yusuke1997/dblp-id-title huggingface dataset:
Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature
Retrieval-Augmented Diffusion Models
Embed and Project: Discrete Sampling with Universal Hashing
Meta-Learning Reliable Priors in the Function Space
NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction
Emergent Discrete Communication in Semantic Spaces
Diffusion-Convolutional Neural Networks
An Efficient Adversarial Attack for Tree Ensembles
Reinforcement Learning Using Approximate Belief States
Heterogeneous Neural Networks for Adaptive Behavior in Dynamic Environments
Towards training digitally-tied analog blocks via hybrid gradient computation
MassSpecGym: A benchmark for the discovery and identification of molecules
Generative multitask learning mitigates target-causing confounding
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies
Machine Learning for Video-Based Rendering
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
Learning to Pivot with Adversarial Networks
Bias-Corrected Bootstrap and Model Uncertainty
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL
A Bayes-Sard Cubature Method
Adaptive Market Making via Online Learning
Nonstationary Sparse Spectral Permanental Process
Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity
Maia-2: A Unified Model for Human-AI Alignment in Chess
Nearest Neighbor Speculative Decoding for LLM Generation and Attribution
On the Scalability of GNNs for Molecular Graphs
No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning
Base of RoPE Bounds Context Length
Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning
Beyond the Single Neuron Convex Barrier for Neural Network Certification
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Data Programming: Creating Large Training Sets, Quickly
Distributionally Robust Federated Averaging
Defending Against Adversarial Attacks via Neural Dynamic System
A Cross-Domain Benchmark for Active Learning
Managing Uncertainty in Cue Combination
Simple and Efficient Weighted Minwise Hashing
Optimal Top-Two Method for Best Arm Identification and Fluid Analysis
An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise
Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-Tuning
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
One-Shot Imitation Learning
Learning Hierarchical Priors in VAEs
Blind Deconvolutional Phase Retrieval via Convex Programming
Training a Limited-Interconnect, Synthetic Neural IC
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features
Application of SVMs for Colour Classification and Collision Detection with AIBO Robots
Inference for determinantal point processes without spectral knowledge
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer
Compositional PAC-Bayes: Generalization of GNNs with persistence and beyond
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO
Extracting Latent Structure From Multiple Interacting Neural Populations
Permutation Equivariant Neural Functionals
Consistent Robust Regression
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks
Dynamic Fair Division with Partial Information
Transferring disentangled representations: bridging the gap between synthetic and real images
Calibrated Structured Prediction
UQ-Guided Hyperparameter Optimization for Iterative Learners
Using the Nyström Method to Speed Up Kernel Machines
Towards Better Dynamic Graph Learning: New Architecture and Unified Library
Factorial Learning and the EM Algorithm
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
FIRE: Semantic Field of Words Represented as Non-Linear Functions
Relation-Constrained Decoding for Text Generation
Robust Rent Division
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
Provable Submodular Minimization using Wolfe's Algorithm
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
Overcoming the Convex Barrier for Simplex Inputs
Learning with Target Prior
Optimistic Tree Searches for Combinatorial Black-Box Optimization
Semidefinite Relaxations for Approximate Inference on Graphs with Cycles
Strategic Attentive Writer for Learning Macro-Actions
Toxicity Detection for Free
Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks
Adversarial Surrogate Losses for Ordinal Regression
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
The Clusteron: Toward a Simple Abstraction for a Complex Neuron
ReaSCAN: Compositional Reasoning in Language Grounding
Distributed Newton Can Communicate Less and Resist Byzantine Workers
Measuring Neural Synchrony by Message Passing
CAPP-130: A Corpus of Chinese Application Privacy Policy Summarization and Interpretation
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity
RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models
Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery
DeepGEM: Generalized Expectation-Maximization for Blind Inversion
NeRF Revisited: Fixing Quadrature Instability in Volume Rendering
Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games
Linear Label Ranking with Bounded Noise
Prospective Learning: Learning for a Dynamic Future
Point Cloud Completion with Pretrained Text-to-Image Diffusion Models
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning
Large-Scale Stochastic Sampling from the Probability Simplex
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
Uncoupled Learning Dynamics with O(log T) Swap Regret in Multiplayer Games
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training
Cookie Consent Has Disparate Impact on Estimation Accuracy
Can Active Memory Replace Attention?
PAC-Bayes Tree: weighted subtrees with guarantees
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space
Complexity of Derivative-Free Policy Optimization for Structured H ∞ Control
Learning Sample-Specific Models with Low-Rank Personalized Regression
Mirror and Preconditioned Gradient Descent in Wasserstein Space
Learning Strategic Network Emergence Games
Universal Kernels on Non-Standard Input Spaces
Generative Forests
Subject-driven Text-to-Image Generation via Apprenticeship Learning
Regularized Modal Regression with Applications in Cognitive Impairment Prediction
Coupled Mamba: Enhanced Multimodal Fusion with Coupled State Space Model
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
SCaR: Refining Skill Chaining for Long-Horizon Robotic Manipulation via Dual Regularization
Synchronization, Oscillations and 1/f Noise in Networks of Spiking Neurons
Inverse-Weighted Survival Games
Learning Influence Functions from Incomplete Observations
Orientation Contrast Sensitivity from Long-range Interactions in Visual Cortex
Doubly Robust Thompson Sampling with Linear Payoffs
Efficiently incorporating quintuple interactions into geometric deep learning force fields
Models of Ocular Dominance Column Formation: Analytical and Computational Results
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent
LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search
Instance Based Approximations to Profile Maximum Likelihood
Deep Extrapolation for Attribute-Enhanced Generation
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
A Maximum-Likelihood Approach to Modeling Multisensory Enhancement
Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers
Projected GANs Converge Faster
Time-Varying Dynamic Bayesian Networks
M-Best-Diverse Labelings for Submodular Energies and Beyond
Minimax-Optimal Location Estimation
Classification with Hybrid Generative/Discriminative Models
GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent
Generating Behaviorally Diverse Policies with Latent Diffusion Models
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration
Zonotope Domains for Lagrangian Neural Network Verification
QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction
Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods
SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
Fair Graph Distillation
Transductive and Inductive Methods for Approximate Gaussian Process Regression
OneBit: Towards Extremely Low-bit Large Language Models
Joint Training of Deep Ensembles Fails Due to Learner Collusion
On the detrimental effect of invariances in the likelihood for variational inference
On the Out-of-distribution Generalization of Probabilistic Image Modelling
Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
Dimension-free deterministic equivalents and scaling laws for random feature regression
SING: Symbol-to-Instrument Neural Generator
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
DISCS: A Benchmark for Discrete Sampling
MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-Object Demand-driven Navigation
Sparse is Enough in Scaling Transformers
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes
MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encoding
f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences
Knowledge Distillation for High Dimensional Search Index
LLMs Can Evolve Continually on Modality for X-Modal Reasoning
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers
Nonparametric Bayesian Texture Learning and Synthesis
Autoencoders, Minimum Description Length and Helmholtz Free Energy
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting
Adaptive Online Learning
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
A Probabilistic U-Net for Segmentation of Ambiguous Images
Fourier Sparse Leverage Scores and Approximate Kernel Learning
A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification
Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Bayesian Nonparametric Modeling of Suicide Attempts
Learning to Exploit Stability for 3D Scene Parsing
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Conservative Offline Policy Adaptation in Multi-Agent Games
Evaluating Open-QA Evaluation
Local Hyper-Flow Diffusion
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation
Pattern Playback in the 90s
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
A recipe for optimizing a time-histogram
Coherent Soft Imitation Learning
Uplift Modeling from Separate Labels
Nested Counterfactual Identification from Arbitrary Surrogate Experiments
Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
Does Graph Distillation See Like Vision Dataset Counterpart?
GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Partially View-aligned Clustering
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
Perceiving without Learning: From Spirals to Inside/Outside Relations
A lattice filter model of the visual pathway
BitDelta: Your Fine-Tune May Only Be Worth One Bit
Learning efficient task-dependent representations with synaptic plasticity
SkipPredict: When to Invest in Predictions for Scheduling
Accelerated Proximal Gradient Methods for Nonconvex Programming
Hybrid Reinforcement Learning and Its Application to Biped Robot Control
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Efficient nonmyopic batch active search
PAC-Bayes bounds for stable algorithms with instance-dependent priors
Meta-trained agents implement Bayes-optimal agents
RB2: Robotic Manipulation Benchmarking with a Twist
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
Deep Bayesian Active Learning for Preference Modeling in Large Language Models
SparCL: Sparse Continual Learning on the Edge
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments
Alternating Estimation for Structured High-Dimensional Multi-Response Models
Regularization in ResNet with Stochastic Depth
On the Effects of Data Scale on UI Control Agents
Fair Kernel K-Means: from Single Kernel to Multiple Kernel
Implicit Optimization Bias of Next-token Prediction in Linear Models
Image Captioning: Transforming Objects into Words
Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask?
Learning Bounds for Importance Weighting
MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection
Stateful ODE-Nets using Basis Function Expansions
A Collaborative Mechanism for Crowdsourcing Prediction Problems
Weighted Linear Bandits for Non-Stationary Environments
Dependent Gaussian Processes
Communication-efficient distributed eigenspace estimation with arbitrary node failures
DesCo: Learning Object Recognition with Rich Language Descriptions
BoxE: A Box Embedding Model for Knowledge Base Completion
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays
VeriX: Towards Verified Explainability of Deep Neural Networks
Bayesian Sets
Spatiotemporal Feature Extraction with Data-Driven Koopman Operators
EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models
Optimization over Continuous and Multi-dimensional Decisions with Observational Data
An Optimal Elimination Algorithm for Learning a Best Arm
Torsional Diffusion for Molecular Conformer Generation
Natural Language Grammar Induction Using a Constituent-Context Model
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection
Truthful Data Acquisition via Peer Prediction
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Clustering Under Prior Knowledge with Application to Image Segmentation
Characterizing possible failure modes in physics-informed neural networks
Reinforcement Learning with Convex Constraints
CMOL CrossNets: Possible Neuromorphic Nanoelectronic Circuits
Why Do Better Loss Functions Lead to Less Transferable Features?
Fair Clustering Through Fairlets
Optimization Can Learn Johnson Lindenstrauss Embeddings
Federated Black-Box Adaptation for Semantic Segmentation
Rewiring Neurons in Non-Stationary Environments
Truncated Linear Regression in High Dimensions
A Convolutional Neural Network Hand Tracker
Train 'n Trade: Foundations of Parameter Markets
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs
Escaping from saddle points on Riemannian manifolds
Flaws can be Applause: Unleashing Potential of Segmenting Ambiguous Objects in SAM
Visual Speech Recognition with Stochastic Networks
Self-supervised Learning of Motion Capture
Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
A kernel test for quasi-independence
Categorical Flow Matching on Statistical Manifolds
A Model to Search for Synthesizable Molecules
GRIFT: A graphical model for inferring visual classification features from human data
Fair Exploration via Axiomatic Bargaining
Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise
Structured Learning with Approximate Inference
LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models
Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares
Trade-Offs of Diagonal Fisher Information Matrix Estimators
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds
Branch and Bound for Semi-Supervised Support Vector Machines
Off-policy evaluation for slate recommendation
DDK: Distilling Domain Knowledge for Efficient Large Language Models
Scalable Diverse Model Selection for Accessible Transfer Learning
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
StopWasting My Gradients: Practical SVRG
Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli
Shaping embodied agent behavior with activity-context priors from egocentric video
Learning to Discover Social Circles in Ego Networks
Exploiting spatial overlap to efficiently compute appearance distances between image windows
Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
When to Trust Your Model: Model-Based Policy Optimization
Reinforcement Learning with Non-Exponential Discounting
Maximization of Approximately Submodular Functions
Balancing Multiple Sources of Reward in Reinforcement Learning
Probabilistic ODE Solvers with Runge-Kutta Means
The Effect of Singularities in a Learning Machine when the True Parameters Do Not Lie on such Singularities
Variational Inference in Mixed Probabilistic Submodular Models
Variational Monte Carlo on a Budget - Fine-tuning pre-trained Neural Wavefunctions
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
Universally Expressive Communication in Multi-Agent Reinforcement Learning
Zero-Regret Performative Prediction Under Inequality Constraints
BYOL-Explore: Exploration by Bootstrapped Prediction
The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction
Improving Viewpoint-Independent Object-Centric Representations through Active Viewpoint Selection
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization
Long-Term Fairness with Unknown Dynamics
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking
An Integrated Vision Sensor for the Computation of Optical Flow Singular Points
Satisfying Real-world Goals with Dataset Constraints
Adversarial Symmetric Variational Autoencoder
Neural Unsigned Distance Fields for Implicit Function Learning
Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Geodesic Self-Attention for 3D Point Clouds
A Computational Basis for Phonology
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
Neural Nearest Neighbors Networks
Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs
On Lazy Training in Differentiable Programming
Boosting the Transferability of Adversarial Attack on Vision Transformer with Adaptive Token Tuning
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria
Community Exploration: From Offline Optimization to Online Learning
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment
Cascade Speculative Drafting for Even Faster LLM Inference
SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models
Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler
Learning Optical Flow from Continuous Spike Streams
Kohonen Networks and Clustering
The VC-Dimension versus the Statistical Capacity of Multilayer Networks
Geometric Analysis of Matrix Sensing over Graphs
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes
Estimating graphical models for count data with applications to single-cell gene network
Neighborhood Reconstructing Autoencoders
An in-silico Neural Model of Dynamic Routing through Neuronal Coherence
Human-AI Collaborative Bayesian Optimisation
Recurrent Ladder Networks
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
AP-Adapter: Improving Generalization of Automatic Prompts on Unseen Text-to-Image Diffusion Models
A Neural Model of Delusions and Hallucinations in Schizophrenia
Wings: Learning Multimodal LLMs without Text-only Forgetting
Self-Learning Transformations for Improving Gaze and Head Redirection
Combining causal and similarity-based reasoning
Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data
Spectral Editing of Activations for Large Language Model Alignment
Winner-Take-All Autoencoders
NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization
Second Order Properties of Error Surfaces
Large Scale Distributed Deep Networks
Automatic Unsupervised Outlier Model Selection
Design Principles of the Hippocampal Cognitive Map
Machine versus Human Attention in Deep Reinforcement Learning Tasks
Sparse Inverse Covariance Estimation with Calibration
Non-parametric Structured Output Networks
Variational Planning for Graph-based MDPs
Learning discrete distributions with infinite support
Secretary Ranking with Minimal Inversions
Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
A polar prediction model for learning to represent visual transformations
Sim2real transfer learning for 3D human pose estimation: motion to the rescue
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
Decision-Making with Auto-Encoding Variational Bayes
Learning Auto-regressive Models from Sequence and Non-sequence Data