Search Results for author: Zhenming Liu

Found 22 papers, 5 papers with code

Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation

no code implementations10 Apr 2024 Ningfeng Liu, Jie Yu, Siyu Xiu, Xinfang Zhao, Siyu Lin, Bo Qiang, Ruqiu Zheng, Hongwei Jin, Liangren Zhang, Zhenming Liu

Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology.

Drug Discovery

Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models

no code implementations1 Dec 2022 Qiong Wu, Jian Li, Zhenming Liu, Yanhua Li, Mihai Cucuringu

This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or interactions between users in a social network.

Ensemble Learning Time Series Analysis

Towards Reliable Item Sampling for Recommendation Evaluation

no code implementations28 Nov 2022 Dong Li, Ruoming Jin, Zhenming Liu, Bin Ren, Jing Gao, Zhi Liu

Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are "inconsistent" with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation.

Recommendation Systems

On the regularization landscape for the linear recommendation models

no code implementations29 Sep 2021 Dong Li, Zhenming Liu, Ruoming Jin, Zhi Liu, Jing Gao, Bin Ren

Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders several standard recommendation benchmarks.

Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow

no code implementations29 Sep 2021 Xin Zhang, Yanhua Li, Ziming Zhang, Christopher Brinton, Zhenming Liu, Zhi-Li Zhang, Hui Lu, Zhihong Tian

State-of-the-art imitation learning (IL) approaches, e. g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse.

Denoising Imitation Learning

On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search

1 code implementation2 Dec 2020 Adam Hare, Yu Chen, Yinan Liu, Zhenming Liu, Christopher G. Brinton

Despite the recent successes of deep learning in natural language processing (NLP), there remains widespread usage of and demand for techniques that do not rely on machine learning.

BIG-bench Machine Learning Sentence +1

Rosella: A Self-Driving Distributed Scheduler for Heterogeneous Clusters

no code implementations28 Oct 2020 Qiong Wu, Zhenming Liu

We evaluate Rosella with a variety of workloads on a 32-node AWS cluster.


On Efficient Constructions of Checkpoints

no code implementations ICML 2020 Yu Chen, Zhenming Liu, Bin Ren, Xin Jin

Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models.


BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation

no code implementations5 Aug 2020 Qiong Wu, Adam Hare, Sirui Wang, Yuwei Tu, Zhenming Liu, Christopher G. Brinton, Yanhua Li

In this work, we reexamine the inter-related problems of "topic identification" and "text segmentation" for sparse document learning, when there is a single new text of interest.

Segmentation Text Segmentation +1

DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning

no code implementations20 Aug 2019 Yibo Li, Jianxing Hu, Yanxing Wang, Jielong Zhou, Liangren Zhang, Zhenming Liu

Furthermore, the generated compounds were evaluated by molecular docking in DRD2 targets and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.

Drug Discovery Molecular Docking

Reward Advancement: Transforming Policy under Maximum Causal Entropy Principle

no code implementations11 Jul 2019 Guojun Wu, Yanhua Li, Zhenming Liu, Jie Bao, Yu Zheng, Jieping Ye, Jun Luo

In this paper, we define and investigate a general reward trans-formation problem (namely, reward advancement): Recovering the range of additional reward functions that transform the agent's policy from original policy to a predefined target policy under MCE principle.

Decision Making

Adaptive Reduced Rank Regression

1 code implementation NeurIPS 2020 Qiong Wu, Felix Ming Fai Wong, Zhenming Liu, Yanhua Li, Varun Kanade

We study the low rank regression problem $\my = M\mx + \epsilon$, where $\mx$ and $\my$ are $d_1$ and $d_2$ dimensional vectors respectively.


Towards Non-Parametric Learning to Rank

no code implementations9 Jul 2018 Ao Liu, Qiong Wu, Zhenming Liu, Lirong Xia

Next, we fix the problem by introducing a new algorithm with features constructed from "global information" of the data matrix.

Feature Engineering Learning-To-Rank

Multi-Objective De Novo Drug Design with Conditional Graph Generative Model

1 code implementation18 Jan 2018 Yibo Li, Liangren Zhang, Zhenming Liu

Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design.


From which world is your graph

no code implementations NeurIPS 2017 Cheng Li, Felix Mf Wong, Zhenming Liu, Varun Kanade

This work focuses on unifying two of the most widely used link-formation models: the stochastic block model (SBM) and the small world (or latent space) model (SWM).

Dimensionality Reduction Position +1

From which world is your graph?

no code implementations3 Nov 2017 Cheng Li, Felix Wong, Zhenming Liu, Varun Kanade

Discovering statistical structure from links is a fundamental problem in the analysis of social networks.

Dimensionality Reduction Position

Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization

no code implementations27 Jun 2014 Felix Ming Fai Wong, Zhenming Liu, Mung Chiang

We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day.

Stock Market Prediction

From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments

no code implementations23 Jun 2014 Henry Lam, Zhenming Liu

We consider a non-stochastic online learning approach to price financial options by modeling the market dynamic as a repeated game between the nature (adversary) and the investor.

Distributed Non-Stochastic Experts

no code implementations NeurIPS 2012 Varun Kanade, Zhenming Liu, Bozidar Radunovic

This paper shows the difficulty of simultaneously achieving regret asymptotically better than \sqrt{kT} and communication better than T. We give a novel algorithm that for an oblivious adversary achieves a non-trivial trade-off: regret O(\sqrt{k^{5(1+\epsilon)/6} T}) and communication O(T/k^\epsilon), for any value of \epsilon in (0, 1/5).

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