Optimization
Relational Verification Leaps Forward with RABBit
Code is at this URL.
Randomized Sparse Matrix Compression for Large-Scale Constrained Optimization in Cancer Radiotherapy
In 2020, an estimated 18.1 million new cancer cases and about 9.9 million cancer-related deaths were reported globally [
GLinSA T: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent
Constrained decision-making problems are pervasive across various disciplines.
Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits
SOI by directly leveraging human feedback without being restricted by a predefined reward model nor cumbersome model selection.
Accelerating Non-Maximum Suppression: A Graph Theory Perspective King-Siong Si1* Lu Sun
Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the "last mile" to enhance the efficiency of object detection.
Optimal Multi-Fidelity Best-Arm Identification
Work done while at Inria, Lille, France.
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning Tong Y ang
We further propose a federated natural actor critic (NAC) method for multi-task RL with function approximation and stochastic policy evaluation, and establish its finite-time sample complexity taking the errors of function approximation into account.
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization Xiangxin Zhou
Antibody design, a crucial task with significant implications across various disciplines such as therapeutics and biology, presents considerable challenges due to its intricate nature. In this paper, we tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences, considering both rationality and functionality.
