yusuke1997/dblp-id-title huggingface dataset:
Supervised learning: no loss no cry
Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces
Accelerated Diffusion Models via Speculative Sampling
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension
Predictive linear-Gaussian models of controlled stochastic dynamical systems
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
Boosting grammatical inference with confidence oracles
Hypo3D: Exploring Hypothetical Reasoning in 3D
Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
Identifying Best Interventions through Online Importance Sampling
Dynamic Regret of Online Markov Decision Processes
EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification
TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems
Contrastive Visual Data Augmentation
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
Looping suffix tree-based inference of partially observable hidden state
Learning Complex Neural Network Policies with Trajectory Optimization
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
Inference Networks for Sequential Monte Carlo in Graphical Models
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
Towards Minimax Online Learning with Unknown Time Horizon
Sable: a Performant, Efficient and Scalable Sequence Model for MARL
No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets
Challenges of the Email Domain for Text Classification
Near-optimal sample complexity bounds for learning Latent k-polytopes and applications to Ad-Mixtures
Optimal Classification with Multivariate Losses
Optimizing Adaptive Attacks against Watermarks for Language Models
Adversarial Perturbations Are Formed by Iteratively Learning Linear Combinations of the Right Singular Vectors of the Adversarial Jacobian
Identifying Metric Structures of Deep Latent Variable Models
Putting the "Learning" into Learning-Augmented Algorithms for Frequency Estimation
GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation
FedECADO: A Dynamical System Model of Federated Learning
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Kernelized value function approximation for reinforcement learning
Learning de-identified representations of prosody from raw audio
Beat the Mean Bandit
A Theory-Refinement Approach to Information Extraction
Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
The Nonparametric Metadata Dependent Relational Model
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning
Region-based value iteration for partially observable Markov decision processes
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders
Implicit Generative Modeling for Efficient Exploration
On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning
An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule
Learning to Branch for Multi-Task Learning
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
Covering Number for Efficient Heuristic-based POMDP Planning
Low-Rank Sinkhorn Factorization
Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data
ÉLÉNA: A Bottom-Up Learning Method
Reinforcement Learning and Shaping: Encouraging Intended Behaviors
Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons
An Online Statistical Framework for Out-of-Distribution Detection
Adversarially trained neural representations are already as robust as biological neural representations
Fast k-means with accurate bounds
COExpander: Adaptive Solution Expansion for Combinatorial Optimization
Multifactor Gaussian process models for style-content separation
Faster Eigenvector Computation via Shift-and-Invert Preconditioning
Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains
Parameterized Physics-informed Neural Networks for Parameterized PDEs
Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields
The Role of Sparsity for Length Generalization in LLMs
Compact Random Feature Maps
Multicalibration for Confidence Scoring in LLMs
Enhancing Logits Distillation with Plug&Play Kendall's τ Ranking Loss
Incomplete-data classification using logistic regression
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
PHEW : Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data
Neural Architecture Search in A Proxy Validation Loss Landscape
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
Online Continual Learning from Imbalanced Data
Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery
Bayesian Model Selection, the Marginal Likelihood, and Generalization
Bivariate Causal Discovery with Proxy Variables: Integral Solving and Beyond
FILTRA: Rethinking Steerable CNN by Filter Transform
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Adaptive kernel predictors from feature-learning infinite limits of neural networks
A Simple Guard for Learned Optimizers
Generalized Average-Case Analyses of the Nearest Neighbor Algorithm
High Fidelity Image Counterfactuals with Probabilistic Causal Models
Convergence of Gradient Dynamics with a Variable Learning Rate
Reinforcement Learning as Classification: Leveraging Modern Classifiers
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process
Feature Extraction and Clustering of Tactile Impressions with Connectionist Models
Hierarchy Through Composition with Multitask LMDPs
A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting
MathScale: Scaling Instruction Tuning for Mathematical Reasoning
Taxonomy-Structured Domain Adaptation
Equilibrium of Data Markets with Externality
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
An object-oriented representation for efficient reinforcement learning
Learn to weight terms in information retrieval using category information
MFTN: A Multi-scale Feature Transfer Network Based on IMatchFormer for Hyperspectral Image Super-Resolution
Target-Based Temporal-Difference Learning
GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language Models
Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition
Necessary and sufficient conditions for causal feature selection in time series with latent common causes
Learning Domain Adaptive Object Detection with Probabilistic Teacher
Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning
Channel Equilibrium Networks for Learning Deep Representation
Learning to Make Rent-to-Buy Decisions with Systems Applications
Post-Inference Prior Swapping
Understanding Fixed Predictions via Confined Regions
The rate adapting poisson model for information retrieval and object recognition
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
NMA-tune: Generating Highly Designable and Dynamics Aware Protein Backbones
A Unified Lottery Ticket Hypothesis for Graph Neural Networks
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
Rethink GraphODE Generalization within Coupled Dynamical System
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
Investigating Generalization by Controlling Normalized Margin
Hybrid Neural Representations for Spherical Data
A Comparative Study of Cost-Sensitive Boosting Algorithms
Topological Data Analysis of Decision Boundaries with Application to Model Selection
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives
Floating Anchor Diffusion Model for Multi-motif Scaffolding
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
BinauralFlow: A Causal and Streamable Approach for High-Quality Binaural Speech Synthesis with Flow Matching Models
The Role of Randomness in Stability
From Generalization Analysis to Optimization Designs for State Space Models
Large-Scale Bandit Problems and KWIK Learning
Learning Sorting and Decision Trees with POMDPs
Apprenticeship learning using linear programming
Automated Model Selection with Bayesian Quadrature
Adaptive Computation with Elastic Input Sequence
Differentially Private Clustering in High-Dimensional Euclidean Spaces
Learning Dynamics in Continual Pre-Training for Large Language Models
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Textural or Textual: How Vision-Language Models Read Text in Images
Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks
SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
Automatic Feature Generation for Problem Solving Systems
Debiaser Beware: Pitfalls of Centering Regularized Transport Maps
Position: Video as the New Language for Real-World Decision Making
Inference with the Universum
Understanding and Improving Knowledge Graph Embedding for Entity Alignment
Permutation invariant SVMs
Conditional Temporal Neural Processes with Covariance Loss
Efficient Dictionary Learning with Gradient Descent
Model Immunization from a Condition Number Perspective
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Momentum-Based Policy Gradient Methods
T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP
Partition First, Embed Later: Laplacian-Based Feature Partitioning for Refined Embedding and Visualization of High-Dimensional Data
Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition
Markov-modulated Marked Poisson Processes for Check-in Data
Learning from Similarity-Confidence Data
Spectrally Approximating Large Graphs with Smaller Graphs
Bayesian pattern ranking for move prediction in the game of Go
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
Differentially Private Worst-group Risk Minimization
Whittle Networks: A Deep Likelihood Model for Time Series
Deep Voice: Real-time Neural Text-to-Speech
Focus On This, Not That! Steering LLMs with Adaptive Feature Specification
Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators
Reflected Flow Matching
Understanding the Emergence of Multimodal Representation Alignment
Agnostic Sample Compression Schemes for Regression
Machine Learning with Hyperkernels
Minimum Probability Flow Learning
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Compositional Generalization via Forced Rendering of Disentangled Latents
Scalable Internal-State Policy-Gradient Methods for POMDPs
Experiments in Non-Monotonic Learning
Data Poisoning Attacks Against Multimodal Encoders
TruthFlow: Truthful LLM Generation via Representation Flow Correction
Investigating Non-Transitivity in LLM-as-a-Judge
An Analysis of Direct Reinforcement Learning in Non-Markovian Domains
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
Kronecker Recurrent Units
Beyond the Norms: Detecting Prediction Errors in Regression Models
Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model
Reinforcement Learning for Quantum Control under Physical Constraints
On the Integration of Topic Modeling and Dictionary Learning
Combining Explanation-Based Learning and Artificial Neural Networks
Learning from Scarce Experience
A Nonparametric Approach to Noisy and Costly Optimization
One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes
xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Learning Structured Decision Problems with Unawareness
An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks
Eventual Discounting Temporal Logic Counterfactual Experience Replay
Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)
On the Implicit Bias of Adam
From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models
Quantization Algorithms for Random Fourier Features
SparseMAP: Differentiable Sparse Structured Inference
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
Differentially Private Distributed Bayesian Linear Regression with MCMC
Quantum Optimization via Gradient-Based Hamiltonian Descent
Bayesian Optimization for Distributionally Robust Chance-constrained Problem
Competitive Reinforcement Learning
Robustness to Programmable String Transformations via Augmented Abstract Training
Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds
Convex Learning of Multiple Tasks and their Structure
ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy
Randomized Exploration in Reinforcement Learning with General Value Function Approximation
HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
Temporal Label Smoothing for Early Event Prediction
Representing Molecules as Random Walks Over Interpretable Grammars
Graph Out-of-Distribution Detection Goes Neighborhood Shaping
Inference algorithms for pattern-based CRFs on sequence data
PASTA: Pessimistic Assortment Optimization
Retrieval Across Any Domains via Large-scale Pre-trained Model
When Do LLMs Help With Node Classification? A Comprehensive Analysis
Robust and Efficient Kernel Hyperparameter Paths with Guarantees
End-to-end Active Object Tracking via Reinforcement Learning
Combining Instance-Based and Model-Based Learning
On the Power of Compressed Sensing with Generative Models
Learning Optimal Tree Models under Beam Search
Perturb-and-Project: Differentially Private Similarities and Marginals
Structural Re-weighting Improves Graph Domain Adaptation
MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity
Explainability as statistical inference
Accelerating Shapley Explanation via Contributive Cooperator Selection
SQL-Rank: A Listwise Approach to Collaborative Ranking
Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting
Interactive Learning from Activity Description
Transformers, parallel computation, and logarithmic depth
Continual Repeated Annealed Flow Transport Monte Carlo
Generalizing the Order of Operators in Macro-Operators
f-Domain Adversarial Learning: Theory and Algorithms
Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders
Parallel Online Clustering of Bandits via Hedonic Game
Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
CLARIFY: Contrastive Preference Reinforcement Learning for Untangling Ambiguous Queries
Using inaccurate models in reinforcement learning
Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes
Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
Model-based Offline Reinforcement Learning with Count-based Conservatism
Ordinal Graphical Models: A Tale of Two Approaches
Lower Bounds for Learning in Revealing POMDPs
Stability of matrix factorization for collaborative filtering
DataFreeShield: Defending Adversarial Attacks without Training Data
Essentially No Barriers in Neural Network Energy Landscape
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
A decoupled approach to exemplar-based unsupervised learning
The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models Via Visual Information Steering
Nearly Optimal Sample Complexity for Learning with Label Proportions
A Framework for Private Matrix Analysis in Sliding Window Model
On Combining Multiple Speedup Techniques
The Importance of Causal Structure and Facts in Evaluating Explanations
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
Bounding the Partition Function using Holder's Inequality
Deep AutoRegressive Networks
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution
QuRating: Selecting High-Quality Data for Training Language Models
Concept Reachability in Diffusion Models: Beyond Dataset Constraints
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
Controlled Differential Equations on Long Sequences via Non-standard Wavelets
Machine Learning by Function Decomposition
Neural Inverse Kinematic
Bayesian Sparsification of Deep C-valued Networks
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
Optimization and Analysis of the pAp@k Metric for Recommender Systems
Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Compressive Spectral Clustering
Finding Wasserstein Ball Center: Efficient Algorithm and The Applications in Fairness
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
The Loss Surface of Deep and Wide Neural Networks
Efficiently solving convex relaxations for MAP estimation
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Weighted Sampling without Replacement for Deep Top-k Classification
Learning state-action basis functions for hierarchical MDPs
Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization
Slimming the Fat-Tail: Morphing-Flow for Adaptive Time Series Modeling
The Falling Factorial Basis and Its Statistical Applications
On Teacher Hacking in Language Model Distillation
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
Parameter learning for relational Bayesian networks
SDQ: Stochastic Differentiable Quantization with Mixed Precision
EL-Attention: Memory Efficient Lossless Attention for Generation
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation
Input-Sparsity Low Rank Approximation in Schatten Norm
Adversarial Robustness against Multiple and Single l p -Threat Models via Quick Fine-Tuning of Robust Classifiers
Efficient Motion Prompt Learning for Robust Visual Tracking
Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case
Is Local SGD Better than Minibatch SGD?
Pre-training for Speech Translation: CTC Meets Optimal Transport
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
A Structural SVM Based Approach for Optimizing Partial AUC
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning
TAROT: Targeted Data Selection via Optimal Transport
Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling
The advantages of multiple classes for reducing overfitting from test set reuse
Approximate Predictive Representations of Partially Observable Systems
Distributed Stochastic Gradient MCMC
DOGE: Domain Reweighting with Generalization Estimation
Vision-Language Models Create Cross-Modal Task Representations
An integrated approach to feature invention and model construction for drug activity prediction
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Is Complex Query Answering Really Complex?
Landmarking Manifolds with Gaussian Processes
Continuously Parameterized Mixture Models
Optimal Differentially Private Model Training with Public Data
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
Active Explanation Reduction: An Approach to the Multiple Explanations Problem
De-AntiFake: Rethinking the Protective Perturbations Against Voice Cloning Attacks
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping
Local Low-Rank Matrix Approximation
LongRoPE2: Near-Lossless LLM Context Window Scaling
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
DropNet: Reducing Neural Network Complexity via Iterative Pruning
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization
The Need for Constructive Induction
Learning to Label Aerial Images from Noisy Data
Statistical Foundations of Prior-Data Fitted Networks
Empowering Graph Invariance Learning with Deep Spurious Infomax
Algorithmic Recourse for Long-Term Improvement
Generalization Analysis of Deep Non-linear Matrix Completion
video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model
Delay-DSGN: A Dynamic Spiking Graph Neural Network with Delay Mechanisms for Evolving Graph
Coupled Variational Autoencoder
Statistical Collusion by Collectives on Learning Platforms
Learning Programs: A Hierarchical Bayesian Approach
DS-VLM: Diffusion Supervision Vision Language Model
Kernel QuantTree
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing
Improved Optimistic Algorithms for Logistic Bandits
Provably Efficient Imitation Learning from Observation Alone
Fast computation of Nash Equilibria in Imperfect Information Games
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective
Conditional mean embeddings as regressors
Temporal Gaussian Mixture Layer for Videos
Graphite: Iterative Generative Modeling of Graphs
A Role for Anticipation in Reactive Systems that Learn
Generalization in Federated Learning: A Conditional Mutual Information Framework