Search Results for author:
Found 27 papers, 14 papers with code
Teaching Large Language Models to Reason through Learning and Forgetting
Leveraging inference-time search in large language models has proven effective in further enhancing a trained model's capability to solve complex mathematical and reasoning problems.
Predicting Long Term Sequential Policy Value Using Softer Surrogates
In two simulated healthcare examples--HIV and sepsis management--we show that our estimators can provide accurate predictions about the policy value only after observing 10\% of the full horizon data.
Improving Parallel Program Performance with LLM Optimizers via Agent-System Interface
Modern scientific discovery increasingly relies on high-performance computing for complex modeling and simulation.
EVOLvE: Evaluating and Optimizing LLMs For Exploration
In this work, we measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications.
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
We investigate end-to-end generative optimization -- using generative models such as LLMs within the optimizer for automatic updating of general computational workflows.
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators
Evaluating a new policy online without a confident estimate of its performance can lead to costly, unsafe, or hazardous outcomes, especially in education and healthcare.
The Importance of Directional Feedback for LLM-based Optimizers
We study the potential of using large language models (LLMs) as an interactive optimizer for solving maximization problems in a text space using natural language and numerical feedback.
The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances
To help understand the impact of generic LLM use on coding education, we conducted a large-scale randomized control trial with 5, 831 students from 146 countries in an online coding class in which we provided some students with access to a chat interface with GPT-4.
LLF-Bench: Benchmark for Interactive Learning from Language Feedback
We introduce a new benchmark, LLF-Bench (Learning from Language Feedback Benchmark; pronounced as "elf-bench"), to evaluate the ability of AI agents to interactively learn from natural language feedback and instructions.
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
A rich literature in cognitive science has studied people's causal and moral intuitions.
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task
Resource limitations make it hard to provide all students with one of the most effective educational interventions: personalized instruction.
Deep Reinforcement Learning
Explainable artificial intelligence
+2
Model-based Offline Reinforcement Learning with Local Misspecification
We present a model-based offline reinforcement learning policy performance lower bound that explicitly captures dynamics model misspecification and distribution mismatch and we propose an empirical algorithm for optimal offline policy selection.
Giving Feedback on Interactive Student Programs with Meta-Exploration
However, teaching and giving feedback on such software is time-consuming -- standard approaches require instructors to manually grade student-implemented interactive programs.
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data
Inspired by statistical model selection methods for supervised learning, we introduce a task- and method-agnostic pipeline for automatically training, comparing, selecting, and deploying the best policy when the provided dataset is limited in size.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Play to Grade: Testing Coding Games as Classifying Markov Decision Process
Contemporary coding education often presents students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process
Contemporary coding education often present students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games.
Inducing Grammar from Long Short-Term Memory Networks by Shapley Decomposition
The principle of compositionality has deep roots in linguistics: the meaning of an expression is determined by its structure and the meanings of its constituents.
Pragmatic Issue-Sensitive Image Captioning
Image captioning systems have recently improved dramatically, but they still tend to produce captions that are insensitive to the communicative goals that captions should meet.
LitGen: Genetic Literature Recommendation Guided by Human Explanations
In collaboration with the Clinical Genomic Resource (ClinGen)---the flagship NIH program for clinical curation---we propose the first machine learning system, LitGen, that can retrieve papers for a particular variant and filter them by specific evidence types used by curators to assess for pathogenicity.
DisSent: Learning Sentence Representations from Explicit Discourse Relations
Learning effective representations of sentences is one of the core missions of natural language understanding.
Learning to Explain: Answering Why-Questions via Rephrasing
We demonstrate that our strategy is sufficient to generate highly plausible explanations for general open-domain phenomena compared to other models trained on different datasets.
Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding
We compare the performance of our model with several baselines in a challenging cross-hospital setting with substantial domain shift.
DeepTag: inferring all-cause diagnoses from clinical notes in under-resourced medical domain
However, clinicians lack the time and resource to annotate patient records with standard medical diagnostic codes and most veterinary visits are captured in free text notes.
From Information Bottleneck To Activation Norm Penalty
Recently, a regularization method has been proposed to optimize the variational lower bound of the Information Bottleneck Lagrangian.
DisSent: Sentence Representation Learning from Explicit Discourse Relations
Learning effective representations of sentences is one of the core missions of natural language understanding.
