yusuke1997/dblp-id-title huggingface dataset:
Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks
Map-Reduce for Machine Learning on Multicore
On Divergence Measures for Training GFlowNets
Fragment Completion in Humans and Machines
Bayesian Adversarial Learning
Interpreting the neural code with Formal Concept Analysis
Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming
Bayesian Modeling of Facial Similarity
Catastrophic Interference in Connectionist Networks: Can It Be Predicted, Can It Be Prevented?
Learning visual motion in recurrent neural networks
Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms
Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
A Closer Look at the CLS Token for Cross-Domain Few-Shot Learning
Randomized and Deterministic Maximin-share Approximations for Fractionally Subadditive Valuations
SfPUEL: Shape from Polarization under Unknown Environment Light
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
RoleAgent: Building, Interacting, and Benchmarking High-quality Role-Playing Agents from Scripts
A Benchmark for Compositional Visual Reasoning
A Comprehensive Analysis of Semantic Compositionality in Text-to-Image Generation
Multiscale Deep Equilibrium Models
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning
Characteristic Circuits
Replicable Reinforcement Learning
What shapes feature representations? Exploring datasets, architectures, and training
"Burn-in, bias, and the rationality of anchoring"
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
Deep Neural Networks with Box Convolutions
Learning in two-player zero-sum partially observable Markov games with perfect recall
Spectral Signatures in Backdoor Attacks
Exploring the Limits of Out-of-Distribution Detection
Exploiting Chaos to Control the Future
A Recipe for Charge Density Prediction
Learning from Demonstration
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
Online F-Measure Optimization
Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows
Robust compressed sensing using generative models
Block Transformer: Global-to-Local Language Modeling for Fast Inference
Prediction of Protein Topologies Using Generalized IOHMMS and RNNs
Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging
On the Cryptographic Hardness of Learning Single Periodic Neurons
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning
Variational Continual Bayesian Meta-Learning
Bach in a Box - Real-Time Harmony
Minimax Regret for Cascading Bandits
Learning to Predict Visibility and Invisibility from Occlusion Events
Deep Explicit Duration Switching Models for Time Series
Dual Path Networks
MetaInit: Initializing learning by learning to initialize
Bifurcation Analysis of a Silicon Neuron
LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks
Visual Adversarial Imitation Learning using Variational Models
Understanding Instance-based Interpretability of Variational Auto-Encoders
The Neural Testbed: Evaluating Joint Predictions
The Generalized Lasso with Nonlinear Observations and Generative Priors
IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration
Coop: Memory is not a Commodity
Uncovering, Explaining, and Mitigating the Superficial Safety of Backdoor Defense
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
Moral Responsibility for AI Systems
Sparse Symplectically Integrated Neural Networks
Comparing the Performance of Connectionist and Statistical Classifiers on an Image Segmentation Problem
A Model for Temporal Dependencies in Event Streams
PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains
On Large-Cohort Training for Federated Learning
Blind Video Temporal Consistency via Deep Video Prior
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Disease Trajectory Maps
Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System
Non-parametric Models for Non-negative Functions
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Structured Matrix Recovery via the Generalized Dantzig Selector
Training neural operators to preserve invariant measures of chaotic attractors
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
Context-Dependent Multiple Distribution Phonetic Modeling with MLPs
Learning predictable and robust neural representations by straightening image sequences
Rethinking Score Distillation as a Bridge Between Image Distributions
Trap-MID: Trapdoor-based Defense against Model Inversion Attacks
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method
Learning Motion Refinement for Unsupervised Face Animation
Association Graph Learning for Multi-Task Classification with Category Shifts
Distributionally Robust Optimization with Data Geometry
SynMob: Creating High-Fidelity Synthetic GPS Trajectory Dataset for Urban Mobility Analysis
Dynamics of Attention as Near Saddle-Node Bifurcation Behavior
EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models
Finding good policies in average-reward Markov Decision Processes without prior knowledge
Uncertainty on Asynchronous Time Event Prediction
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering
Trivializations for Gradient-Based Optimization on Manifolds
Optimal Teaching for Limited-Capacity Human Learners
A Structural Smoothing Framework For Robust Graph Comparison
Towards Improving Faithfulness in Abstractive Summarization
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
REM: From Structural Entropy to Community Structure Deception
A Large Scale Search Dataset for Unbiased Learning to Rank
An Exact Algorithm for F-Measure Maximization
Sparse Metric Learning via Smooth Optimization
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
Transfer from Multiple MDPs
Incorporating Second-Order Functional Knowledge for Better Option Pricing
Bandits with Ranking Feedback
Mortal Multi-Armed Bandits
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Noise Attention Learning: Enhancing Noise Robustness by Gradient Scaling
Combining Graph Laplacians for Semi-Supervised Learning
3D Object Recognition: A Model of View-Tuned Neurons
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Verification Based Solution for Structured MAB Problems
Beware of Road Markings: A New Adversarial Patch Attack to Monocular Depth Estimation
Practical Data-Dependent Metric Compression with Provable Guarantees
Differentially Private Reinforcement Learning with Self-Play
Non-delusional Q-learning and value-iteration
Learning Sparse Distributions using Iterative Hard Thresholding
RobIR: Robust Inverse Rendering for High-Illumination Scenes
Robust Contrastive Multi-view Clustering against Dual Noisy Correspondence
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Metric Flow Matching for Smooth Interpolations on the Data Manifold
House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement
Class-size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification
Appropriate Evaluation of Diagnostic Utility of Machine Learning Algorithm Generated Images
Backprop with Approximate Activations for Memory-efficient Network Training
Meta-in-context learning in large language models
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
An Information-theoretic Learning Algorithm for Neural Network Classification
Perturbative Black Box Variational Inference
Towards Test-Time Refusals via Concept Negation
A Model for Associative Multiplication
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme
Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning
Active learning for misspecified generalized linear models
Regression with Input-Dependent Noise: A Bayesian Treatment
Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker
A Smooth Binary Mechanism for Efficient Private Continual Observation
Flexible Transfer Learning under Support and Model Shift
Efficient Projection-free Algorithms for Saddle Point Problems
QueST: Self-Supervised Skill Abstractions for Learning Continuous Control
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE
Modern Analytic Techniques to Solve the Dynamics of Recuurent Neural Networks
Thompson Sampling with Information Relaxation Penalties
Simulation of the Neocognitron on a CCD Parallel Processing Architecture
Iterative Methods via Locally Evolving Set Process
Non-linear CCA and PCA by Alignment of Local Models
From Boltzmann Machines to Neural Networks and Back Again
Generalization Gap in Amortized Inference
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents
Diffusion Improves Graph Learning
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers
Non-Gaussian Gaussian Processes for Few-Shot Regression
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks
Evaluate then Cooperate: Shapley-based View Cooperation Enhancement for Multi-view Clustering
Differentially Private Model Compression
HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links
Asymptotically Optimal Regularization in Smooth Parametric Models
Simplicity Bias in 1-Hidden Layer Neural Networks
Anatomical origin and computational role of diversity in the response properties of cortical neurons
Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with \beta-Divergences
Example-Based Image Synthesis of Articulated Figures
Building Timeseries Dataset: Empowering Large-Scale Building Analytics
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
Learning Overcomplete HMMs
WRENCH: A Comprehensive Benchmark for Weak Supervision
A Minimal Intervention Principle for Coordinated Movement
Translation Synchronization via Truncated Least Squares
Sparse PCA from Sparse Linear Regression
The Description Length of Deep Learning models
Towards Principled Graph Transformers
Connecting Optimization and Regularization Paths
Continuous Product Graph Neural Networks
The Lingering of Gradients: How to Reuse Gradients Over Time
Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian
Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy
Shape As Points: A Differentiable Poisson Solver
Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins
Unbiased learning of deep generative models with structured discrete representations
Crowdclustering
Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs
Smooth Interactive Submodular Set Cover
Can adversarial training learn image captioning ?
CUR from a Sparse Optimization Viewpoint
Chained Boosting
Generalizable Imitation Learning from Observation via Inferring Goal Proximity
Universal In-Context Approximation By Prompting Fully Recurrent Models
The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity
Scaling Gaussian Process Regression with Derivatives
Optimization of AMS using Weighted AUC optimized models
Selective Sampling-based Scalable Sparse Subspace Clustering
ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models
Large-scale Optimization of Partial AUC in a Range of False Positive Rates
Is Integer Arithmetic Enough for Deep Learning Training?
Benefits of over-parameterization with EM
Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings
Sequence and Tree Kernels with Statistical Feature Mining
Approximating Real-Time Recurrent Learning with Random Kronecker Factors
Inferring Motor Programs from Images of Handwritten Digits
Bayesian Nonparametric Spectral Estimation
Gaussian Processes for Multiuser Detection in CDMA receivers
Exact learning dynamics of deep linear networks with prior knowledge
Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
Parameter elimination in particle Gibbs sampling
Robust Bayesian Satisficing
Tractable Optimality in Episodic Latent MABs
CountGD: Multi-Modal Open-World Counting
A Comprehensively Tight Analysis of Gradient Descent for PCA
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models
Kernel Mean Estimation via Spectral Filtering
Transformer in Transformer
Search Improves Label for Active Learning
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities
No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices
Denoising Diffusion Restoration Models
A Reparametrization-Invariant Sharpness Measure Based on Information Geometry
Robust Regression with Twinned Gaussian Processes
Topology-Preserving Deep Image Segmentation
A Growing Neural Gas Network Learns Topologies
Estimating the class prior and posterior from noisy positives and unlabeled data
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
Swift Sampler: Efficient Learning of Sampler by 10 Parameters
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound
Theoretically Provable Spiking Neural Networks
Fast Lifted MAP Inference via Partitioning
Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
Weakly supervised causal representation learning
Probablistic Emulation of a Global Climate Model with Spherical DYffusion
Temporal Difference Updating without a Learning Rate
When Can We Track Significant Preference Shifts in Dueling Bandits?
Improving Adaptivity via Over-Parameterization in Sequence Models
Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
Robust Tensor Decomposition with Gross Corruption
Bayesian Adaptation for Covariate Shift
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
Data-Dependent Structural Risk Minimization for Perceptron Decision Trees
Online L1-Dictionary Learning with Application to Novel Document Detection
Making the most of your day: online learning for optimal allocation of time
Constrained Graph Variational Autoencoders for Molecule Design
Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models
Nonparametric Model-Based Reinforcement Learning
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
Towards robust and generalizable representations of extracellular data using contrastive learning
Balanced Training for Sparse GANs
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
HumBugDB: A Large-scale Acoustic Mosquito Dataset
Fairness via Representation Neutralization
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation
Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits
Optimal rates for k-NN density and mode estimation
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction
Information Dynamics and Emergent Computation in Recurrent Circuits of Spiking Neurons
Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
Efficient Combination of Rematerialization and Offloading for Training DNNs
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation
Mind the Duality Gap: Logarithmic regret algorithms for online optimization
MaskFactory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
Predicting Scene Parsing and Motion Dynamics in the Future
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
Multidimensional Fractional Programming for Normalized Cuts
Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems
FEEL-SNN: Robust Spiking Neural Networks with Frequency Encoding and Evolutionary Leak Factor
Practical Methods for Graph Two-Sample Testing
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
Learning Agent Representations for Ice Hockey
Strategyproof Voting under Correlated Beliefs
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
Exponential Family Predictive Representations of State
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach
Self-Supervised Learning Through Efference Copies
Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
Performance of Synthetic Neural Network Classification of Noisy Radar Signals
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
Multi-Resolution Weak Supervision for Sequential Data
Co-Training and Expansion: Towards Bridging Theory and Practice
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
Linear decision rule as aspiration for simple decision heuristics
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems
Probing the Compositionality of Intuitive Functions
MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts
Experimental Performance of Deliberation-Aware Responder in Multi-Proposer Ultimatum Game
Neural Persistence Dynamics
Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery
Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning
Learning Representations by Maximizing Mutual Information Across Views
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game
A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization
On the Role of Optimization in Double Descent: A Least Squares Study
Compositionality of optimal control laws
Hierarchical Distributed Representations for Statistical Language Modeling
Facility Location Problem in Differential Privacy Model Revisited
CyCLIP: Cyclic Contrastive Language-Image Pretraining
FM-Delta: Lossless Compression for Storing Massive Fine-tuned Foundation Models
Opponent Modeling with In-context Search
Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes
Permuton-induced Chinese Restaurant Process
Manifold Structured Prediction
Information Processing to Create Eye Movements
MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders
Online Learning with Sublinear Best-Action Queries
A Stochastic approximation method for inference in probabilistic graphical models
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds
Gaussian Gated Linear Networks
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
SCOP: Scientific Control for Reliable Neural Network Pruning
Topology Constraints in Graphical Models
Adversarial Examples Are Not Real Features
Using Feedforward Neural Networks to Monitor Alertness from Changes in EEG Correlation and Coherence
Multi-agent Cooperation in Diverse Population Games
Task-Agnostic Machine-Learning-Assisted Inference
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks
Cortico-cerebellar networks as decoupling neural interfaces
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making
Neur2SP: Neural Two-Stage Stochastic Programming
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
Never Go Full Batch (in Stochastic Convex Optimization)
Information-theoretic Limits for Community Detection in Network Models
What You See is What You Get: Principled Deep Learning via Distributional Generalization