ML Seminars (Spring 2017)

  • Amir Globerson. Tel Aviv University
          Efficient Optimization of a Convolutional Network with Gaussian Inputs
          1st March, 2017; 5pm-6pm   32-G643
  • Mehryar Mohri. Courant Institute, NYU
          Online Learning for Time Series Prediction
          8th March, 2017; 4pm-5pm   32-G463
  • Lester Mackey. Microsoft Research
          TBD
          15 March, 2017; 4pm-5pm   32-G463
  • Ben Recht. UC Berkeley
          Optimization Challenges in Deep Learning
          22 March, 2017; 4pm-5pm   32-G463
  • Ruslan Salakhutdinov Carnegie Mellon University, Pittsburgh, PA
          Learning Deep Unsupervised and Multimodal Models
          05th Apr, 2016; 4pm-5pm   34-101
  • Jeff Miller. Harvard University, Cambridge
          TBD
          26th Apr, 2017; 4pm-5pm   32-G463
  • Ryan Adams. Harvard University and Google Brain
          Building Probabilistic Structure into Massively Parameterized Models
          10th May, 2017; 4pm-5pm   32-141

ML Seminars (Fall 2016)

  • Honglak Lee University of Michigan, Ann Arbor
          Deep architectures for visual reasoning, multimodal learning, and decision-making
          16th Nov, 2016; 4pm-5pm   32-G463
  • Elad Hazan Princeton University
          A Non-generative Framework and Convex Relaxations for Unsupervised Learning
          26th Oct, 2016; 4pm-5pm   32-G463
  • Tina Eliassi-Rad (Northeastern)
          The Reasonable Effectiveness of Roles in Complex Networks
          19th Oct, 2016;   32-G575
  • Carlo Morselli School of Criminology, University of Montreal
          Criminal Networks
          29th Sep, 2016; 4pm-5pm   4-237
  • Gah-Yi Vahn (LSB)
          The data-driven (s, S) policy: why you can have confidence in censored demand data
          5th Oct, 2016; 4:00 PM to 5:00 PM   32-G575
  • Le Song (Georgia Tech).
          Discriminative Embedding of Latent Variable Models for Structured Data
          16th Sep, 2016; 2pm-3pm   32-G882
  • Ashish Kapoor (MSR Redmond).
          Safe Decision Making Under Uncertainty
          14th Sep, 2016; 4pm-5pm   32-D507
  • Alan Malek (UC Berkeley).
          Minimax strategies for online linear regression, square-loss prediction, and time series prediction
          15th Aug, 2016; 11am   32-D677
  • Sashank Reddi (CMU).
          Faster Stochastic Methods for Nonconvex Optimization in Machine Learning
          13th July, 2016; 3pm   32-G882
  • Andre Wibisono (UC Berkeley).
          A variational perspective on accelerated methods in optimization
          14th July, 2016; 3pm   32-G882