yusuke1997/dblp-id-title huggingface dataset:
Interactively Learning Preference Constraints in Linear Bandits
Reviving and Improving Recurrent Back-Propagation
Barrier Algorithms for Constrained Non-Convex Optimization
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Protecting Language Generation Models via Invisible Watermarking
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
Improving Video-based Heart Rate and Respiratory Rate Estimation via Pulse-Respiration Quotient
Incremental Bayesian networks for structure prediction
Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework
Hybrid huberized support vector machines for microarray classification
GMAC: A Distributional Perspective on Actor-Critic Framework
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
Feature Selection via Concave Minimization and Support Vector Machines
Efficient end-to-end learning for quantizable representations
Warped Convolutions: Efficient Invariance to Spatial Transformations
Towards a Better Understanding of Memory-based Reasoning Systems
Dynamic hierarchical Markov random fields and their application to web data extraction
Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback
Tokenized Bandit for LLM Decoding and Alignment
High Dimensional Bayesian Optimisation and Bandits via Additive Models
In-Context Learning and Occam's Razor
A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach
Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models
Angle Domain Guidance: Latent Diffusion Requires Rotation Rather Than Extrapolation
Overcoming the Optimizer's Curse: Obtaining Realistic Prescriptions from Neural Networks
ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation
Differentially Private Sum-Product Networks
MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation
Telling Peer Direct Effects from Indirect Effects in Observational Network Data
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement
DCTdiff: Intriguing Properties of Image Generative Modeling in the DCT Space
Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits
A Scalable Frank-Wolfe-Based Algorithm for the Max-Cut SDP
Meta-Learning Bidirectional Update Rules
Multi-Task Reinforcement Learning with Context-based Representations
Differentiable Multi-Target Causal Bayesian Experimental Design
Differentially Private Community Detection for Stochastic Block Models
Weightless: Lossy weight encoding for deep neural network compression
Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels-based Dataset Distillation
Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems
Mixture of Experts Made Intrinsically Interpretable
Learning Associative Memories with Gradient Descent
Provable Bounds for Learning Some Deep Representations
Inference-Time Alignment of Diffusion Models with Direct Noise Optimization
Libra: Building Decoupled Vision System on Large Language Models
Tensor Gaussian Process with Contraction for Multi-Channel Imaging Analysis
Guidance with Spherical Gaussian Constraint for Conditional Diffusion
Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data
Bayesian Estimation of Differential Privacy
Improving Learning Using Causality and Abduction
Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
Feature Shift Localization Network
Catformer: Designing Stable Transformers via Sensitivity Analysis
Safe Policy Improvement with Baseline Bootstrapping
Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling
LLM-Augmented Chemical Synthesis and Design Decision Programs
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
Community Detection Using Time-Dependent Personalized PageRank
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition
A Natural Actor-Critic Framework for Zero-Sum Markov Games
ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport Plans
Sharp Minima Can Generalize For Deep Nets
On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
Smooth Interpolation for Improved Discrete Graph Generative Models
Exploring the Mind: Integrating Questionnaires and fMRI
What is the Effect of Importance Weighting in Deep Learning?
The Elastic Embedding Algorithm for Dimensionality Reduction
Learning Determinantal Point Processes with Moments and Cycles
Nesting Particle Filters for Experimental Design in Dynamical Systems
Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data
Cross-model Back-translated Distillation for Unsupervised Machine Translation
Unsupervised Co-part Segmentation through Assembly
An Incremental Genetic Algorithm for Real-Time Learning
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
Learning to Ride a Bicycle using Iterated Phantom Induction
Analyzing and Mitigating Interference in Neural Architecture Search
Unsupervised Learning by Predicting Noise
AIMS: An Adaptive Interactive Modeling System for Supporting Engineering Decision Making
Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners
LARNet: Lie Algebra Residual Network for Face Recognition
Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Monotonic Location Attention for Length Generalization
Online Learning to Rank with Features
Dealing with non-stationary environments using context detection
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Prediction with expert advice for the Brier game
Magicoder: Empowering Code Generation with OSS-Instruct
Mind the duality gap: safer rules for the Lasso
Delayed Bandits: When Do Intermediate Observations Help?
Dimension-Free Adaptive Subgradient Methods with Frequent Directions
Improving Ultrametrics Embeddings Through Coresets
GraphFM: Improving Large-Scale GNN Training via Feature Momentum
Fast and Efficient Reinforcement Learning with Truncated Temporal Differences
Characterizing and Understanding Temporal Effects in COVID-19 Data
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
A Closer Look at Self-Supervised Lightweight Vision Transformers
Interval Bound Interpolation for Few-shot Learning with Few Tasks
Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing
MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning
Deep Unsupervised Hashing via External Guidance
PARQ: Piecewise-Affine Regularized Quantization
The Batch Complexity of Bandit Pure Exploration
Approximate Kalman Filters for Embedding Author-Word Co-occurrence Data over Time
On Non-local Convergence Analysis of Deep Linear Networks
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains
Robust Training of Neural Networks Using Scale Invariant Architectures
µnit Scaling: Simple and Scalable FP8 LLM Training
Neural Program Synthesis from Diverse Demonstration Videos
Sound Abstraction and Decomposition of Probabilistic Programs
Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws
LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs - No Silver Bullet for LC or RAG Routing
Improved Communication Cost in Distributed PageRank Computation - A Theoretical Study
Robust Testing and Estimation under Manipulation Attacks
Bayesian Exploration Networks
Generalization to New Actions in Reinforcement Learning
Variational Bayesian image modelling
MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization
Visualizing and Understanding Atari Agents
Solution stability in linear programming relaxations: graph partitioning and unsupervised learning
Flat Metric Minimization with Applications in Generative Modeling
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
The Hierarchical Adaptive Forgetting Variational Filter
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization
A Geometric Approach to Archetypal Analysis via Sparse Projections
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
SITCOM: Step-wise Triple-Consistent Diffusion Sampling For Inverse Problems
FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
S3O: A Dual-Phase Approach for Reconstructing Dynamic Shape and Skeleton of Articulated Objects from Single Monocular Video
Position: Understanding LLMs Requires More Than Statistical Generalization
Non-Disjoint Discretization for Naive-Bayes Classifiers
Conditional Image Synthesis with Auxiliary Classifier GANs
Generative Causal Explanations for Graph Neural Networks
Extreme Value Policy Optimization for Safe Reinforcement Learning
Faster Stochastic Optimization with Arbitrary Delays via Adaptive Asynchronous Mini-Batching
On the importance of initialization and momentum in deep learning
Consistent Submodular Maximization
Automatically Auditing Large Language Models via Discrete Optimization
A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks
A General Framework For Detecting Anomalous Inputs to DNN Classifiers
Residual Matrix Transformers: Scaling the Size of the Residual Stream
Incremental Version-Space Merging
Towards a Formal Analysis of EBL
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
Proxsparse: Regularized Learning of Semi-Structured Sparsity masks for Pretrained LLMS
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models
Active Feature Acquisition with Generative Surrogate Models
Reflective Policy Optimization
RobustLight: Improving Robustness via Diffusion Reinforcement Learning for Traffic Signal Control
Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer
BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms
Tutorial summary: Convergence of natural dynamics to equilibria
Classification with Multiple Latent Variable Models using Maximum Entropy Discrimination
Clustering High Dimensional Categorical Data via Topographical Features
Machine Learning in the Combination of Expert Opinion Approach to IR
Clustering to Maximize the Ratio of Split to Diameter
Instance-Optimal Pure Exploration for Linear Bandits on Continuous Arms
Active Perception and Reinforcement Learning
A graphical model for predicting protein molecular function
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
Reinforcement Learning in Dynamic Environments using Instantiated Information
Image Color Constancy Using EM and Cached Statistics
Understanding Gradient Descent on the Edge of Stability in Deep Learning
Why Most Decisions Are Easy in Tetris - And Perhaps in Other Sequential Decision Problems, As Well
Compressing Large Language Models by Joint Sparsification and Quantization
Exponential Spectral Pursuit: An Effective Initialization Method for Sparse Phase Retrieval
MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
Markov Mixed Membership Models
Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss
Compressing Gradient Optimizers via Count-Sketches
Active learning using pre-clustering
Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties
Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, Bellevue, Washington, USA, July 2, 2011
Meta Evidential Transformer for Few-Shot Open-Set Recognition
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons
Selective Preference Aggregation
GREAD: Graph Neural Reaction-Diffusion Networks
A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions
Deep Variational Reinforcement Learning for POMDPs
InfAlign: Inference-aware language model alignment
Best Arm Identification in Graphical Bilinear Bandits
Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
Selective Network Linearization for Efficient Private Inference
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer
Object Segmentation Without Labels with Large-Scale Generative Models
Adversarial Feature Matching for Text Generation
Symmetric Machine Theory of Mind
Gated Feedback Recurrent Neural Networks
Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems
Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments
Correlated Q-Learning
Linear Time Solver for Primal SVM
A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more
Defects of Convolutional Decoder Networks in Frequency Representation
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
On the Statistical Mechanisms of Distributional Compositional Generalization
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting
The Most Generative Maximum Margin Bayesian Networks
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models
Recovery of Sparse Signals from a Mixture of Linear Samples
LEAPS: A discrete neural sampler via locally equivariant networks
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo
Adaptive Estimator Selection for Off-Policy Evaluation
Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
Principal Component Flows
Model Fusion with Kullback-Leibler Divergence
Kernel Methods for Radial Transformed Compositional Data with Many Zeros
Not all distributional shifts are equal: Fine-grained robust conformal inference
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation
Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch
PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
BOCK : Bayesian Optimization with Cylindrical Kernels
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Graph4MM: Weaving Multimodal Learning with Structural Information
Multi-Level Branched Regularization for Federated Learning
Lipschitz Continuity in Model-based Reinforcement Learning
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
Risk-Sensitive Policy Optimization via Predictive CVaR Policy Gradient
Clustering High Dimensional Dynamic Data Streams
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition
Variational nearest neighbor Gaussian process
PLay: Parametrically Conditioned Layout Generation using Latent Diffusion
Robust Outlier Arm Identification
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees
Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers
On the Consistency of Ranking Algorithms
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning
Nested Expectations with Kernel Quadrature
Joint Localization and Activation Editing for Low-Resource Fine-Tuning
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
Tutorial summary: Large social and information networks: opportunities for ML
Lipschitz Generative Adversarial Nets
Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry
Revolve: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization
Graph kernels between point clouds
Spatial-Channel Token Distillation for Vision MLPs
Improved OOD Generalization via Adversarial Training and Pretraing
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning
PDE-Based Optimal Strategy for Unconstrained Online Learning
Relative Fisher Information and Natural Gradient for Learning Large Modular Models
A Comparison of Learning Techniques in Second Language Learning
Near-Optimal Learning of Extensive-Form Games with Imperfect Information
Solving Hierarchical Information-Sharing Dec-POMDPs: An Extensive-Form Game Approach
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes
On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs
Density-Adaptive Learning and Forgetting
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
Teaching a black-box learner
Improving SVM accuracy by training on auxiliary data sources
Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
Stability Evaluation through Distributional Perturbation Analysis
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation
Sequential Kernelized Independence Testing
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
Exploiting sparse Markov and covariance structure in multiresolution models
Robust Multi-bit Text Watermark with LLM-based Paraphrasers
Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
Scaling Vision Transformers to 22 Billion Parameters
Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity
DINO: Distributed Newton-Type Optimization Method
Unsupervised Learning of Visual 3D Keypoints for Control
AdaWorld: Learning Adaptable World Models with Latent Actions
Policy Caches with Successor Features
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data
PixelSNAIL: An Improved Autoregressive Generative Model
Class-Imbalanced Graph Learning without Class Rebalancing
Optimization Proxies using Limited Labeled Data and Training Time - A Semi-Supervised Bayesian Neural Network Approach
Fair Selective Classification Via Sufficiency
VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis
FlatQuant: Flatness Matters for LLM Quantization
Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective
Learning first-order rules from data with multiple parts: applications on mining chemical compound data
MultiLabel Classification on Tree- and DAG-Structured Hierarchies
Unit Scaling: Out-of-the-Box Low-Precision Training
Fisher Information Embedding for Node and Graph Learning
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
Hidden Symmetries of ReLU Networks
Predicting Legislative Roll Calls from Text
Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction
Generative Data Mining with Longtail-Guided Diffusion
Linear Adversarial Concept Erasure
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
Parallel Bayesian Network Structure Learning
A Graph Based Classification Method for Multiple Sclerosis Clinical Forms Using Support Vector Machine
Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time
Tackling View-Dependent Semantics in 3D Language Gaussian Splatting
On a Combinatorial Problem Arising in Machine Teaching
Accounting for burstiness in topic models
Interpreting and Disentangling Feature Components of Various Complexity from DNNs
Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Understanding Forgetting in Continual Learning with Linear Regression
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation
Simple regret for infinitely many armed bandits
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill
ZipAR: Parallel Autoregressive Image Generation through Spatial Locality
An Analysis of Representation Shift in Concept Learning
Loss Decomposition for Fast Learning in Large Output Spaces
A2Q+: Improving Accumulator-Aware Weight Quantization
Feature-Budgeted Random Forest
UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees
Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation
Learning Infinite Layer Networks Without the Kernel Trick
Function Contrastive Learning of Transferable Meta-Representations
Feature Engineering for Text Classification
Efficient Selection of Multiple Bandit Arms: Theory and Practice
Mind the Gap: a Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers
Differentiable Mapper for Topological Optimization of Data Representation
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection
A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
Finite Time Logarithmic Regret Bounds for Self-Tuning Regulation
Optimizing the Latent Space of Generative Networks
Neural Networks and Rational Functions
Theoretical guarantees on the best-of-n alignment policy
On Medians of (Randomized) Pairwise Means
Contextual Feature Selection with Conditional Stochastic Gates
Learning Transferable Features with Deep Adaptation Networks
Explicit Preference Optimization: No Need for an Implicit Reward Model
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix