yusuke1997/dblp-id-title huggingface dataset:
Efficient Online Reinforcement Learning with Offline Data
On The Power of Curriculum Learning in Training Deep Networks
Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space
On The Projection Operator to A Three-view Cardinality Constrained Set
Learning the Experts for Online Sequence Prediction
Beyond log 2 (T) regret for decentralized bandits in matching markets
A Difference Standardization Method for Mutual Transfer Learning
Multilayer Matrix Factorization via Dimension-Reducing Diffusion Variational Inference
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization
Data-Dependent Stability of Stochastic Gradient Descent
On the Convergence of Federated Averaging with Cyclic Client Participation
k-variates++: more pluses in the k-means++
BiBench: Benchmarking and Analyzing Network Binarization
Agnostic System Identification for Model-Based Reinforcement Learning
Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
HEAP: Hyper Extended A-PDHG Operator for Constrained High-dim PDEs
Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
Redundant feature elimination for multi-class problems
Mixture Proportion Estimation via Kernel Embeddings of Distributions
Graph Mixup on Approximate Gromov-Wasserstein Geodesics
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
Copula-based Kernel Dependency Measures
DiffusionVLA: Scaling Robot Foundation Models via Unified Diffusion and Autoregression
LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits
Auxiliary Deep Generative Models
Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms
Stacking Deep Set Networks and Pooling by Quantiles
On a Combination of Alternating Minimization and Nesterov's Momentum
Generalization error bounds for learning to rank: Does the length of document lists matter?
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
NExtLong: Toward Effective Long-Context Training without Long Documents
Enhancing Adversarial Robustness in SNNs with Sparse Gradients
Localizing Policy Gradient Estimates to Action Transition
Input uncertainty propagation through trained neural networks
A Kernel-Based View of Language Model Fine-Tuning
AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation
Follow-the-Regularized-Leader Routes to Chaos in Routing Games
One-dimensional Path Convolution
Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently
Position: Future Directions in the Theory of Graph Machine Learning
Why Larger Language Models Do In-context Learning Differently?
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation
"Boosting'' a Positive-Data-Only Learner
Local Private Hypothesis Testing: Chi-Square Tests
Prediction models that learn to avoid missing values
Representation Surgery for Multi-Task Model Merging
Policy Consolidation for Continual Reinforcement Learning
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization
DOLPHIN: A Programmable Framework for Scalable Neurosymbolic Learning
Composing Normalizing Flows for Inverse Problems
Safe Policy Iteration
Adaptive Data Collection for Robust Learning Across Multiple Distributions
Active Learning of Parameterized Skills
Representative Ranking for Deliberation in the Public Sphere
Solving multiclass support vector machines with LaRank
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
Don't Restart, Just Reuse: Reoptimizing MILPs with Dynamic Parameters
Cognitive Models of Test-Item Effects in Human Category Learning
OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models
Learning to Design Analog Circuits to Meet Threshold Specifications
Gradient Descent Converges Linearly for Logistic Regression on Separable Data
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs
Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
Maximum Margin Multiclass Nearest Neighbors
Neural Guided Diffusion Bridges
Multi-instance multi-label learning in the presence of novel class instances
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
Graph-based Isometry Invariant Representation Learning
Optimal Shrinkage for Distributed Second-Order Optimization
Variational Schrödinger Diffusion Models
Aggregation of Multiple Knockoffs
Second-Order Provable Defenses against Adversarial Attacks
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions
Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
Slot-VAE: Object-Centric Scene Generation with Slot Attention
The Variational Nystrom method for large-scale spectral problems
Generation from Noisy Examples
Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU
Multi-Objective Molecule Generation using Interpretable Substructures
Obtaining Fairness using Optimal Transport Theory
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
Learning to Separate Voices by Spatial Regions
Learning to Branch
Robust Adversarial Reinforcement Learning
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification
Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments
Nightmare at test time: robust learning by feature deletion
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning
In value-based deep reinforcement learning, a pruned network is a good network
A procedure for unsupervised lexicon learning
An Experimental Comparison of Human and Machine Learning Formalisms
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization
Towards Efficient Training and Evaluation of Robust Models against l0 Bounded Adversarial Perturbations
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction
The Tree Ensemble Layer: Differentiability meets Conditional Computation
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior
Learning from measurements in exponential families
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Neural Inverse Rendering for General Reflectance Photometric Stereo
Near-Optimal Joint Object Matching via Convex Relaxation
Cyclically Equivariant Neural Decoders for Cyclic Codes
Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models
Symmetry Induces Structure and Constraint of Learning
DUPLEX: Dual GAT for Complex Embedding of Directed Graphs
Ditto: Quantization-aware Secure Inference of Transformers upon MPC
Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points
Improving Path Planning with Learning
Online Learning in the Random-Order Model
Predicting probability distributions for surf height using an ensemble of mixture density networks
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain
On the Robustness of Reward Models for Language Model Alignment
Learning the structure of Markov logic networks
Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching
Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies
Off-Policy Reinforcement Learning with Delayed Rewards
Grammar Variational Autoencoder
A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training
RAGGED: Towards Informed Design of Scalable and Stable RAG Systems
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
Collapse or Thrive: Perils and Promises of Synthetic Data in a Self-Generating World
A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning
MILP-FBGen: LP/MILP Instance Generation with Feasibility/Boundedness
The Expressive Power of Path-Based Graph Neural Networks
End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization
Variational Russian Roulette for Deep Bayesian Nonparametrics
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation
Training CNNs with Selective Allocation of Channels
Statistical Inference on Multi-armed Bandits with Delayed Feedback
AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement
Toward Automated Rational Reconstruction: A Case Study
SimpleSVM
Learning with Adaptive Resource Allocation
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness
Robust Pure Exploration in Linear Bandits with Limited Budget
Representational Issues in Machine Learning
Certified Unlearning for Neural Networks
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
Workshop summary: On-line learning with limited feedback
Identifying Neural Dynamics Using Interventional State Space Models
Improving Model Alignment Through Collective Intelligence of Open-Source Models
The State of Sparse Training in Deep Reinforcement Learning
Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal
On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures
An Efficient Search-and-Score Algorithm for Ancestral Graphs using Multivariate Information Scores for Complex Non-linear and Categorical Data
No Dimensional Sampling Coresets for Classification
Refining Initial Points for K-Means Clustering
Comparing Few to Rank Many: Active Human Preference Learning Using Randomized Frank-Wolfe Method
Deep Reinforcement Learning with Robust and Smooth Policy
Pricing a Low-regret Seller
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Efficient Full-Matrix Adaptive Regularization
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
Orbit-product representation and correction of Gaussian belief propagation
Neural Network Control Policy Verification With Persistent Adversarial Perturbation
Do Not Mimic My Voice : Speaker Identity Unlearning for Zero-Shot Text-to-Speech
Nonparametric estimation of the precision-recall curve
Personalized handwriting recognition via biased regularization
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Experience-efficient learning in associative bandit problems
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety
QASA: Advanced Question Answering on Scientific Articles
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property
Density Ratio Estimation with Conditional Probability Paths
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
Parallelized Spatiotemporal Slot Binding for Videos
CerDEQ: Certifiable Deep Equilibrium Model
A Reasoning-Based Approach to Cryptic Crossword Clue Solving
Revisiting Unbiased Implicit Variational Inference
Zoo-Tuning: Adaptive Transfer from A Zoo of Models
Investigating the Overlooked Hessian Structure: From CNNs to LLMs
An Information Theoretic Approach to Interaction-Grounded Learning
Principled Algorithms for Optimizing Generalized Metrics in Binary Classification
Scalable Deep Gaussian Markov Random Fields for General Graphs
Theoretical Limitations of Ensembles in the Age of Overparameterization
Nonparametric Link Prediction in Dynamic Networks
Adversarial Neural Pruning with Latent Vulnerability Suppression
Unifying Molecular and Textual Representations via Multi-task Language Modelling
Joint Group and Topic Discovery from Relations and Text
Understanding Priors in Bayesian Neural Networks at the Unit Level
Noise-Guided Predicate Representation Extraction and Diffusion-Enhanced Discretization for Scene Graph Generation
On the Functional Similarity of Robust and Non-Robust Neural Representations
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
Self-supervised learning of Split Invariant Equivariant representations
The Polynomial Stein Discrepancy for Assessing Moment Convergence
Inference and Sampling of $K_33$-free Ising Models
Unlock the Cognitive Generalization of Deep Reinforcement Learning via Granular Ball Representation
Prompt-tuning Latent Diffusion Models for Inverse Problems
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Modular Conformal Calibration
DRCFS: Doubly Robust Causal Feature Selection
Hierarchical Clustering with Structural Constraints
Bifurcated Attention for Single-Context Large-Batch Sampling
GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities
Hidden No More: Attacking and Defending Private Third-Party LLM Inference
Generalized spectral bounds for sparse LDA
Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models
When Can Proxies Improve the Sample Complexity of Preference Learning?
Stabilizing Transformer Training by Preventing Attention Entropy Collapse
Optimal Batched Linear Bandits
A novel orthogonal NMF-based belief compression for POMDPs
Efficient Private Empirical Risk Minimization for High-dimensional Learning
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
Effective Bayesian Modeling of Groups of Related Count Time Series
The Test of Tests: A Framework for Differentially Private Hypothesis Testing
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds
Generalized Boosting Algorithms for Convex Optimization
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning
A New Representation of Successor Features for Transfer across Dissimilar Environments
Causality Based Front-door Defense Against Backdoor Attack on Language Models
Optimizing Sequential Experimental Design with Deep Reinforcement Learning
PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification
Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs
Rethinking Transformers in Solving POMDPs
Screening rules for Lasso with non-convex Sparse Regularizers
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
Meritocratic Fairness for Cross-Population Selection
The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling
Machine Learning, Proceedings of the Fifth International Conference on Machine Learning, Ann Arbor, Michigan, USA, June 12-14, 1988
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits
Privacy-Shielded Image Compression: Defending Against Exploitation from Vision-Language Pretrained Models
Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning
Robust Spatio-Temporal Centralized Interaction for OOD Learning
Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models
ERICT: Enhancing Robustness by Identifying Concept Tokens in Zero-Shot Vision Language Models
Constraining the Dynamics of Deep Probabilistic Models
Inverting the Viterbi algorithm: an abstract framework for structure design
DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence
Aligned Objective for Soft-Pseudo-Label Generation in Supervised Learning
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
Discovering Features with Synergistic Interactions in Multiple Views
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel Images
Smaller, more accurate regression forests using tree alternating optimization
Adapting to game trees in zero-sum imperfect information games
On a projective ensemble approach to two sample test for equality of distributions
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Cost
On the Expressive Power of Geometric Graph Neural Networks
The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright BreachesWithout Adjusting Finetuning Pipeline
Naive Exploration is Optimal for Online LQR
On learning to localize objects with minimal supervision
Deep Mixtures of Factor Analysers
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning
Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning
MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger Bridges
A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection
Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
Co-training Improves Prompt-based Learning for Large Language Models
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
Stratified Adversarial Robustness with Rejection
On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding
Forget Unlearning: Towards True Data-Deletion in Machine Learning
Hierarchical Decision Making In Electricity Grid Management
Can Mamba Learn How To Learn? A Comparative Study on In-Context Learning Tasks
On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures
One-Step Generalization Ratio Guided Optimization for Domain Generalization
On the Power of Context-Enhanced Learning in LLMs
A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
Automatic Rule Acquisition for Spelling Correction
Teaching Physical Awareness to LLMs through Sounds
An information theoretic analysis of maximum likelihood mixture estimation for exponential families
Finding Dense Subgraphs via Low-Rank Bilinear Optimization
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
The Benefits of Learning with Strongly Convex Approximate Inference
Which Invariance Should We Transfer? A Causal Minimax Learning Approach
SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment
Adaptive Gradient Descent without Descent
Modeling Hierarchical Structures with Continuous Recursive Neural Networks
Towards Escaping from Class Dependency Modeling for Multi-Dimensional Classification
Multi-Task Learning for Subspace Segmentation
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
Trading convexity for scalability
Causal Discovery via MML
Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
Limitations on Inductive Learning
Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example
Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling
Flow-based Domain Randomization for Learning and Sequencing Robotic Skills
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
Meta-learning Hyperparameter Performance Prediction with Neural Processes
Position: AI/ML Influencers Have a Place in the Academic Process
Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity
Modeling changing dependency structure in multivariate time series
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems
Fast Private Kernel Density Estimation via Locality Sensitive Quantization
Clipped SGD Algorithms for Performative Prediction: Tight Bounds for Stochastic Bias and Remedies
Learning Modular Structures from Network Data and Node Variables
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
No-Regret Learning in Partially-Informed Auctions
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
Scaling Laws for Multilingual Neural Machine Translation
Learning for efficient retrieval of structured data with noisy queries
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
DyPolySeg: Taylor Series-Inspired Dynamic Polynomial Fitting Network for Few-shot Point Cloud Semantic Segmentation
Fault Tolerance in Iterative-Convergent Machine Learning
Why the Rich Get Richer? On the Balancedness of Random Partition Models
Improved Theoretically-Grounded Evolutionary Algorithms for Subset Selection with a Linear Cost Constraint
Fastfood - Computing Hilbert Space Expansions in loglinear time
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data
Instance Dependent Regret Analysis of Kernelized Bandits
Online Convex Optimization in Adversarial Markov Decision Processes
MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition
Margin based feature selection - theory and algorithms
Incremental Learning of Control Strategies with Genetic algorithms
Declarative Bias in Equation Discovery
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
Generalized Random Forests Using Fixed-Point Trees
Constants Matter: The Performance Gains of Active Learning