yusuke1997/dblp-id-title huggingface dataset:
Alternating Minimization for Regression Problems with Vector-valued Outputs
XNAS: Neural Architecture Search with Expert Advice
Ranking Data with Continuous Labels through Oriented Recursive Partitions
Provable Benefit of Cutout and CutMix for Feature Learning
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Searching for Efficient Transformers for Language Modeling
An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Q-Learning with Hidden-Unit Restarting
Information Maximization for Few-Shot Learning
Structured output regression for detection with partial truncation
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
The Many Faces of Adversarial Risk
Maximum Entropy Discrimination
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models
Locally Differentially Private (Contextual) Bandits Learning
GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation
Sharp Calibrated Gaussian Processes
Hidden Markov Model} Induction by Bayesian Model Merging
A Concept-Based Explainability Framework for Large Multimodal Models
Differentially Private Online-to-batch for Smooth Losses
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate
Learning to Learn with Feedback and Local Plasticity
Practical Two-Step Lookahead Bayesian Optimization
Learning Combinatorial Optimization Algorithms over Graphs
Language Conditioned Spatial Relation Reasoning for 3D Object Grounding
A scaled Bregman theorem with applications
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
Real-Time Particle Filters
Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning
Isaac Gym: High Performance GPU Based Physics Simulation For Robot Learning
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
Learning Rate Free Bayesian Inference in Constrained Domains
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
Network Flow Algorithms for Structured Sparsity
Nonstationary Covariance Functions for Gaussian Process Regression
A Unified Near-Optimal Estimator For Dimension Reduction in l alpha (0 < alpha <= 2) Using Stable Random Projections
Learning on Graph with Laplacian Regularization
Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm
FLAIR: Federated Learning Annotated Image Repository
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions
Aligning Language Models with Human Preferences via a Bayesian Approach
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Deep Counterfactual Estimation with Categorical Background Variables
Hedging in games: Faster convergence of external and swap regrets
Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control
Generalized Information-theoretic Multi-view Clustering
Mode Connectivity in Auction Design
Functional Variational Inference based on Stochastic Process Generators
Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model
Understanding Benign Overfitting in Gradient-Based Meta Learning
HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation
Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
Preconditioning Matters: Fast Global Convergence of Non-convex Matrix Factorization via Scaled Gradient Descent
Provably Powerful Graph Networks
Simplifying Neural Network Training Under Class Imbalance
Optimal Transport-Guided Conditional Score-Based Diffusion Model
A Neurodynamical Approach to Visual Attention
Learning and Testing Causal Models with Interventions
Meta-Learning Representations for Continual Learning
Global Optimisation of Neural Network Models via Sequential Sampling
A Theoretical Framework for Target Propagation
Classifier-guided Gradient Modulation for Enhanced Multimodal Learning
Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding
STaR: Bootstrapping Reasoning With Reasoning
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
Advancing Video Anomaly Detection: A Concise Review and a New Dataset
Spectral embedding for dynamic networks with stability guarantees
A Hierarchical Model of Visual Rivalry
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows
Beating SGD: Learning SVMs in Sublinear Time
Error Bounds for Learning with Vector-Valued Random Features
Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
Training Conditional Random Fields for Maximum Labelwise Accuracy
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
A Theory of Multiclass Boosting
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
Understanding and Improving Training-free Loss-based Diffusion Guidance
Proximity-Informed Calibration for Deep Neural Networks
Dimensionality Reduction for Wasserstein Barycenter
S 3 -NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes
Network Generality, Training Required, and Precision Required
Continuous Speech Recognition by Linked Predictive Neural Networks
Smart Vision Chip Fabricated Using Three Dimensional Integration Technology
Reining Generalization in Offline Reinforcement Learning via Representation Distinction
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
A Bayesian Network for Real-Time Musical Accompaniment
Multi-Chain Graphs of Graphs: A New Approach to Analyzing Blockchain Datasets
Generalized Dantzig Selector: Application to the k-support norm
Solving Inverse Problems via Diffusion Optimal Control
Dimensionality reduction: theoretical perspective on practical measures
Locally Private Gaussian Estimation
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations
How to Combine Expert (and Novice) Advice when Actions Impact the Environment?
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
Photonic Differential Privacy with Direct Feedback Alignment
Navigating Extremes: Dynamic Sparsity in Large Output Spaces
Bayesian Probabilistic Numerical Integration with Tree-Based Models
Tricks for Time Series: Preface
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
Toward Robust Spiking Neural Network Against Adversarial Perturbation
Beyond spectral gap: the role of the topology in decentralized learning
SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification
COOM: A Game Benchmark for Continual Reinforcement Learning
Inferring stochastic low-rank recurrent neural networks from neural data
On U-processes and clustering performance
A Bayesian Model of Conditioned Perception
Neural (Tangent Kernel) Collapse
Crafting Interpretable Embeddings for Language Neuroscience by Asking LLMs Questions
Restoring the Generalizability of SVM Based Decoding in High Dimensional Neuroimage Data
Active Exploration for Inverse Reinforcement Learning
The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Leveraging Drift to Improve Sample Complexity of Variance Exploding Diffusion Models
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Factuality Enhanced Language Models for Open-Ended Text Generation
Anonymous and Copy-Robust Delegations for Liquid Democracy
Online Neural Sequence Detection with Hierarchical Dirichlet Point Process
LOVM: Language-Only Vision Model Selection
An Algorithm for Learning Switched Linear Dynamics from Data
A primal-dual method for conic constrained distributed optimization problems
Learning, Regularization and Ill-Posed Inverse Problems
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Reinforcement Learning in Markovian and Non-Markovian Environments
Learning and Generalization in RNNs
Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
Dungeons and Data: A Large-Scale NetHack Dataset
How does PDE order affect the convergence of PINNs?
Group-wise oracle-efficient algorithms for online multi-group learning
Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Set
Reducing multiclass to binary by coupling probability estimates
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
Learning Distributions Generated by One-Layer ReLU Networks
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality
Efficiently escaping saddle points on manifolds
PEACE: A Dataset of Pharmaceutical Care for Cancer Pain Analgesia Evaluation and Medication Decision
Optimization and Bayes: A Trade-off for Overparameterized Neural Networks
Exploring Question Decomposition for Zero-Shot VQA
Simple Local Models for Complex Dynamical Systems
Receding Horizon Inverse Reinforcement Learning
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery
Fast Convergence of Regularized Learning in Games
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Real-World Image Variation by Aligning Diffusion Inversion Chain
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems
SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
Harnessing Hard Mixed Samples with Decoupled Regularizer
Conditional Random Fields with High-Order Features for Sequence Labeling
Transformer-based Working Memory for Multiagent Reinforcement Learning with Action Parsing
The Gaussian Process Density Sampler
Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss
Clustering Sequences with Hidden Markov Models
Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Correlates of Attention in a Model of Dynamic Visual Recognition
Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
GameTraversalBenchmark: Evaluating Planning Abilities Of Large Language Models Through Traversing 2D Game Maps
Almost Surely Stable Deep Dynamics
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
Sampling from Probabilistic Submodular Models
EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content
Stochastic Bias-Reduced Gradient Methods
Incentivizing Combinatorial Bandit Exploration
Distinguishing Distributions When Samples Are Strategically Transformed
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
Clustering with the Fisher Score
Relaxed Clipping: A Global Training Method for Robust Regression and Classification
Continuous vs. Discrete Optimization of Deep Neural Networks
Demystify Mamba in Vision: A Linear Attention Perspective
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks
Reflexion: language agents with verbal reinforcement learning
DAGER: Exact Gradient Inversion for Large Language Models
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models
The Statistical Cost of Robust Kernel Hyperparameter Turning
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
Microelectronic Implementations of Connectionist Neural Networks
Rebounding Bandits for Modeling Satiation Effects
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls
Layer-wise analysis of deep networks with Gaussian kernels
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
Improving Robustness of 3D Point Cloud Recognition from a Fourier Perspective
Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation
Online Learning for Adversaries with Memory: Price of Past Mistakes
Correlational Strength and Computational Algebra of Synaptic Connections Between Neurons
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model
Zero-shot causal learning
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
Entity Alignment with Noisy Annotations from Large Language Models
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model
Refined Learning Bounds for Kernel and Approximate $k$-Means
Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent Confounders
Partition and Code: learning how to compress graphs
Neural Sparse Representation for Image Restoration
Handcrafted Backdoors in Deep Neural Networks
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition
Puzzlefusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
Learning to Edit Visual Programs with Self-Supervision
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
Pruning vs Quantization: Which is Better?
Minimax Time Series Prediction
Doubly Hierarchical Geometric Representations for Strand-based Human Hairstyle Generation
Learning Task Specifications from Demonstrations
Grounded Reinforcement Learning: Learning to Win the Game under Human Commands
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems
AdanCA: Neural Cellular Automata As Adaptors For More Robust Vision Transformer
Composition Theorems for Interactive Differential Privacy
Full-Atom Protein Pocket Design via Iterative Refinement
Guiding Deep Molecular Optimization with Genetic Exploration
Actively Testing Your Model While It Learns: Realizing Label-Efficient Learning in Practice
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Higher-Order Uncoupled Dynamics Do Not Lead to Nash Equilibrium - Except When They Do
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games
Riemannian stochastic optimization methods avoid strict saddle points
Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction
GTA: A Benchmark for General Tool Agents
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
On Separate Normalization in Self-supervised Transformers
Testing Calibration in Nearly-Linear Time
Provable Subspace Identification Under Post-Nonlinear Mixtures
Normalizing Flows for Knockoff-free Controlled Feature Selection
Learning Large-Scale MTP 2 Gaussian Graphical Models via Bridge-Block Decomposition
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
Regularizing Neural Networks with Meta-Learning Generative Models
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
Segmentation Circuits Using Constrained Optimization
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
Action-Gap Phenomenon in Reinforcement Learning
Differentiable Cloth Simulation for Inverse Problems
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances
NaturalProver: Grounded Mathematical Proof Generation with Language Models
Image Synthesis with a Single (Robust) Classifier
Transportability from Multiple Environments with Limited Experiments: Completeness Results
SlowFocus: Enhancing Fine-grained Temporal Understanding in Video LLM
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
A novel family of non-parametric cumulative based divergences for point processes
Deep Generalized Schrödinger Bridge
Black-Box Forgetting
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
BERT Lost Patience Won't Be Robust to Adversarial Slowdown
The convergence rate of regularized learning in games: From bandits and uncertainty to optimism and beyond
Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels
Dimension-free Private Mean Estimation for Anisotropic Distributions
The Gain from Ordering in Online Learning
Star Temporal Classification: Sequence Modeling with Partially Labeled Data
Detecting hidden confounding in observational data using multiple environments
Synaptic Strength For Convolutional Neural Network
Representing Long-Range Context for Graph Neural Networks with Global Attention
Breaking the centralized barrier for cross-device federated learning
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration
Mixture of Experts Meets Prompt-Based Continual Learning
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence
Multiview Scene Graph
Spatio-Spectral Graph Neural Networks
Automated Evaluation of GNN Explanations with Neuro Symbolic Reasoning
Distributed Zero-Order Optimization under Adversarial Noise
Causal Effect Identification in a Sub-Population with Latent Variables
Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration
Sufficient Conditions for Agnostic Active Learnable
Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation
ConStat: Performance-Based Contamination Detection in Large Language Models
Gaussian Process Regression with Mismatched Models
Sampled Softmax with Random Fourier Features
Deep Model Transferability from Attribution Maps
Model Selection for Bayesian Autoencoders
Multi-Step Dyna Planning for Policy Evaluation and Control
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
Scene Segmentation with CRFs Learned from Partially Labeled Images
Maximum-Margin Matrix Factorization
Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering
Replicability in Learning: Geometric Partitions and KKM-Sperner Lemma
Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model
On-line Policy Improvement using Monte-Carlo Search
How to tell when a clustering is (approximately) correct using convex relaxations
V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs
Variance Reduced Policy Evaluation with Smooth Function Approximation
Algorithmic Stability and Generalization Performance
Exponentiated Gradient Algorithms for Large-margin Structured Classification
Complexity of Highly Parallel Non-Smooth Convex Optimization
High-Dimensional Gaussian Process Bandits
Efficient Symbolic Policy Learning with Differentiable Symbolic Expression
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
Smoothed Analysis of Online and Differentially Private Learning
Early-stopped neural networks are consistent
UCLID-Net: Single View Reconstruction in Object Space
CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization
Scanning Trojaned Models Using Out-of-Distribution Samples
List and Certificate Complexities in Replicable Learning
Statistically Valid Variable Importance Assessment through Conditional Permutations
Learning Instance-Independent Value Functions to Enhance Local Search
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Cardinality Restricted Boltzmann Machines
An Information Theory Perspective on Variance-Invariance-Covariance Regularization
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
Unsupervised Learning for Physical Interaction through Video Prediction
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality
Implicit Wiener Series for Higher-Order Image Analysis
Structured Learning for Cell Tracking
Beyond Optimism: Exploration With Partially Observable Rewards
A Solver-free Framework for Scalable Learning in Neural ILP Architectures
Skill-aware Mutual Information Optimisation for Zero-shot Generalisation in Reinforcement Learning
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
Towards Optimal Caching and Model Selection for Large Model Inference
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Robust covariance estimation with missing values and cell-wise contamination
"The Lovasz $\theta$ function, SVMs and finding large dense subgraphs"
Repeated Contextual Auctions with Strategic Buyers
The Emergence of Multiple Movement Units in the Presence of Noise and Feedback Delay
Probabilistic Multi-Task Feature Selection
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
To Err Like Human: Affective Bias-Inspired Measures for Visual Emotion Recognition Evaluation
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes
Learning Non-Rigid 3D Shape from 2D Motion
Improved Multimodal Deep Learning with Variation of Information
Competitive Distribution Estimation: Why is Good-Turing Good
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
Simulation-Free Training of Neural ODEs on Paired Data
On the Comparison between Multi-modal and Single-modal Contrastive Learning
On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning
Predict-then-Calibrate: A New Perspective of Robust Contextual LP