MLconf Seattle 2015
| title | author | length |
|---|---|---|
| Joseph K. Bradley, Software Engineer, Databricks Inc. @ MLconf SEA | Joseph K. Bradley | 24m |
Spark exposes a distributed DataFrame + Catalyst optimizer API (Python/Java/Scala, R coming) that runs the same code on laptop or cluster and beats old RDD… | ||
| Robert Moakler, Data Science Intern, Integral Ad Science @ MLconf SEA | Robert Moakler | 19m |
Uses viewability (ad >50% in view for ≥1s) as a natural experiment: users with viewable impressions form treatment, non‑viewable impressions form control… | ||
| Josh Wills, Director of Data Science, Cloudera @ MLconf SEA | Josh Wills | 27m |
Argues that feature engineering needs an IDE and shows a working prototype: the Exhibit project layers a Supernova schema (one record per entity with arrays of… | ||
| Ray Richardson, Chief Technology Officer at Simularity @ MLconf SEA | Ray Richardson | 25m |
Technique converts time-series windows to SAX words, running on tiny devices including a 2K RAM prototype, and computes anomaly scores with simple metrics (p… | ||
| Sergey A. Razin Ph.D., Chief Technology Officer, SIOS Technology @ MLconf SEA | Sergey A. Razin Ph.D. | 24m |
Claim: machine learning and graph theory power a platform called Semantic IO that finds root causes in complex virtual clouds. It ingests many monitoring tools… | ||
| Ivy Zhu, Research Scientist, Intel @ MLconf SEA | Ivy Zhu | 21m |
Presents a concrete algorithmic approach: a Bayesian hierarchical topographical factor analysis that models whole-brain images with latent factors instead of… | ||
| Shiva Amiri, Chief Product Officer, RTDS Inc. @ MLconf SEA | Shiva Amiri | 17m |
RTDS presents SymetryML, a distributed GPU predictive analytics engine (v3.5) claiming zero latency machine learning via a compact in-memory representation… | ||
| Mark Zangari, CEO, Quantellia @ MLconf SEA | Mark Zangari | 33m |
Presents Agency Theory — a math framework that uses entropy-style measures to find which levers and datapoints actually change outcomes, claiming organizations… | ||
| Ewa Dominowska, Engineering Manager, Facebook @ MLconf SEA | Ewa Dominowska | 26m |
Facebook describes building an ads ML stack that selects from over a billion ads for over a billion people, splitting the problem into information retrieval… | ||
| Ehtsham Elahi, Senior Research Engineer, Netflix @ MLconf SEA | Ehtsham Elahi | 18m |
Netflix applies Spark and GraphX to run large-scale topic-sensitive PageRank and distributed LDA, representing per-vertex activations as vectors and sampling… | ||
| Carlos Guestrin, CEO of Dato Inc. @ MLconf SEA | Carlos Guestrin | 17m |
Shows that a columnar SFrame compresses real datasets far smaller than gzip and lets 931 million-row, 950-column biology tables run aggregations in ~2 seconds… | ||
| Anthony Bak, Principal Data Scientist at Ayasdi @ MLconf SEA | Anthony Bak | 28m |
Topology (Ayasdi style TDA) builds a combinatorial summary by taking a real-valued lens, forming an overlapping interval cover, clustering each inverse image… | ||
| Animashree Anandkumar, Electrical Engineering and CS Dept, UC Irvine @ MLconf SEA | Animashree Anandkumar | 33m |
Tensor methods claim consistent parameter recovery for topic models, community/mixed‑membership models, HMMs and ICA by decomposing third-order moments instead… | ||
| Xavier Amatriain, VP of Engineering, Quora @ MLconf SEA | Xavier Amatriain | 25m |
Quora builds concrete ML systems: an answer-ranking using learning-to-rank with LambdaMART that emphasizes text quality and user expertise (currently… | ||
| Misha Bilenko, Principal Researcher, Microsoft @ MLconf SEA | Misha Bilenko | 44m |
Many Shades of Scale: Big Learning Beyond Big Data:
In the machine learning research community, much of the attention devoted to ‘big data’ in recent years… | ||
| nothing matches | ||