yusuke1997/dblp-id-title huggingface dataset:
Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model
Consistency of Neural Causal Partial Identification
Counterfactual Explanations Can Be Manipulated
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning
pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning
Evaluation of Text-to-Video Generation Models: A Dynamics Perspective
FNeVR: Neural Volume Rendering for Face Animation
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection
Observability of Neural Network Behavior
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP
Bayesian Source Localization with the Multivariate Laplace Prior
A generative model of the hippocampal formation trained with theta driven local learning rules
Shape Context: A New Descriptor for Shape Matching and Object Recognition
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Discriminative Clustering by Regularized Information Maximization
A Programmable Analog Neural Computer and Simulator
On global convergence of ResNets: From finite to infinite width using linear parameterization
EgoTaskQA: Understanding Human Tasks in Egocentric Videos
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization
Distributional Robustness with IPMs and links to Regularization and GANs
On the Sparsity of the Strong Lottery Ticket Hypothesis
Time-Constrained Robust MDPs
Piper: Multidimensional Planner for DNN Parallelization
Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline
TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion models
Non-Linear Domain Adaptation with Boosting
Aligner: Efficient Alignment by Learning to Correct
A Trellis-Structured Neural Network
emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography
Dynamic Filter Networks
Regularized Conditional Diffusion Model for Multi-Task Preference Alignment
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
Spatio-Angular Convolutions for Super-resolution in Diffusion MRI
SAMPa: Sharpness-aware Minimization Parallelized
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Learning to Find Pictures of People
Scheduling jobs with stochastic holding costs
Muscles in Time: Learning to Understand Human Motion In-Depth by Simulating Muscle Activations
Asynchronous Dynamics of Continuous Time Neural Networks
Generalized Maximum Margin Clustering and Unsupervised Kernel Learning
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity
Provably expressive temporal graph networks
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
InstanT: Semi-supervised Learning with Instance-dependent Thresholds
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
Uncovering the Structural Fairness in Graph Contrastive Learning
Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
Lifted Symmetry Detection and Breaking for MAP Inference
Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
Alternating Mirror Descent for Constrained Min-Max Games
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
Redeeming intrinsic rewards via constrained optimization
Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient
Texture Synthesis Using Convolutional Neural Networks
Experts in a Markov Decision Process
Learning Populations of Parameters
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)
Training Private Models That Know What They Don't Know
Efficient Design of Boltzmann Machines
CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
Protein Interface Prediction using Graph Convolutional Networks
Conditional Image Generation with PixelCNN Decoders
Interpreting prediction markets: a stochastic approach
Score-Optimal Diffusion Schedules
Learning Utilities and Equilibria in Non-Truthful Auctions
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Distributed Estimation, Information Loss and Exponential Families
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals
Modified Frank Wolfe in Probability Space
Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data
Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
Depth is More Powerful than Width with Prediction Concatenation in Deep Forest
Non-Convex Bilevel Optimization with Time-Varying Objective Functions
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
Multi-Label Open Set Recognition
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
GILBO: One Metric to Measure Them All
Unorganized Malicious Attacks Detection
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
A Network for Image Segmentation Using Color
Kernel Dependency Estimation
Permutation-based Causal Inference Algorithms with Interventions
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
Towards mental time travel: a hierarchical memory for reinforcement learning agents
A Dataset of Relighted 3D Interacting Hands
Zero-One Laws of Graph Neural Networks
Classification by Pairwise Coupling
Contrastive Learning for Image Captioning
D^2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Covariate Shift Corrected Conditional Randomization Test
ContinuAR: Continuous Autoregression For Infinite-Fidelity Fusion
Logarithmic Bayes Regret Bounds
Template based Graph Neural Network with Optimal Transport Distances
Do Current Multi-Task Optimization Methods in Deep Learning Even Help?
Learning to summarize with human feedback
Online combinatorial optimization with stochastic decision sets and adversarial losses
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Does Video-Text Pretraining Help Open-Vocabulary Online Action Detection?
Intrinsic Self-Supervision for Data Quality Audits
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Task-Robust Pre-Training for Worst-Case Downstream Adaptation
Statistical-Computational Trade-offs for Density Estimation
TinyTTA: Efficient Test-time Adaptation via Early-exit Ensembles on Edge Devices
EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models
How regularization affects the critical points in linear networks
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Computational Structure of coordinate transformations: A generalization study
Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits
The Emergence of Objectness: Learning Zero-shot Segmentation from Videos
Tree-structured Approximations by Expectation Propagation
Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation
A Model of Spatial Representations in Parietal Cortex Explains Hemineglect
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling
Nonsmooth Implicit Differentiation for Machine-Learning and Optimization
Is Distance Matrix Enough for Geometric Deep Learning?
On Convergence and Generalization of Dropout Training
Gradient Regularized V-Learning for Dynamic Treatment Regimes
A Formal Model of the Insect Olfactory Macroglomerulus: Simulations and Analytic Results
Fast Unbalanced Optimal Transport on a Tree
Reasons and Solutions for the Decline in Model Performance after Editing
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms
Introspective Learning : A Two-Stage approach for Inference in Neural Networks
Approximate inference using planar graph decomposition
Learning Informative Statistics: A Nonparametnic Approach
In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
Tractable Variational Structures for Approximating Graphical Models
Catastrophic Interference in Human Motor Learning
A Model for Resolution Enhancement (Hyperacuity) in Sensory Representation
Causal Influence Detection for Improving Efficiency in Reinforcement Learning
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition
Federated Spectral Clustering via Secure Similarity Reconstruction
Sparse Representation for Gaussian Process Models
Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation
Stock Selection via Nonlinear Multi-Factor Models
Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification
Interpreting the Weight Space of Customized Diffusion Models
Maximal Sparsity with Deep Networks?
Learned Region Sparsity and Diversity Also Predicts Visual Attention
Do Vision Transformers See Like Convolutional Neural Networks?
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
Inductive Quantum Embedding
A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure
Meta-Learning MCMC Proposals
Choose a Transformer: Fourier or Galerkin
Metalearned Neural Memory
Word Space
Your contrastive learning problem is secretly a distribution alignment problem
Minimax Embeddings
Efficient online algorithms for fast-rate regret bounds under sparsity
FLAME : Factuality-Aware Alignment for Large Language Models
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Multi-Information Source Optimization
GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats
Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos
Planning with an Adaptive World Model
A Single-timescale Analysis for Stochastic Approximation with Multiple Coupled Sequences
IDGen: Item Discrimination Induced Prompt Generation for LLM Evaluation
Adaptive Spline Networks
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
The Autoencoding Variational Autoencoder
Fault Diagnosis of Antenna Pointing Systems Using Hybrid Neural Network and Signal Processing Models
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity
Density-driven Regularization for Out-of-distribution Detection
Denoising and Dimension Reduction in Feature Space
Graph Scattering beyond Wavelet Shackles
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
How Modular should Neural Module Networks Be for Systematic Generalization?
RNADE: The real-valued neural autoregressive density-estimator
Improving Conditional Coverage via Orthogonal Quantile Regression
Rethinking LDA: Moment Matching for Discrete ICA
Statistically Efficient Estimations Using Cortical Lateral Connections
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
Gaussian Differential Privacy on Riemannian Manifolds
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments
Efficient Centroid-Linkage Clustering
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
Parallel Double Greedy Submodular Maximization
SEGA: Variance Reduction via Gradient Sketching
Exploring Low-Dimensional Subspace in Diffusion Models for Controllable Image Editing
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning
Combining Observational Data and Language for Species Range Estimation
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
Improved Distributed Principal Component Analysis
On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks
Fast Variational Inference for Large-scale Internet Diagnosis
GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning
OpenAGI: When LLM Meets Domain Experts
Translating Embeddings for Modeling Multi-relational Data
Adaptive Sampling for Discovery
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
Efficient Online Estimation of Causal Effects by Deciding What to Observe
An Image is Worth 32 Tokens for Reconstruction and Generation
MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics
Towards Sharper Generalization Bounds for Structured Prediction
Neural Computation with Winner-Take-All as the Only Nonlinear Operation
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions
All Politics is Local: Redistricting via Local Fairness
RTRMC: A Riemannian trust-region method for low-rank matrix completion
Autoformalization with Large Language Models
How Many Samples are Needed to Estimate a Convolutional Neural Network?
A Bounded Ability Estimation for Computerized Adaptive Testing
Destabilization and Route to Chaos in Neural Networks with Random Connectivity
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits
Gaussian Process Training with Input Noise
Predicting Molecular Conformation via Dynamic Graph Score Matching
MemoryFormer : Minimize Transformer Computation by Removing Fully-Connected Layers
Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills
Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential
Variational Gaussian Processes with Decoupled Conditionals
Small-World Phenomena and the Dynamics of Information
How the Poverty of the Stimulus Solves the Poverty of the Stimulus
Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms
Multi-agent Trajectory Prediction with Fuzzy Query Attention
Learning Reliable Logical Rules with SATNet
HRN: A Holistic Approach to One Class Learning
Small Nash Equilibrium Certificates in Very Large Games
CoIN: A Benchmark of Continual Instruction Tuning for Multimodel Large Language Models
Optimal Filtering in the Salamander Retina
Leveraging Distribution Alignment via Stein Path for Cross-Domain Cold-Start Recommendation
Privacy Amplification by Mixing and Diffusion Mechanisms
Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images
CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes
Automated Speech Analysis for Psychosis Evaluation
The Perceptron Algorithm Is Fast for Non-Malicious Distributions
Neural Networks Learning and Memorization with (almost) no Over-Parameterization
Differentiable Sparse Coding
Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class
Super-Bit Locality-Sensitive Hashing
On the Convergence of Black-Box Variational Inference
Learning to Perform Local Rewriting for Combinatorial Optimization
Simultaneous Preference and Metric Learning from Paired Comparisons
Hidden Markov Models for Human Genes
When Does Optimizing a Proper Loss Yield Calibration?
Zap Q-Learning With Nonlinear Function Approximation
Sparse and Locally Constant Gaussian Graphical Models
The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022
Batched Multi-armed Bandits Problem
Select and Sample - A Model of Efficient Neural Inference and Learning
Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman Network
Regime Switching Bandits
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages
Learning Spatially-Aware Language and Audio Embeddings
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation
Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning
LTD Facilitates Learning in a Noisy Environment
Adaptive Range Coding
Sharing Key Semantics in Transformer Makes Efficient Image Restoration
Unified Generative and Discriminative Training for Multi-modal Large Language Models
Generalizing Graph Matching beyond Quadratic Assignment Model
Differentially Private Robust Low-Rank Approximation
Optimal ablation for interpretability
GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers
Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
Non-Invasive Silent Speech Recognition in Multiple Sclerosis with Dysphonia
A Large Deviation Bound for the Area Under the ROC Curve
Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
Policy Optimization for Robust Average Reward MDPs
MEMO: Test Time Robustness via Adaptation and Augmentation
Learning to Classify Galaxy Shapes Using the EM Algorithm
On the Modularity of Hypernetworks
Visalogy: Answering Visual Analogy Questions
Using multiple samples to learn mixture models
The Geometry of Eye Rotations and Listing's Law
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network
Adversarial Attacks on Online Learning to Rank with Click Feedback
Interpretable Distribution Features with Maximum Testing Power
On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks
Modern Neural Networks Generalize on Small Data Sets
Ultrahyperbolic Neural Networks
Imitating Past Successes can be Very Suboptimal
Variational Laws of Visual Attention for Dynamic Scenes
Regularization Path of Cross-Validation Error Lower Bounds
Conformalized Quantile Regression
Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks
Tracking Most Significant Shifts in Nonparametric Contextual Bandits
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
PatchGame: Learning to Signal Mid-level Patches in Referential Games
Learning Differentiable Programs with Admissible Neural Heuristics
Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
LoCo: Local Contrastive Representation Learning
Learning to Group Auxiliary Datasets for Molecule
Recurrent neural networks: vanishing and exploding gradients are not the end of the story
Active Learning for Anomaly and Rare-Category Detection
Validation Free and Replication Robust Volume-based Data Valuation
Toward Understanding Privileged Features Distillation in Learning-to-Rank
Fast learning rates with heavy-tailed losses
Sparse Probabilistic Circuits via Pruning and Growing
Preference-based Pure Exploration
Approximation Based Variance Reduction for Reparameterization Gradients
Neural Algorithmic Reasoners are Implicit Planners
A Unified Conditional Framework for Diffusion-based Image Restoration
Asymptotics for Sketching in Least Squares Regression
Multiresolution analysis on the symmetric group
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
Correspondence learning via linearly-invariant embedding
Global Versus Local Methods in Nonlinear Dimensionality Reduction
Solving Minimum-Cost Reach Avoid using Reinforcement Learning
Smoothing Structured Decomposable Circuits
Space-Time Local Embeddings
An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression
Initializing Services in Interactive ML Systems for Diverse Users
Adversarially Robust Streaming Algorithms via Differential Privacy
AFEC: Active Forgetting of Negative Transfer in Continual Learning
TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
CoFie: Learning Compact Neural Surface Representations with Coordinate Fields
Deep Learning with Topological Signatures
Sample Size Requirements for Feedforward Neural Networks
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
Exponential Lower Bounds for Fictitious Play in Potential Games
Locality-Aware Generalizable Implicit Neural Representation
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
Learning Probability Measures with respect to Optimal Transport Metrics
AvE: Assistance via Empowerment
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping
Normalization and effective learning rates in reinforcement learning
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
Decision-Focused Learning with Directional Gradients
Convergence and No-Regret in Multiagent Learning
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Algorithms for Independent Components Analysis and Higher Order Statistics
Estimating decision tree learnability with polylogarithmic sample complexity
Real-time adaptive information-theoretic optimization of neurophysiology experiments
Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
Optimal Web-Scale Tiering as a Flow Problem
SeqPATE: Differentially Private Text Generation via Knowledge Distillation
Model Shapley: Equitable Model Valuation with Black-box Access
Team-PSRO for Learning Approximate TMECor in Large Team Games via Cooperative Reinforcement Learning
Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance
Discovery of Options via Meta-Learned Subgoals
CQM: Curriculum Reinforcement Learning with a Quantized World Model
Mitigating Test-Time Bias for Fair Image Retrieval
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions
Low Precision Local Training is Enough for Federated Learning
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research