Machine Learning Seminars

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Title: Compressing Variational Bayes
Speaker: Stefan Mandt
When: Tuesday, November 17, 2020 - 12:30
Location: Zoom TBD
Neural image compression methods have recently outperformed their classical counterparts in rate-distortion performance and show great potential to also revolutionize video coding. In this talk, I will show how methods from approximate Bayesian inference and generative modeling can lead to dramatic performance improvements in compression. In particular, I will explain how sequential variational autoencoders can be converted into video codecs, how deep latent variable models can be compressed in post-processing with variable bitrates, and how iterative amortized inference can be used to achieve the world record in image compression performance.
Stephan Mandt is an Assistant Professor of Computer Science at the University of California, Irvine. From 2016 until 2018, he was a Senior Researcher and Head of the statistical machine learning group at Disney Research, first in Pittsburgh and later in Los Angeles. He held previous postdoctoral positions at Columbia University and Princeton University. Stephan holds a Ph.D. in Theoretical Physics from the University of Cologne. He is a Fellow of the German National Merit Foundation, a Kavli Fellow of the U.S. National Academy of Sciences, and was a visiting researcher at Google Brain. Stephan regularly serves as an Area Chair for NeurIPS, ICML, AAAI, and ICLR, and is a member of the Editorial Board of JMLR. His research is currently supported by NSF, DARPA, Intel, and Qualcomm. .
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Title: Oblivious data for kernel methods
Speaker: Steffen Grunewalder (Lancaster)
When: Thursday, January 30, 2020 - 12:00
Location: Allen CSE1 403
I’ll present an approach to reduce the influence of sensitive features in data in the context of kernel methods. The resulting method uses Hilbert space valued conditional expectations to create new features that are close approximations of the original (non-sensitive) features while having a reduced dependence on the sensitive features. I’ll provide optimality statements about these new features and a bound on the alpha-mixing coefficient between the sensitive features and these new features. In practice, standard techniques to estimate conditional expectations can be used to generate these features. I’ll discuss a plug-in approach for estimating conditional expectation which uses properties of the empirical process to control estimation errors. .
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Title: The Complexity of Non-Convex Stochastic Optimization
Speaker: Dylan Foster (MIT)
When: Thursday, December 5, 2019 - 10:00
Location: CSE2 371
We characterize the complexity of finding approximate stationary points (points with gradient norm at most epsilon) using stochastic first-order methods. In the well-studied model where algorithms access smooth, potentially non-convex functions through queries to an unbiased stochastic gradient oracle with bounded variance, we prove that any algorithm requires at least $\epsilon^{-4}$ queries to find an $\epsilon$-stationary point. This lower bound is tight, and establishes that stochastic gradient descent is minimax optimal. In a more restrictive model where the noisy gradient estimates satisfy an additional "mean-squared smoothness" property, we prove a lower bound of $\epsilon^{-3}$ queries, establishing the optimality of recently proposed variance reduction techniques. Joint work with Yossi Arjevani, Yair Carmon, John Duchi, Nathan Srebro, and Blake Woodworth.
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Title: The brain outside the lab
Speaker: Bing Brunton (University of Washington)
When: Tuesday, November 12, 2019 - 12:00
Location: Allen 403
Developing useful interfaces between brains and machines is a grand challenge of neuroengineering. An effective interface has the capacity to not only interpret neural signals, but predict the intentions of the human to perform an action in the near future; prediction is made even more challenging outside well-controlled laboratory experiments. I will talk about past and ongoing work in my lab to understand and model neural activity underlying natural human behavior, focusing on arm movements. We work with an opportunistic clinical dataset that comprises continuously monitored poses for 12 human subjects over ~1500 total hours, along with the simultaneously recorded high-resolution intracranial neural activity sampled at 1 kHz. The size and scope of this dataset greatly exceeds all previous datasets with movements and neural recordings (including our previously published AJILE), making it possible to leverage modern data-intensive techniques in machine learning to decode and understand the neuroscience of natural human behaviors.
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Title: Some recent advances in learning from user behavior
Speaker: Adith Swaminathan (Microsoft Research)
When: Tuesday, October 29, 2019 - 12:00
Location: Allen 403
How can we re-use the logs of an interactive system to train new policies to engage with users? Building on work in off-policy contextual bandits and reinforcement learning, we will describe recent work (with Yao Liu, Alekh Agarwal and Emma Brunskill;, UAI’19) that describes a class of off-policy estimators for temporally-extended interaction settings. Then, we will describe recent work (with Eric Zhan, Matthew Hausknecht and Yisong Yue; that derives calibratable policies which exhibit controllable generation of diverse long-term sequential behavior. We will conclude by sketching some open problems in counterfactual learning for user-interactive systems.
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Title: Learning to Decide: Dynamics and Economics
Speaker: Nika Haghtalab (Cornell University)
When: Thursday, October 24, 2019 - 11:00
Location: Gates 172
Machine learning systems are increasingly used for automated decision making; for example for designing economic policies and for identifying qualified candidates in education, financial, or judicial systems. When designing such systems, it is important to consider how changes in the target population or the environment affect the performance of the systems. Moreover, it is important to consider how these systems influence the societal forces that impact the target population. In this talk, I will explore three lines of my research addressing this dynamical and economic perspective on machine learning: Learning parameters of auctions in presence of changes in user preferences, learning admission and hiring classifiers that encourage candidates to invest in valuable skill sets, and augmenting human decision making in hiring and admission to increase the diversity of candidates.
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Title: Speeding up Distributed SGD via Communication-Efficient Model Aggregation
Speaker: Gauri Joshi (Carnegie Mellon University)
When: Tuesday, October 22, 2019 - 12:00
Location: Allen 403
Large-scale machine learning training, in particular, distributed stochastic gradient descent (SGD), needs to be robust to inherent system variability such as unpredictable computation and communication delays. This work considers a distributed SGD framework where each worker node is allowed to perform local model updates and the resulting models are averaged periodically. Our goal is to analyze and improve the true speed of error convergence with respect to wall-clock time (instead of the number of iterations). For centralized model-averaging, we propose a strategy called AdaComm that gradually increases the model-averaging frequency in order to strike the best error-runtime trade-off. For decentralized model-averaging, we propose MATCHA, where we use matching decomposition sampling of the base graph to parallelize inter-worker information exchange and reduce communication delay. Experiments on training deep neural networks show that AdaComm and MATCHA can take 3x less time to achieve the same final training loss as compared to fully synchronous SGD and vanilla decentralized SGD respectively.
Based on joint work with Jianyu Wang, Anit Sahu, and Soummya Kar
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Title: Adaptive Discretization for Decision making in large continuous spaces
Speaker: Christina Yu (Cornell University)
When: Monday, October 21, 2019 - 01:00
Location: Gates 271
In this talk, I will present a sequence of two works that explore adaptive discretization for decision making in large (continuous) state and action spaces. In the first work, we present a novel Q-learning policy for epsiodic reinforcement learning with adaptive data-driven discretization; the algorithm selectively maintains a finer partition of the state-action space in regions which are frequently visited in historical trajectories, and have higher payoff estimates. We recover the worst case regret guarantees of prior algorithms for continuous state-action spaces, which additionally require either an optimal discretization as input, and/or access to a simulation oracle. Moreover, experiments suggest that the algorithm automatically adapts to the underlying structure of the problem, resulting in a lower memory footprint and a faster convergence compared to using uniform discretization. In the second work, we consider the challenge when the metric is unknown. We consider a nonparametric contextual multi-arm bandit problem where each arm is associated to a nonparametric reward function mapping from contexts to the expected reward. Suppose that there is a large set of arms, yet there is a simple but unknown structure amongst the arm reward functions, e.g. finite types or smooth with respect to an unknown metric space. We present a novel algorithm which learns data-driven similarities amongst the arms, in order to implement adaptive partitioning of the context-arm space for more efficient learning. We provide regret bounds along with simulations that highlight the algorithm's dependence on the local geometry of the reward functions.
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Title: Performance of Q-learning with Linear Function Approximation: Stability and Finite-Time Analysis
Speaker: Siva Theja Maguluri (Georgia Tech)
When: Friday, October 18, 2019 - 01:30
Location: CSE 303
We consider the model-free reinforcement learning problem and study the popular Q-learning algorithm with linear function approximation for finding the optimal policy. We provide finite-time error bounds for the convergence of Q-learning with linear function approximation under an assumption on the sampling policy. Unlike some prior work in the literature, we do not make the unnatural assumption that the samples are i.i.d. (since they are Markovian), and do not require an additional projection step in the algorithm. To show this result, we first consider a more general nonlinear stochastic approximation algorithm under Markovian noise, and derive a finite-time bound on the mean-square error, which we believe is of independent interest. The key idea of our proof is to use Lyapunov drift arguments and exploit the geometric mixing property of the underlying Markov chain. This talk is based on the following paper:
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Title: Safe and Reliable Reinforcement Learning for Continuous Control
Speaker: Stephen Tu
When: Tuesday, April 23, 2019 - 12:00
Location: CSE2 271
Many autonomous systems such as self-driving cars, unmanned aerial vehicles, and personalized robotic assistants are inherently complex. In order to deal with this complexity, practitioners are increasingly turning towards data-driven learning techniques such as reinforcement learning (RL) for designing sophisticated control policies. However, there are currently two fundamental issues that limit the widespread deployment RL: sample inefficiency and the lack of formal safety guarantees. In this talk, I will propose solutions for both these issues in the context of continuous control tasks. In particular, I will show that in the widely applicable setting where the dynamics are linear, model-based algorithms which exploit this structure are substantially more sample efficient than model-free algorithms, such as the widely used policy gradient method. Furthermore, I will describe a new model-based algorithm which comes with provable safety guarantees and is computationally efficient, relying only on convex programming. I will conclude the talk by discussing the next steps towards safe and reliable deployment of reinforcement learning.