Search Results for author:
Found 35 papers, 11 papers with code
A Research Framework for Understanding Education-Occupation Alignment with NLP Techniques
Understanding the gaps between job requirements and university curricula is crucial for improving student success and institutional effectiveness in higher education.
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers
Evaluation of large language models (LLMs) for code has primarily relied on static benchmarks, including HumanEval (Chen et al., 2021), which measure the ability of LLMs to generate complete code that passes unit tests.
Thermometer: Towards Universal Calibration for Large Language Models
Addressing these challenges, we propose THERMOMETER, a calibration approach tailored to LLMs.
Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions
This paper explores a modern predictive uncertainty estimation approach, called evidential deep learning (EDL), in which a single neural network model is trained to learn a meta distribution over the predictive distribution by minimizing a specific objective function.
One step closer to unbiased aleatoric uncertainty estimation
Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making.
Correlated Attention in Transformers for Multivariate Time Series
Multivariate time series (MTS) analysis prevails in real-world applications such as finance, climate science and healthcare.
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
In this work, we propose to learn rules, grounded in data regions and described in natural language, that illustrate how the human should collaborate with the AI.
Non-asymptotic System Identification for Linear Systems with Nonlinear Policies
This paper considers a single-trajectory system identification problem for linear systems under general nonlinear and/or time-varying policies with i. i. d.
Reliable Gradient-free and Likelihood-free Prompt Tuning
Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs.
Label-Free Concept Bottleneck Models
Motivated by these challenges, we propose Label-free CBM which is a novel framework to transform any neural network into an interpretable CBM without labeled concept data, while retaining a high accuracy.
Variance-reduced Clipping for Non-convex Optimization
This is in clear contrast to the well-established assumption in folklore non-convex optimization, a. k. a.
Group Fairness with Uncertainty in Sensitive Attributes
To overcome this limitation, we propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes.
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws.
Who Should Predict? Exact Algorithms For Learning to Defer to Humans
We show that prior approaches can fail to find a human-AI system with low misclassification error even when there exists a linear classifier and rejector that have zero error (the realizable setting).
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model
It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures.
On Convergence of Gradient Descent Ascent: A Tight Local Analysis
In this paper, we aim to bridge this gap by analyzing the \emph{local convergence} of general \emph{nonconvex-nonconcave} minimax problems.
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics
Rapid technological innovation threatens to leave much of the global workforce behind.
An alternative approach for distributed parameter estimation under Gaussian settings
This paper takes a different approach for the distributed linear parameter estimation over a multi-agent network.
On observability and optimal gain design for distributed linear filtering and prediction
This paper presents a new approach to distributed linear filtering and prediction.
Attacking c-MARL More Effectively: A Data Driven Approach
In recent years, a proliferation of methods were developed for cooperative multi-agent reinforcement learning (c-MARL).
Safe Adaptive Learning-based Control for Constrained Linear Quadratic Regulators with Regret Guarantees
We study the adaptive control of an unknown linear system with a quadratic cost function subject to safety constraints on both the states and actions.
Selective Regression Under Fairness Criteria
Selective regression allows abstention from prediction if the confidence to make an accurate prediction is not sufficient.
Evaluating Robustness of Cooperative MARL
In recent years, a proliferation of methods were developed for multi-agent reinforcement learning (MARL).
Tactics on Refining Decision Boundary for Improving Certification-based Robust Training
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation.
Stochastic Optimization with Non-stationary Noise: The Power of Moment Estimation
In particular, standard results on optimal convergence rates for stochastic optimization assume either there exists a uniform bound on the moments of the gradient noise, or that the noise decays as the algorithm progresses.
Online Optimal Control with Affine Constraints
We show that OGD-BZ can achieve a policy regret upper bound that is the square root of the horizon length multiplied by some logarithmic terms of the horizon length under proper algorithm parameters.
Verifiably Safe Exploration for End-to-End Reinforcement Learning
We also prove that our method of enforcing the safety constraints preserves all safe policies from the original environment.
A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm
Out of the recent advances in systems and control (S\&C)-based analysis of optimization algorithms, not enough work has been specifically dedicated to machine learning (ML) algorithms and its applications.
GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models
Motivated by optimal transport theory, we design the zero-sum game in GAT-GMM using a random linear generator and a softmax-based quadratic discriminator architecture, which leads to a non-convex concave minimax optimization problem.
Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges
Autonomous systems -- such as self-driving cars, autonomous drones, and automated trains -- must come with strong safety guarantees.
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity
We study oracle complexity of gradient based methods for stochastic approximation problems.
Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift
In this paper, we consider the scenario of a gradual concept drift due to the underlying non-stationarity of the data source.
Learning Occupational Task-Shares Dynamics for the Future of Work
The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment.
Learning Patient Engagement in Care Management: Performance vs. Interpretability
In this paper, we present a real world data-driven method and the behavioral engagement scoring pipeline for scoring the engagement level of a patient in two regards: (1) Their interest in enrolling into a relevant care program, and (2) their interest and commitment to program goals.
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States.
