6 papers
Kd-tree Based Wasserstein Distance Approximation for High-Dimensional Data
Kanata Teshigawara, Keisho Oh, Ken Kobayashi +1
The Wasserstein distance is a discrepancy measure between probability distributions, defined by an optimal transport problem. It has been used for various tasks such as retrieving…
Content-Aware Ad Banner Layout Generation with Two-Stage Chain-of-Thought in Vision Language Models
Kei Yoshitake, Kento Hosono, Ken Kobayashi +1
In this paper, we propose a method for generating layouts for image-based advertisements by leveraging a Vision-Language Model (VLM). Conventional advertisement layout techniques h…
Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification
Ryota Ueda, Takami Sato, Ken Kobayashi +1
Semidefinite programming (SDP) relaxation has emerged as a promising approach for neural network verification, offering tighter bounds than other convex relaxation methods for deep…
Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami +3
Off-Policy Evaluation (OPE) aims to assess the effectiveness of counterfactual policies using only offline logged data and is often used to identify the top-k promising policies fo…
SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation
Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami +3
This paper introduces SCOPE-RL, a comprehensive open-source Python software designed for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection (OP…
Unlocking Layer-wise Relevance Propagation for Autoencoders
Kenyu Kobayashi, Renata Khasanova, Arno Schneuwly +2
Autoencoders are a powerful and versatile tool often used for various problems such as anomaly detection, image processing and machine translation. However, their reconstructions a…