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Machine Learning and Friends Lunch

About

This semester of the UMass Machine Learning and Friends Lunch (MLFL) series has been graciously sponsored by our friends at Oracle Labs. MLFL is a lively and interactive forum held weekly where friends of the UMass Amherst machine learning community can sit down, have lunch, and give or hear a 50-minute presentation on recent machine learning research.

What is it?   A gathering of students/faculty/staff with broad interest in the methods and applications of machine learning.
When is it?    Thursdays 12:00pm to 1:00pm, unless otherwise noted. Arrive at 11:45 to get pizza.
Where is it?    CS150
Who is invited?   Everyone is welcome.
Is there food?    Yes! Pizza is provided.
Can I present?    Yes! If you would like to present your research, please email one of the organizers: Ari Kobren, Rajarshi Das, Li Yang Ku, Hang Su, Samer Nashed and Aruni Roy Chowdhury
Who generously sponsors this regular event   Oracle Labs  

 

Suggestions, comments, want to present? Contact us at  mlfl@ds.cs.umass.edu.

 

Recent Talks

April 26, 2018 Robot Perception For Manipulation - Peter Yu
April 19, 2018 Bridging Probabilistic Inference And Motion Planning With Markov Chain Monte Carlo - Daqing Yi
April 10, 2018 Insights from Deep Representations - Maithra Raghu
March 29, 2018 Exponential Family Embeddings - Maja Rudolph
March 22, 2018 New and Old Concentration Inequalities - Philip Thomas
March 8, 2018 CANCELLED - Jerry Li
March 1, 2018 Yulia Tsvetkov
February 22, 2018 Joseph Tassarotti - Hashing and Sketching for Latent Dirichlet-Categorical Models
February 15, 2018 Adji Dieng - Deep Sequence Models: Context Representation, Regularization, and Application to Language.
February 8, 2018 Bharath Hariharan - Visual recognition beyond large labeled training sets
February 1, 2018 Graham Neubig - What Can Neural Networks Teach us about Language?
November 30, 2017 Rajesh Ranganath - Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications
November 20, 2017 Claudia Pérez D’Arpino - Learning How to Plan for Multi-Step Manipulation in Collaborative Robotics
November 16, 2017 Steven Wu - A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
November 9, 2017 What Makes a Good Argument? Understanding and Predicting High Quality Arguments Using NLP Methods
November 2, 2017 Beomjoon Kim - Learning to Guide Task and Motion Planning by Predicting Constraints
October 26, 2017 Claudia Pérez D'Arpino
October 12, 2017 Varun Jampani - Bilateral Neural Networks for Image,Video and 3D Vision
October 5, 2017 Francesco Orabona - Coin Betting For Backprop Without Learning Rates And More
September 28, 2017 Nika Haghtalab - Oracle-efficient Online Learning And Applications To Auction Design
September 21, 2017 Yogarshi Vyas - Detecting Asymmetric Semantic Relations in Context : A Case-Study on Hypernymy Detection
September 14, 2017 Pat Flaherty - A Nonparametric Bayesian Model for Single-cell Variant Calling
September 7, 2017 Dean Eckles

2016/2017 Academic Year

June 13, 2017 Qiang Liu - A Stein Variational Framework for Deep Probabilistic Modeling
May 4, 2017 Scott W. Linderman - Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems
April 26, 2017 Tom Williams - Genuine Helpers: Enabling Natural Language Capabilities for Interactive Robots
April 20, 2017 Hari Balasubramanian - Models Based on Longitudinal Healthcare Event Data
April 13, 2017 Sainbayar Sukhbaatar - Intrinsic Motivation And Automatic Curricula Via Asymmetric Self-Play
April 6, 2017 Robert Kozma - Computational Aspects of Brain Dynamics: Experiments, Models, and New AI Approaches
March 30, 2017 Mikael Henaff - Tracking The World State With Recurrent Entity Networks
March 23, 2017 David Moorman - Making Sense of Neuron Ensembles: Advances and Issues in Neural Coding
February 23, 2017 Andrew Trapp - Using Density To Identify Fixations In Gaze Data Optimization-Based Formulations And Algorithms
February 16, 2017 Naomi Fitter - Exploring Human-Inspired Haptic Interaction Skills For Robots
February 14, 2017 Tsendsuren Munkhdalai - Language Understanding And Reasoning With Memory Augmented Neural Networks
February 9, 2017 Hao Tang - Sequence Prediction With Neural Segmental Models
February 2, 2017 Stephan Mandt - Advances In Scalable Probabilistic Modeling Theory Applications And Challenges
January 19, 2017 Jeff Flanigan - Parsing And Generation For The Abstract Meaning Representation
December 8, 2016 John Lalor - Building Evaluation Scales For NLP Using Item Response Theory
December 1, 2016 Jayant Krishnamurthy - Semantic Parsing To Probabilistic Programs For Situated Question Answering
November 30, 2016 Mohit Iyyer, University of Maryland
November 30, 2016 Pegram Rooshenas - Learning Tractable Graphical Models
November 17, 2016 Andreas ten Pas - Grasp Pose Detection In Dense Clutter
November 10, 2016 Evan Shelhamer - A Fuller Understanding Of Fully Convolutional Networks
November 10, 2016 Samantha Kleinberg - Causal Inference and Explanation to Improve Human Health
November 3, 2016 Tianan Xue - Visual Dynamics Probabilistic Future Frame Synthesis Via Cross Convolutional Networks
October 27, 2016 Chris Kedzie, Real-Time Web Scale Event Summarization Using Sequential Decision Making
October 20, 2016 Jiajun Wu - Computational Perception of Physical Object Properties
October 13, 2016 Hugo Larochelle - Fighting our Big Data Addiction with Representation Learning
October 11, 2016 Chao Chen - Topological Analysis of Modern Data
September 22, 2016 Thien Nyugen - Neural Information Extraction with Memory
September 16, 2016 Siva Reddy - Freebase Semantic Parsing With And Without QA Pairs
September 15, 2016 Antoine Bordes - Reasoning With Memory Networks Successes And Challenges

 

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