28 citations · 120 across the 23 of their papers we have counts for
25 papers
On the Regularization Landscape for the Linear Recommendation Models
Dong Li, Zhenming Liu, Ruoming Jin +4
Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders on several standard recommendation benchmarks. Whil…
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models
Qiong Wu, Jian Li, Zhenming Liu +2
This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or int…
Towards Reliable Item Sampling for Recommendation Evaluation
Dong Li, Ruoming Jin, Zhenming Liu +3
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…
Toward Efficient Interactions between Python and Native Libraries
Jialiang Tan, Yu Chen, Zhenming Liu +4
Python has become a popular programming language because of its excellent programmability. Many modern software packages utilize Python for high-level algorithm design and depend o…
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search
Adam Hare, Yu Chen, Yinan Liu +2
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…
RackSched: A Microsecond-Scale Scheduler for Rack-Scale Computers (Technical Report)
Hang Zhu, Kostis Kaffes, Zixu Chen +4
Low-latency online services have strict Service Level Objectives (SLOs) that require datacenter systems to support high throughput at microsecond-scale tail latency. Dataplane oper…
Rosella: A Self-Driving Distributed Scheduler for Heterogeneous Clusters
Qiong Wu, Zhenming Liu
Large-scale interactive web services and advanced AI applications make sophisticated decisions in real-time, based on executing a massive amount of computation tasks on thousands o…
On Efficient Constructions of Checkpoints
Yu Chen, Zhenming Liu, Bin Ren +1
Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models. In this paper, we propose a lossy compression scheme for checkp…
BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation
Qiong Wu, Adam Hare, Sirui Wang +4
Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are avail…
Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing
Qiong Wu, Christopher G. Brinton, Zheng Zhang +3
Pricing assets has attracted significant attention from the financial technology community. We observe that the existing solutions overlook the cross-sectional effects and not full…
Reward Advancement: Transforming Policy under Maximum Causal Entropy Principle
Guojun Wu, Yanhua Li, Zhenming Liu +4
Many real-world human behaviors can be characterized as a sequential decision making processes, such as urban travelers choices of transport modes and routes (Wu et al. 2017). Diff…
Adaptive Reduced Rank Regression
Qiong Wu, Felix Ming Fai Wong, Zhenming Liu +2
We study the low rank regression problem $\my = M\mx + ε$, where $\mx$ and $\my$ are and dimensional vectors respectively. We consider the extreme high-dimensional sett…
DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching
Zaoxing Liu, Zhihao Bai, Zhenming Liu +5
Load balancing is critical for distributed storage to meet strict service-level objectives (SLOs). It has been shown that a fast cache can guarantee load balancing for a clustered…
Towards Non-Parametric Learning to Rank
Ao Liu, Qiong Wu, Zhenming Liu +1
This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with…
From which world is your graph?
Cheng Li, Felix Wong, Zhenming Liu +1
Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference alg…
Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization
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 th…
From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments
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.…
Mind Your Own Bandwidth: An Edge Solution to Peak-hour Broadband Congestion
Felix Ming Fai Wong, Carlee Joe-Wong, Sangtae Ha +2
Motivated by recent increases in network traffic, we propose a decentralized network edge-based solution to peak-hour broadband congestion that incentivizes users to moderate their…
Learning about social learning in MOOCs: From statistical analysis to generative model
Christopher G. Brinton, Mung Chiang, Shaili Jain +3
We study user behavior in the courses offered by a major Massive Online Open Course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable ed…
Statistically-secure ORAM with Overhead
Kai-Min Chung, Zhenming Liu, Rafael Pass
We demonstrate a simple, statistically secure, ORAM with computational overhead ; previous ORAM protocols achieve only computational security (under computatio…
Distributed Non-Stochastic Experts
Varun Kanade, Zhenming Liu, Bozidar Radunovic
We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to sites, and the sites are r…
The Diffusion of Networking Technologies
Sharon Goldberg, Zhenming Liu
There has been significant interest in the networking community on the impact of cascade effects on the diffusion of networking technology upgrades in the Internet. Thinking of the…
Chernoff-Hoeffding Bounds for Markov Chains: Generalized and Simplified
Kai-Min Chung, Henry Lam, Zhenming Liu +1
We prove the first Chernoff-Hoeffding bounds for general nonreversible finite-state Markov chains based on the standard L_1 (variation distance) mixing-time of the chain. Specifica…
Information Dissemination via Random Walks in d-Dimensional Space
Henry Lam, Zhenming Liu, Michael Mitzenmacher +2
We study a natural information dissemination problem for multiple mobile agents in a bounded Euclidean space. Agents are placed uniformly at random in the -dimensional space $\{…