yusuke1997/dblp-id-title huggingface dataset:
Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding
A Unified View of Multi-Label Performance Measures
A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Representation Subspace Distance for Domain Adaptation Regression
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health
Revisiting probabilistic models for clustering with pair-wise constraints
Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates
Efficient Algorithms for Finding Multi-way Splits for Decision Trees
ADHMR: Aligning Diffusion-based Human Mesh Recovery via Direct Preference Optimization
Learning the Electronic Hamiltonian of Large Atomic Structures
Learning Unnormalized Statistical Models via Compositional Optimization
Smooth Min-Max Monotonic Networks
Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
A Novel Method to Solve Neural Knapsack Problems
Learning Invariants from Explanations
Lightweight-Mark: Rethinking Deep Learning-Based Watermarking
Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models
Mean Field Multi-Agent Reinforcement Learning
Trading Off Consistency and Efficiency in version-Space Induction
Harmonic Exponential Families on Manifolds
Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
Interpreting and Improving Diffusion Models from an Optimization Perspective
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Structural alignment based kernels for protein structure classification
LoRA-Gen: Specializing Large Language Model via Online LoRA Generation
Weisfeiler-Lehman Meets Gromov-Wasserstein
Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints
Robust Multimodal Large Language Models Against Modality Conflict
Ridge Regression Confidence Machine
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Sequence to Sequence Training of CTC-RNNs with Partial Windowing
Hierarchical sampling for active learning
Progressive Identification of True Labels for Partial-Label Learning
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
RelGNN: Composite Message Passing for Relational Deep Learning
Image Clustering with External Guidance
A Swiss Army Knife for Minimax Optimal Transport
Learning Connections in Financial Time Series
Multilevel Clustering via Wasserstein Means
Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus
Reducing overfitting in process model induction
Can AI Assistants Know What They Don't Know?
Large scale manifold transduction
Sub-sampled Cubic Regularization for Non-convex Optimization
Safe Screening of Non-Support Vectors in Pathwise SVM Computation
Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation
On the Generalization Gap in Reparameterizable Reinforcement Learning
Learning Algebraic Multigrid Using Graph Neural Networks
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?
Nonlinear Causal Discovery with Latent Confounders
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Working Memory Graphs
Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
ByMI: Byzantine Machine Identification with False Discovery Rate Control
Parameter Estimation for Generalized Thurstone Choice Models
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation
Optimizing persistent homology based functions
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
How to address monotonicity for model risk management?
Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards
Robustifying Sequential Neural Processes
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
Convolutional Poisson Gamma Belief Network
Active Treatment Effect Estimation via Limited Samples
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies
Robust and Stable Black Box Explanations
Variational Network Inference: Strong and Stable with Concrete Support
DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy
AND: Audio Network Dissection for Interpreting Deep Acoustic Models
Regret Minimization and Convergence to Equilibria in General-sum Markov Games
Agent-as-a-Judge: Evaluate Agents with Agents
Generalized Exponential Concentration Inequality for Renyi Divergence Estimation
A Spline Theory of Deep Networks
KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search
E(n) Equivariant Message Passing Simplicial Networks
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
The Fast Johnson-Lindenstrauss Transform Is Even Faster
REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates
Doubly Optimal No-Regret Learning in Monotone Games
Approximation Guarantees for Adaptive Sampling
How Private are DP-SGD Implementations?
Label Inference Attacks from Log-loss Scores
Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery
PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
A Unified View of FANOVA: A Comprehensive Bayesian Framework for Component Selection and Estimation
Linkless Link Prediction via Relational Distillation
Unbiased Objective Estimation in Predictive Optimization
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models
A Study on Transformer Configuration and Training Objective
BAT: Learning to Reason about Spatial Sounds with Large Language Models
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity
Approximation Theory of Convolutional Architectures for Time Series Modelling
On the convergence of no-regret learning in selfish routing
Manifold Mixup: Better Representations by Interpolating Hidden States
Sequential Projection Learning for Hashing with Compact Codes
A Knowledge-intensive Approach to Learning Relational Concepts
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
Inference Suboptimality in Variational Autoencoders
Linear Causal Disentanglement via Interventions
Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers
Feature Expansion for Graph Neural Networks
Flat-LoRA: Low-Rank Adaptation over a Flat Loss Landscape
Accelerating Gossip SGD with Periodic Global Averaging
Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion
Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate
Secant Line Search for Frank-Wolfe Algorithms
Unitary Evolution Recurrent Neural Networks
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Hybrid Batch Bayesian Optimization
No-Regret Reinforcement Learning in Smooth MDPs
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
Generalization and Representational Limits of Graph Neural Networks
Leveraging Diffusion Model as Pseudo-Anomalous Graph Generator for Graph-Level Anomaly Detection
Does Data Scaling Lead to Visual Compositional Generalization?
SILVER: Single-loop variance reduction and application to federated learning
A Dynamical Systems Perspective on Nesterov Acceleration
AdaSplash: Adaptive Sparse Flash Attention
Rethinking Adversarial Robustness in the Context of the Right to be Forgotten
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Inference for Network Regression Models with Community Structure
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization
Never mind the metrics - what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective
Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters
Bayesian Deconditional Kernel Mean Embeddings
Boosted Backpropagation Learning for Training Deep Modular Networks
Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences
Ridge Regression Learning Algorithm in Dual Variables
Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach
How Effective Can Dropout Be in Multiple Instance Learning ?
A Neural Tangent Kernel Perspective of GANs
Great Models Think Alike and this Undermines AI Oversight
Demystifying Long Chain-of-Thought Reasoning
Small-loss Adaptive Regret for Online Convex Optimization
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings
Global optimization of Lipschitz functions
Adaptive Antithetic Sampling for Variance Reduction
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Rounding Methods for Discrete Linear Classification
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling
Efficient Long Context Fine-tuning with Chunk Flow
On the Spectral Bias of Neural Networks
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning
Learning to Optimally Schedule Internet Banner Advertisements
Learning dictionaries of stable autoregressive models for audio scene analysis
iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
A quasi-Newton approach to non-smooth convex optimization
Online mirror descent and dual averaging: keeping pace in the dynamic case
MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition
Improving Transformers with Probabilistic Attention Keys
Hindsight Learning for MDPs with Exogenous Inputs
Online Adaptive Principal Component Analysis and Its extensions
Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication
Automated Red Teaming with GOAT: the Generative Offensive Agent Tester
Enhancing Robustness of Neural Networks through Fourier Stabilization
Traditional and Heavy Tailed Self Regularization in Neural Network Models
Robustness in Multimodal Learning under Train-Test Modality Mismatch
FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization
CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes
GeomCA: Geometric Evaluation of Data Representations
Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices
Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
Learning to Simulate and Design for Structural Engineering
Otter: Generating Tests from Issues to Validate SWE Patches
PcLast: Discovering Plannable Continuous Latent States
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes
Delayed Reinforcement Learning by Imitation
Learning from True-False Labels via Multi-modal Prompt Retrieving
Active fairness auditing
Online Structured Prediction via Coactive Learning
Dynamic Evaluation of Large Language Models by Meta Probing Agents
A Kernel Perspective for Regularizing Deep Neural Networks
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits
Active learning for Hidden Markov Models: objective functions and algorithms
Separating Knowledge and Perception with Procedural Data
The Empirical Mean is Minimax Optimal for Local Glivenko-Cantelli
Stochastic Hamiltonian Gradient Methods for Smooth Games
A Simple and Strongly-Local Flow-Based Method for Cut Improvement
Forget-free Continual Learning with Winning Subnetworks
Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits
Spike Distance Function as a Learning Objective for Spike Prediction
Tight Regret Bounds for Single-pass Streaming Multi-armed Bandits
The Value Function Polytope in Reinforcement Learning
On learning with kernels for unordered pairs
NA 2 Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning
Compact approximations to Bayesian predictive distributions
TIDE: Time Derivative Diffusion for Deep Learning on Graphs
Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story
Learning Event Completeness for Weakly Supervised Video Anomaly Detection
On the Training Instability of Shuffling SGD with Batch Normalization
A New Computationally Efficient Algorithm to solve Feature Selection for Functional Data Classification in High-dimensional Spaces
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
Budgeted Nonparametric Learning from Data Streams
Position: A Call to Action for a Human-Centered AutoML Paradigm
RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank
Optimal assignment kernels for attributed molecular graphs
Learning Control by Iterative Inversion
Leveraging Union of Subspace Structure to Improve Constrained Clustering
Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components
Fiduciary Bandits
Model Selection in Batch Policy Optimization
Two-view feature generation model for semi-supervised learning
Learning with non-positive kernels
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making
AEQA-NAT : Adaptive End-to-end Quantization Alignment Training Framework for Non-autoregressive Machine Translation
Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect
Robust Secure Swap: Responsible Face Swap With Persons of Interest Redaction and Provenance Traceability
Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models
TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
Revisiting precision recall definition for generative modeling
A Kernel Between Sets of Vectors
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
PAGER: Accurate Failure Characterization in Deep Regression Models
Rates of Convergence of Spectral Methods for Graphon Estimation
Supervised and Unsupervised Discretization of Continuous Features
A new Mallows distance based metric for comparing clusterings
Countering Language Drift with Seeded Iterated Learning
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches
SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
Diversity-Promoting Bayesian Learning of Latent Variable Models
Homomorphism Counts for Graph Neural Networks: All About That Basis
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
Application of Machine Learning To Epileptic Seizure Detection
Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten"
Balancing Discriminability and Transferability for Source-Free Domain Adaptation
Multiple Clustering Views from Multiple Uncertain Experts
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
Generalization on the Unseen, Logic Reasoning and Degree Curriculum
GANMEX: One-vs-One Attributions using GAN-based Model Explainability
On the Problem of Underranking in Group-Fair Ranking
Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context
Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks
Recurrent Orthogonal Networks and Long-Memory Tasks
Learning Logic Programs for Layout Analysis Correction
Short-Term Plasticity Neurons Learning to Learn and Forget
Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Wide Neural Networks Forget Less Catastrophically
A Conditional Random Field for Multiple-Instance Learning
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
Leverage Class-Specific Accuracy to Guide Data Generation for Improving Image Classification
Learning Soft Sparse Shapes for Efficient Time-Series Classification
Manifold-adaptive dimension estimation
Dynamic Metric Embedding into lp Space
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Autoencoder-Based Hybrid Replay for Class-Incremental Learning
Learn from Downstream and Be Yourself in Multimodal Large Language Models Fine-Tuning
Lightweight Image Super-Resolution via Flexible Meta Pruning
Sanity Simulations for Saliency Methods
Action Robust Reinforcement Learning and Applications in Continuous Control
Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function
TD(0) Converges Provably Faster than the Residual Gradient Algorithm
Exact Maximum Margin Structure Learning of Bayesian Networks
Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations
Aligning Language Models with Preferences through f-divergence Minimization
TabFlex: Scaling Tabular Learning to Millions with Linear Attention
Balancing Model Efficiency and Performance: Adaptive Pruner for Long-tailed Data
Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification
Unified K-Means Clustering with Label-Guided Manifold Learning
Actor-Critic based Improper Reinforcement Learning
Fully Dynamic Submodular Maximization over Matroids
Less is More: Active Learning with Support Vector Machines
Incentivizing Compliance with Algorithmic Instruments
Learning Horn Expressions with LogAn-H
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change
Kernel-Based Reinforcement Learning: A Finite-Time Analysis
Uncertainty for Active Learning on Graphs
Direct Policy Search using Paired Statistical Tests
Efficient Dimensionality Reduction for Canonical Correlation Analysis
Fully Dynamic Euclidean Bi-Chromatic Matching in Sublinear Update Time
Towards Resource-friendly, Extensible and Stable Incomplete Multi-view Clustering
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Embedding Safety into RL: A New Take on Trust Region Methods
Time Weaver: A Conditional Time Series Generation Model
Monochromatic Bi-Clustering
Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models
Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
Unsupervised Learning with Permuted Data
EM-Network: Oracle Guided Self-distillation for Sequence Learning
Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems
Individually Fair Learning with One-Sided Feedback
Adapting to Evolving Adversaries with Regularized Continual Robust Training
Fast Decoding in Sequence Models Using Discrete Latent Variables
Optimally Improving Cooperative Learning in a Social Setting
Efficient Learning with Partially Observed Attributes
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Fast Global Alignment Kernels
Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing
Partially supervised feature selection with regularized linear models
Active Learning from Crowds
Breaking Barriers: Combinatorial Algorithms for Non-Monotone Submodular Maximization with Sublinear Adaptivity and 1/e Approximation
Low-rank tensor completion: a Riemannian manifold preconditioning approach
Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization
A fully differentiable beam search decoder
A Study of Face Obfuscation in ImageNet
Super-efficiency of automatic differentiation for functions defined as a minimum
Sharp Generalization for Nonparametric Regression by Over-Parameterized Neural Networks: A Distribution-Free Analysis in Spherical Covariate
Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions
MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space
Trust region Newton methods for large-scale logistic regression
Federated Combinatorial Multi-Agent Multi-Armed Bandits
Extracting Latent State Representations with Linear Dynamics from Rich Observations
On the Generalization of Multi-modal Contrastive Learning
Adversarial Combinatorial Bandits with General Non-linear Reward Functions
Practical and Private (Deep) Learning Without Sampling or Shuffling
Measuring Robustness in Deep Learning Based Compressive Sensing
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
A PAC-Bayesian bound for Lifelong Learning
On the Partition Function and Random Maximum A-Posteriori Perturbations
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
Do We Really Need Message Passing in Brain Network Modeling?
Approximate inference by Markov chains on union spaces
A Model-free Closeness-of-influence Test for Features in Supervised Learning
Adaptive Regularization for Similarity Measures
G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Robust Submodular Maximization: A Non-Uniform Partitioning Approach
Random Grid Neural Processes for Parametric Partial Differential Equations
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
When Does Self-Supervision Help Graph Convolutional Networks?
FedBoost: A Communication-Efficient Algorithm for Federated Learning
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent
Feature subset selection bias for classification learning
Risk Estimation in a Markov Cost Process: Lower and Upper Bounds
On learning linear ranking functions for beam search