ML Seminars (Fall 2017)

ML Seminars typically happen on Wednesdays; exceptions are noted below.
  • Yisong Yue. (Caltech).
          The dueling bandits problem

           8th Sep, 2017. 2pm-3pm   Friday   32-G882

     Abstract:

    In this talk, I will present the Dueling Bandits Problem, which is an online learning framework tailored towards real-time learning from subjective human feedback. In particular, the Dueling Bandits Problem only requires pairwise comparisons, which are shown to be reliably inferred in a variety of subjective feedback settings such as for information retrieval an recommender systems. I will provide an overview of the Dueling Bandits Problem with basic algorithmic results. I will then conclude by discussing some ongoing research directions with applications to personalized medicine.
    This is joint work with Josef Broder, Bobby Kleinberg, Thorsten Joachims, Yanan Sui, Vincent Zhuang, and Joel Burdick.

      BIO

    Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for spatiotemporal reasoning, structured prediction, interactive learning systems, and learning with humans in the loop. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, policy learning in robotics, and adaptive routing & allocation problems.
  • Alex Smola. Amazon
          Sequence Modeling: From Spectral Methods and Bayesian Nonparametrics to Deep Learning

          11th Sep, 2017. 3pm-4pm Monday 32-G463

     Abstract:

    In this talk I will summarize a few recent developments in the design and analysis of sequence models. Starting with simple parametric models such as HMMs for sequences we look at nonparametric extensions in terms of their ability to model more fine-grained types of state and transition behavior. In particular we consider spectral embeddings, nonparametric Bayesian models such as the nested Chinese Restaurant Franchise and the Dirichlet-Hawkes Process. We conclude with a discussion of deep sequence models for user return time modeling, time-dependent collaborative filtering, and large-vocabulary user profiling.
    About the speaker: AWS Spotlight on Alex Smola
  • Noam Brown. CMU
          Libratus: Beating Top Humans in No-Limit Poker

          18th Sep, 2017. 3pm-54m   32-G449

     Abstract:

    Poker has been a challenge problem in AI and game theory for decades. As a game of imperfect information, poker involves obstacles not present in games like chess or Go. No program has been able to beat top professionals in large poker games, until now. In January 2017, our AI Libratus decisively defeated a team of the top professional players in heads-up no-limit Texas Hold'em. Libratus features a number of innovations which form a new approach to AI for imperfect-information games. The algorithms are domain-independent and can be applied to a variety of strategic interactions involving hidden information

    This talk is based on joint work with Tuomas Sandholm.

      BIO

    Noam Brown is a PhD student in computer science at Carnegie Mellon University advised by Professor Tuomas Sandholm. His research combines reinforcement learning and game theory to develop AIs capable of strategic reasoning in imperfect-information interactions. He has applied this research to creating Libratus, the first AI to defeat top humans in no-limit Texas Hold'em. His current research is focused on expanding the applicability of the technology behind Libratus to other domains.
  • Alekh Agarwal. MSR NYC
          Sample-Efficient Reinforcement Learning with Rich Observations

          20th Sep, 2017. 4pm-5pm   32-G882

     Abstract:

    This talk considers a core question in reinforcement learning (RL): How can we tractably solve sequential decision making problems where the learning agent receives rich observations? We begin with a new model called Contextual Decision Processes (CDPs) for studying such problems, and show that it encompasses several prior setups to study RL such as MDPs and POMDPs. Several special cases of CDPs are, however, known to be provably intractable in their sample complexities. To overcome this challenge, we further propose a structural property of such processes, called the Bellman Rank. We find that the Bellman Rank of a CDP (and an associated class of functions) provides an intuitive measure of the hardness of a problem in terms of sample complexity and is small in several practical settings. In particular, we propose an algorithm, whose sample complexity scales with the Bellman Rank of the process, and is completely independent of the size of the observation space of the agent. We also show that our techniques are robust to our modeling assumptions, and make connections to several known results as well as highlight novel consequences of our results.

    This talk is based on joint work with Nan Jiang, Akshay Krishnamurthy, John Langford and Rob Schapire.

      BIO

    Alekh Agarwal is a researcher in the New York lab of Microsoft Research, prior to which he obtained his PhD from UC Berkeley. Alekh’s research currently focuses on topics in interactive machine learning, including contextual bandits, reinforcement learning and online learning. Previously, he has worked on several topics in optimization including stochastic and distributed optimization. He has won several awards for his research including the NIPS 2015 best paper award.
  • Ohad Shamir. Weizmann Institute of Science, Israel
          TBD

          18th Oct, 2017. 4pm-5pm 32-G882

     Abstract:

      BIO

  • Raquel Urtasun. Univ of Toronto and Uber ATG
          TBD

          1st Nov, 2017; 4pm-5pm 32-G882

     Abstract:

      BIO

  • Katherine Heller. Duke University
          TBD

          29th Nov, 2017; 4pm-5pm 32-G882

     Abstract:

      BIO


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
          Measuring Sample Quality with Kernels
          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
          Robust Bayesian inference via coarsening
          26th Apr, 2017; 3pm-4pm   32-G575
  • 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