Optimization is ubiquitous. While derivative-based algorithms have been
...
Pre-trained language models like ChatGPT have significantly improved cod...
Recent research shows the potential of enhancing the problem-solving abi...
The explosive growth of language models and their applications have led ...
We present symbol tuning - finetuning language models on in-context
inpu...
Large language models (LLMs) have achieved impressive performance on cod...
We propose and release a new vulnerable source code dataset. We curate t...
We study how in-context learning (ICL) in language models is affected by...
We investigate an optimization problem in a queueing system where the se...
Large language models have achieved impressive performance on various na...
Motivated by the emerging needs of personalized preventative interventio...
Pre-trained language models have demonstrated impressive performance in ...
Humans can reason compositionally when presented with new tasks. Previou...
In text-to-SQL tasks – as in much of NLP – compositional generalization ...
Programming is a powerful and ubiquitous problem-solving tool. Developin...
Long Short-Term Memory (LSTM) and Transformers are two popular neural
ar...
Knowledge graphs (KGs) capture knowledge in the form of head–relation–ta...
Deep neural networks (DNNs) are vulnerable to adversarial noises, which
...
Recently, there has been significant progress in studying neural network...
Addressing the mismatch between natural language descriptions and the
co...
Knowledge bases are prevalent in various domains and have been widely us...
Program synthesis from input-output examples has been a long-standing
ch...
Spreadsheet formula prediction has been an important program synthesis
p...
Since the first coronavirus case was identified in the U.S. on Jan. 21, ...
Recently, there has been significant progress in studying neural network...
We collaborate with a large teaching hospital in Shenzhen, China and bui...
Adversarial examples have appeared as a ubiquitous property of machine
l...
As machine learning systems grow in scale, so do their training data
req...
There is now extensive evidence demonstrating that deep neural networks ...
We study a dynamic pricing and capacity sizing problem in a GI/GI/1 queu...
Despite achieving tremendous success, existing deep learning models have...
The use of deep learning techniques has achieved significant progress fo...
As an extension of self-exciting Hawkes process, the multivariate Hawkes...
Adversarial attacks are valuable for providing insights into the blind-s...
Deep neural networks (DNNs) have achieved tremendous success in various
...
We consider off-policy policy evaluation when the trajectory data are
ge...
Reverse engineering of binary executables is a critical problem in the
c...
For problem solving, making reactive decisions based on problem descript...
Program translation is an important tool to migrate legacy code in one
l...
Deep learning models have achieved high performance on many tasks, and t...
Deep learning has achieved impressive results in many areas of Computer
...
In this work, we study an important problem: learning programs from
inpu...
Automatic translation from natural language descriptions into programs i...
Traditional classification algorithms assume that training and test data...