Search Results for author:
Found 30 papers, 19 papers with code
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models
We develop AcTune, a new framework that improves the label efficiency of active PLM fine-tuning by unleashing the power of unlabeled data via self-training.
What is the Right Notion of Distance between Predict-then-Optimize Tasks?
Comparing datasets is a fundamental task in machine learning, essential for various learning paradigms; from evaluating train and test datasets for model generalization to using dataset similarity for detecting data drift.
DeepTTV: Deep Learning Prediction of Hidden Exoplanet From Transit Timing Variations
Transit timing variation (TTV) provides rich information about the mass and orbital properties of exoplanets, which are often obtained by solving an inverse problem via Markov Chain Monte Carlo (MCMC).
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards
In the presence of multiple agents, altering the reward function based on human preferences can impact subpopulations very differently, leading to complex tradeoffs and a multi-objective resource allocation problem.
Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting
Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time series forecasting.
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable.
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
In this paper, we aim to alleviate the inherent OOD problem in TSF via invariant learning.
Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis
To overcome this obstacle, we introduce Time-MMD, the first multi-domain, multimodal time series dataset covering 9 primary data domains.
Aligning Large Language Models with Representation Editing: A Control Perspective
To achieve alignment for specific objectives, we introduce external control signals into the state space of this language dynamical system.
Quantitative Convergences of Lie Group Momentum Optimizers
Explicit, momentum-based dynamics that optimize functions defined on Lie groups can be constructed via variational optimization and momentum trivialization.
Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups
However, our trivialization technique creates to a new momentum variable that stays in a simple $\textbf{fixed vector space}$.
Convergence of Kinetic Langevin Monte Carlo on Lie groups
Explicit, momentum-based dynamics for optimizing functions defined on Lie groups was recently constructed, based on techniques such as variational optimization and left trivialization.
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
The teacher LLM generates problem-solving instructions and corrective principles based on the student LLM's errors.
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
DF2: Distribution-Free Decision-Focused Learning
However, existing end-to-end DFL methods are hindered by three significant bottlenecks: model mismatch error, sample average approximation error, and gradient approximation error.
Autoregressive Diffusion Model for Graph Generation
However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian diffusion in the dequantized adjacency matrix space.
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
While some studies have included UQ to improve molecular pre-trained models, the process of selecting suitable backbone and UQ methods for reliable molecular uncertainty estimation remains underexplored.
DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling
Most existing methods for learning from noisy labels use static input features for denoising, but these methods are limited by the information they can provide on true label distributions and can result in biased or incorrect predictions.
AdaPlanner: Adaptive Planning from Feedback with Language Models
We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback.
End-to-End Stochastic Optimization with Energy-Based Model
Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters.
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport
The problem of optimization on Stiefel manifold, i. e., minimizing functions of (not necessarily square) matrices that satisfy orthogonality constraints, has been extensively studied.
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models
We propose {\ours}, a new framework that leverages unlabeled data to improve the label efficiency of active PLM fine-tuning.
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\% in accuracy and calibration.
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP, which directly models the probability density of the forecast value.
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization.
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
We propose a new method for quantifying uncertainties of DNNs from a dynamical system perspective.
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
This article suggests that deterministic Gradient Descent, which does not use any stochastic gradient approximation, can still exhibit stochastic behaviors.
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour.
