Publications (17)
Measuring Faithfulness in Chain-of-Thought Reasoning
Tamera Lanham, Anna Chen, Ansh Radhakrishnan +27
Large language models (LLMs) perform better when they produce step-by-step, "Chain-of-Thought" (CoT) reasoning before answering a question, but it is unclear if the stated reasonin…
Learning Space Partitions for Path Planning
Kevin Yang, Tianjun Zhang, Chris Cummins +6
Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function. Popular approaches like…
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
Taoan Huang, Aaron Ferber, Yuandong Tian +2
Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems. Recently, it has been shown that Large Neighborhoo…
Value Function Based Performance Optimization of Deep Learning Workloads
Benoit Steiner, Chris Cummins, Horace He +1
As machine learning techniques become ubiquitous, the efficiency of neural network implementations is becoming correspondingly paramount. Frameworks, such as Halide and TVM, separa…
Local Branching Relaxation Heuristics for Integer Linear Programs
Taoan Huang, Aaron Ferber, Yuandong Tian +2
Large Neighborhood Search (LNS) is a popular heuristic algorithm for solving combinatorial optimization problems (COP). It starts with an initial solution to the problem and iterat…
Device Placement Optimization with Reinforcement Learning
Azalia Mirhoseini, Hieu Pham, Quoc V. Le +7
The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these req…