Search Results for author:
Found 13 papers, 3 papers with code
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback.
AutoHint: Automatic Prompt Optimization with Hint Generation
This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM).
DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries
For model-free enhancement, we collect unlabeled web queries to augment domain knowledge; and we collect web search results to enrich the information of ads queries.
Efficient Long Sequence Modeling via State Space Augmented Transformer
Specifically, we augment a SSM into the bottom layer of SPADE, and we employ efficient local attention methods for the other layers.
TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantification
In this paper, we propose TEDL, a two-stage learning approach to quantify uncertainty for deep learning models in classification tasks, inspired by our findings in experimenting with Evidential Deep Learning (EDL) method, a recently proposed uncertainty quantification approach based on the Dempster-Shafer theory.
Masked LARk: Masked Learning, Aggregation and Reporting worKflow
We introduce a secure multi-party compute (MPC) protocol that utilizes "helper" parties to train models, so that once data leaves the browser, no downstream system can individually construct a complete picture of the user activity.
Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction
We describe a causal transfer random forest (CTRF) that combines existing training data with a small amount of data from a randomized experiment to train a model which is robust to the feature shifts and therefore transfers to a new targeting distribution.
Causal Inference in the Presence of Interference in Sponsored Search Advertising
This assumption is violated in settings where units are related through a network of dependencies.
Self-Supervised Contextual Bandits in Computer Vision
In the usual self-supervision, we learn implicit labels from the training data for a secondary task.
Data Transformation Insights in Self-supervision with Clustering Tasks
We show faster convergence rate with valid transformations for convex as well as certain family of non-convex objectives along with the proof of convergence to the original set of optima.
A Unified Batch Online Learning Framework for Click Prediction
Through extensive experiments, we demonstrate the utility of of our OL framework; how the two OL schemes relate to each other and how they trade-off between the new and historical data.
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models.
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Based on the machine's output, the teacher can revise the definition of the task or make it more precise.
