yusuke1997/dblp-id-title huggingface dataset:
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations
A Metalearned Neural Circuit for Nonparametric Bayesian Inference
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
Unsupervised Spectral Learning of Finite State Transducers
Energy Disaggregation via Discriminative Sparse Coding
Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
Recovering Bandits
Deep Dynamic Poisson Factorization Model
Boundary Guided Learning-Free Semantic Control with Diffusion Models
Active Learning of General Halfspaces: Label Queries vs Membership Queries
Goal-directed Generation of Discrete Structures with Conditional Generative Models
GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations
Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions
Explaining Latent Representations with a Corpus of Examples
GOOD: A Graph Out-of-Distribution Benchmark
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
Self-Organized Group for Cooperative Multi-agent Reinforcement Learning
Penalising the biases in norm regularisation enforces sparsity
Cycle Self-Training for Domain Adaptation
Controlling the Complexity of HMM Systems by Regularization
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios
Sparse Covariance Modeling in High Dimensions with Gaussian Processes
Rank-N-Contrast: Learning Continuous Representations for Regression
Hierarchical Object-Aware Dual-Level Contrastive Learning for Domain Generalized Stereo Matching
Trading off Utility, Informativeness, and Complexity in Emergent Communication
Competition Adds Complexity
A benchmark for prediction of transcriptomic responses to chemical perturbations across cell types
DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut
Monte-Carlo Tree Search by Best Arm Identification
LOVA3: Learning to Visual Question Answering, Asking and Assessment
Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels
EASI: Evolutionary Adversarial Simulator Identification for Sim-to-Real Transfer
Churn Reduction in the Wireless Industry
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks
A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model
Deep Equilibrium Approaches to Diffusion Models
Preference Alignment with Flow Matching
Diversifying Spatial-Temporal Perception for Video Domain Generalization
Ordered Subgraph Aggregation Networks
Asymptotically Optimal Quantile Pure Exploration for Infinite-Armed Bandits
Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability
Variance Reduced Stochastic Gradient Descent with Neighbors
Near-Optimal No-Regret Learning in General Games
GFlowNet Assisted Biological Sequence Editing
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
Estimating disparity with confidence from energy neurons
Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
Temporal Patterns of Activity in Neural Networks
Efficient Training of Low-Curvature Neural Networks
The Sigmoid Nonlinearity in Prepyriform Cortex
VPGTrans: Transfer Visual Prompt Generator across LLMs
Improving Generalization and Convergence by Enhancing Implicit Regularization
SwiFT: Swin 4D fMRI Transformer
Absolute Neighbour Difference based Correlation Test for Detecting Heteroscedastic Relationships
Learning in the Vestibular System: Simulations of Vestibular Compensation Using Recurrent Back-Propagation
Beauty-in-averageness and its contextual modulations: A Bayesian statistical account
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
Lexinvariant Language Models
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice
Benchmark for Compositional Text-to-Image Synthesis
ICA based on a Smooth Estimation of the Differential Entropy
Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning
Globally Q-linear Gauss-Newton Method for Overparameterized Non-convex Matrix Sensing
Mixture Proportion Estimation and PU Learning: A Modern Approach
Towards A Richer 2D Understanding of Hands at Scale
PRODIGY: Enabling In-context Learning Over Graphs
Safe and Nested Subgame Solving for Imperfect-Information Games
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
Instance-Specific Bayesian Model Averaging for Classification
On the Convergence of Eigenspaces in Kernel Principal Component Analysis
TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight
Preference learning along multiple criteria: A game-theoretic perspective
Distributionally robust weighted k-nearest neighbors
Decoupling "when to update" from "how to update"
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
Towards robust and domain agnostic reinforcement learning competitions: MineRL 2020
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
Testably Learning Polynomial Threshold Functions
Triad Constraints for Learning Causal Structure of Latent Variables
Deep Compression of Pre-trained Transformer Models
Multi-Fidelity Multi-Armed Bandits Revisited
What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
Improved Analysis for Bandit Learning in Matching Markets
Enforcing balance allows local supervised learning in spiking recurrent networks
Anti-Backdoor Learning: Training Clean Models on Poisoned Data
SDP Relaxation with Randomized Rounding for Energy Disaggregation
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression
Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding
Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
Learning and processing the ordinal information of temporal sequences in recurrent neural circuits
Improving Neural Network Surface Processing with Principal Curvatures
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization
Probabilistic Characterization of Neural Model Computations
Stress-Testing Capability Elicitation With Password-Locked Models
Credit Assignment Through Broadcasting a Global Error Vector
Analyzing Hogwild Parallel Gaussian Gibbs Sampling
Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers
Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation
Using Bayesian Dynamical Systems for Motion Template Libraries
Diffusion-based Molecule Generation with Informative Prior Bridges
OpenMask3D: Open-Vocabulary 3D Instance Segmentation
Identifying Selections for Unsupervised Subtask Discovery
Scaling Open-Vocabulary Object Detection
Generalised Mutual Information for Discriminative Clustering
Image Textualization: An Automatic Framework for Generating Rich and Detailed Image Descriptions
A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications
Bregman Alternating Direction Method of Multipliers
Intransitive Likelihood-Ratio Classifiers
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
Practical Deep Learning with Bayesian Principles
Asymptotic Convergence of Backpropagation: Numerical Experiments
What Can We Learn from Unlearnable Datasets?
D4: Improving LLM Pretraining via Document De-Duplication and Diversification
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification
Learning Structured Distributions From Untrusted Batches: Faster and Simpler
A Model for Chemosensory Reception
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Learning Models for Actionable Recourse
FlexCap: Describe Anything in Images in Controllable Detail
Stochastic Neighbor Embedding
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
Difference of Convex Functions Programming for Reinforcement Learning
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations
GULP: a prediction-based metric between representations
Collaborating with Humans without Human Data
Self-Organizing and Adaptive Algorithms for Generalized Eigen-Decomposition
Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor
Model Spider: Learning to Rank Pre-Trained Models Efficiently
Flamingo: a Visual Language Model for Few-Shot Learning
Accelerated Mirror Descent in Continuous and Discrete Time
Multi-State Time Delay Networks for Continuous Speech Recognition
Distral: Robust multitask reinforcement learning
Scale-invariant Learning by Physics Inversion
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation
Canonical normalizing flows for manifold learning
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
Globally injective and bijective neural operators
Adversarial Learning for Feature Shift Detection and Correction
Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain
Self-calibrating Probability Forecasting
Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling
Diversity Is Not All You Need: Training A Robust Cooperative Agent Needs Specialist Partners
Multi-scale Diffusion Denoised Smoothing
Eye Micro-movements Improve Stimulus Detection Beyond the Nyquist Limit in the Peripheral Retina
Learning a discriminative hidden part model for human action recognition
Pictorial Structures for Molecular Modeling: Interpreting Density Maps
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
Solving Stochastic Games
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve
Improved Frequency Estimation Algorithms with and without Predictions
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning
Differentially Private M-Estimators
Seek Commonality but Preserve Differences: Dissected Dynamics Modeling for Multi-modal Visual RL
Copula variational inference
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Classification Diffusion Models: Revitalizing Density Ratio Estimation
Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision
Robust Feature-Level Adversaries are Interpretability Tools
Topological RANSAC for instance verification and retrieval without fine-tuning
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Hierarchical Bayesian Inference in Networks of Spiking Neurons
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
How does a Neural Network's Architecture Impact its Robustness to Noisy Labels?
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
Finite Sample Analysis Of Dynamic Regression Parameter Learning
Robust Optimization for Fairness with Noisy Protected Groups
AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
High-Resolution Image Harmonization with Adaptive-Interval Color Transformation
On the Planning Abilities of Large Language Models - A Critical Investigation
SongCreator: Lyrics-based Universal Song Generation
FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation
Breaking the Communication-Privacy-Accuracy Trilemma
Mixtures of Experts for Audio-Visual Learning
Correlated Topic Models
Enriching Disentanglement: From Logical Definitions to Quantitative Metrics
A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory
Bayesian Bits: Unifying Quantization and Pruning
Comparison Training for a Rescheduling Problem in Neural Networks
Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images
Multistable Shape from Shading Emerges from Patch Diffusion
Compositional Plan Vectors
SignRFF: Sign Random Fourier Features
Density-Difference Estimation
A Geometric Analysis of Neural Collapse with Unconstrained Features
Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization
Neighbourhood Consensus Networks
Extending Gossip Algorithms to Distributed Estimation of U-statistics
A neurally plausible model learns successor representations in partially observable environments
Optimal learning rates for Kernel Conjugate Gradient regression
RETR: Multi-View Radar Detection Transformer for Indoor Perception
Regret in Online Recommendation Systems
Learning with Labeling Induced Abstentions
A Study of Parallel Perturbative Gradient Descent
OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment
John Ellipsoids via Lazy Updates
Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images
What Do Deep Saliency Models Learn about Visual Attention?
Learning low-dimensional generalizable natural features from retina using a U-net
Accounting for network effects in neuronal responses using L1 regularized point process models
Confusion-Based Online Learning and a Passive-Aggressive Scheme
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods
FastEx: Hash Clustering with Exponential Families
Uniform Sampling over Episode Difficulty
Representing and Incorporating Prior Knowledge in Neural Network Training: Preface
Bayesian Hierarchical Community Discovery
Geometric-Averaged Preference Optimization for Soft Preference Labels
Decorate3D: Text-Driven High-Quality Texture Generation for Mesh Decoration in the Wild
Bayesian Optimization with Robust Bayesian Neural Networks
FlowPG: Action-constrained Policy Gradient with Normalizing Flows
WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts
Quantizing Density Estimators
Constrained Robust Submodular Partitioning
Robust Spectral Inference for Joint Stochastic Matrix Factorization
Assisted Learning: A Framework for Multi-Organization Learning
A simple example of Dirichlet process mixture inconsistency for the number of components
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra
Conformal Time-series Forecasting
The Secretary Problem with Predicted Additive Gap
PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
Learning Disentangled Joint Continuous and Discrete Representations
In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
Confounding Feature Acquisition for Causal Effect Estimation
Discovering Hidden Features with Gaussian Processes Regression
Scaling Laws in Natural Scenes and the Inference of 3D Shape
SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models
Boosting the Performance of RBF Networks with Dynamic Decay Adjustment
Parameter tuning and model selection in Optimal Transport with semi-dual Brenier formulation
How can classical multidimensional scaling go wrong?
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
Optimal Preconditioning and Fisher Adaptive Langevin Sampling
Adaptive Risk Minimization: Learning to Adapt to Domain Shift
Exact Recovery of Hard Thresholding Pursuit
CLEAR: Generative Counterfactual Explanations on Graphs
Using Genetic Algorithms to Improve Pattern Classification Performance
A loss framework for calibrated anomaly detection
Mesoscopic modeling of hidden spiking neurons
Wasserstein Training of Restricted Boltzmann Machines
Feature-distributed sparse regression: a screen-and-clean approach
GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization
Should Under-parameterized Student Networks Copy or Average Teacher Weights?
Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization
Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling
Long Range Graph Benchmark
Repulsive Deep Ensembles are Bayesian
A Parallel Mixture of SVMs for Very Large Scale Problems
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
A mean-field analysis of two-player zero-sum games
Increasing Confidence in Adversarial Robustness Evaluations
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
Consistent Structural Relation Learning for Zero-Shot Segmentation
Multimodal and Multilingual Embeddings for Large-Scale Speech Mining
Minimum-Risk Recalibration of Classifiers
Linear Convergence of Gradient Methods for Estimating Structured Transition Matrices in High-dimensional Vector Autoregressive Models
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
A Unified Approach to Count-Based Weakly Supervised Learning
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
Pooling by Sliced-Wasserstein Embedding
Structured Prediction Theory Based on Factor Graph Complexity
Sparse Bayesian Multi-Task Learning
Visual Perception by Large Language Model's Weights
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
CulturePark: Boosting Cross-cultural Understanding in Large Language Models
Adaptive Sampling for Efficient Softmax Approximation
Bayesian Robustification for Audio Visual Fusion
PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting
Provably Robust Temporal Difference Learning for Heavy-Tailed Rewards
A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
GEO-Bench: Toward Foundation Models for Earth Monitoring
Giga-scale Kernel Matrix-Vector Multiplication on GPU
Streaming Long Video Understanding with Large Language Models
Visualizing and sonifying how an artificial ear hears music
Abrupt Learning in Transformers: A Case Study on Matrix Completion
Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective
On Episodes, Prototypical Networks, and Few-Shot Learning
Bayesian Learning via Stochastic Dynamics
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
Activity Driven Adaptive Stochastic Resonance
Robust k-means: a Theoretical Revisit
Incremental Learning for Visual Tracking
Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Generalization Bounds for Domain Adaptation
Variance-Aware Off-Policy Evaluation with Linear Function Approximation
Learning single-index models with shallow neural networks
Online MAP Inference of Determinantal Point Processes
Predicting Future Actions of Reinforcement Learning Agents
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning
Pairwise Choice Markov Chains
Hardness of Learning Neural Networks with Natural Weights
Towards Symmetry-Aware Generation of Periodic Materials
Learning to Orient Surfaces by Self-supervised Spherical CNNs
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records
Meta-Learning Requires Meta-Augmentation
Non-asymptotic Error Bounds for Bidirectional GANs
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits
GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients
Compressive Visual Representations
Stable Dynamic Parameter Adaption
Beyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and Identify
Data-driven Distributionally Robust Polynomial Optimization
The spiked matrix model with generative priors
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
Characterization of Excess Risk for Locally Strongly Convex Population Risk
A Simple Baseline for Visual Commonsense Reasoning
Biological credit assignment through dynamic inversion of feedforward networks
MELONET I: Neural Nets for Inventing Baroque-Style Chorale Variations
Real-World Image Super-Resolution as Multi-Task Learning
Expectation Propagation for t-Exponential Family Using q-Algebra
Prediction-Powered Ranking of Large Language Models
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation
Sub-sampled Newton Methods with Non-uniform Sampling
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability
UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
Likelihood Ratio Confidence Sets for Sequential Decision Making
Expressive Sign Equivariant Networks for Spectral Geometric Learning
Emergent Graphical Conventions in a Visual Communication Game
SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization
Mining Multi-Label Samples from Single Positive Labels
Implicit Curriculum in Procgen Made Explicit
Provably Efficient Model-Free Constrained RL with Linear Function Approximation
Revisiting (\epsilon, \gamma, \tau)-similarity learning for domain adaptation
Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry