple Quantile Graphical Model
Channel Noise in Excitable Neural Membranes
Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
Buy-in-Bulk Active Learning
Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning
ZARTS: On Zero-order Optimization for Neural Architecture Search
A Neural SIR Model for Global Forecasting
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization
An Application of Boosting to Graph Classification
Approximate Feature Collisions in Neural Nets
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
Efficient Formal Safety Analysis of Neural Networks
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
Principled Architecture Selection for Neural Networks: Application to Corporate Bond Rating Prediction
Assembly Fuzzy Representation on Hypergraph for Open-Set 3D Object Retrieval
Saliency-Driven Image Acuity Modulation on a Reconfigurable Array of Spiking Silicon Neurons
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis
Static and Sequential Malicious Attacks in the Context of Selective Forgetting
Structural Credit Assignment in Neural Networks using Reinforcement Learning
A Retrieve-and-Edit Framework for Predicting Structured Outputs
Boost Neural Networks by Checkpoints
A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories
Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization
Optimal Policies Tend To Seek Power
Suggesting Variable Order for Cylindrical Algebraic Decomposition via Reinforcement Learning
Neural System Model of Human Sound Localization
Graph neural networks and non-commuting operators
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
Belief-State Query Policies for User-Aligned POMDPs
Large Scale Adversarial Representation Learning
Minimum Weight Perfect Matching via Blossom Belief Propagation
Multi-label Contrastive Predictive Coding
Information Geometry of the Retinal Representation Manifold
Unsupervised Learning of Distributions of Binary Vectors Using 2-Layer Networks
Copycats: the many lives of a publicly available medical imaging dataset
Visual Pinwheel Centers Act as Geometric Saliency Detectors
Gliding over the Pareto Front with Uniform Designs
Myersonian Regression
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Online Map Vectorization for Autonomous Driving: A Rasterization Perspective
UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
Semantics and Spatiality of Emergent Communication
Automatic differentiation in ML: Where we are and where we should be going
Gradient-based Editing of Memory Examples for Online Task-free Continual Learning
Rethinking the Variational Interpretation of Accelerated Optimization Methods
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory
Neural Stochastic Control
A provable SVD-based algorithm for learning topics in dominant admixture corpus
Active Learning of Convex Halfspaces on Graphs
A Foundation Model for Zero-shot Logical Query Reasoning
Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox
Invert to Learn to Invert
Drift doesn't Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection
"Why Not Looking backward?" A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
Language Semantic Graph Guided Data-Efficient Learning
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
Distributionally Robust Markov Decision Processes
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
Feature-Level Adversarial Attacks and Ranking Disruption for Visible-Infrared Person Re-identification
Enhanced Latent Space Blind Model for Real Image Denoising via Alternative Optimization
Exact Convex Confidence-Weighted Learning
Adam with model exponential moving average is effective for nonconvex optimization
Latent Diffusion for Neural Spiking Data
Learning Continuous Attractors in Recurrent Networks
Reimagining Mutual Information for Enhanced Defense against Data Leakage in Collaborative Inference
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
Estimating Generic 3D Room Structures from 2D Annotations
One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation
A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
Recursive Reinforcement Learning
Communication-Efficient Distributed Learning of Discrete Distributions
Convex Repeated Games and Fenchel Duality
Mr. HiSum: A Large-scale Dataset for Video Highlight Detection and Summarization
Intersecting Regions: The Key to Combinatorial Structure in Hidden Unit Space
On the Necessity of Collaboration for Online Model Selection with Decentralized Data
GFT: Graph Foundation Model with Transferable Tree Vocabulary
Principles of Riemannian Geometry in Neural Networks
Gaussian Copula Embeddings
Speech Recognition Experiments with Perceptrons
Make Your LLM Fully Utilize the Context
What Makes Good Examples for Visual In-Context Learning?
Statistical Reliability of a Blowfly Movement-Sensitive Neuron
On Explore-Then-Commit strategies
Learning spatiotemporal trajectories from manifold-valued longitudinal data
Optimizing Mode Connectivity via Neuron Alignment
Optimal Multi-Fidelity Best-Arm Identification
Quantized Training of Gradient Boosting Decision Trees