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Presentation Schedule - Spring 2019

	Date	Paper	Presenter	Readers
	02/12/2019	Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization	Daniel Vasquez	Chi-Hua Wang and Bin Du
	02/14/2019	Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes	David Newton	Jiapeng Liu and Mustafa Lokhandwala
	02/19/2019	A Distributed Stochastic Gradient Tracking Method	Bingjing Tang	Larissa Mori and Monika Tomar
	02/21/2019	ADAM : A Method for Stochastic Optimization	Kent Gauen	Nimish Awalgaonkar and Ruixin Wang
	02/26/2019	Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization	Prateek Jaiswal	Yang Xie and Zhanyu Wang
	02/28/2019	Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers	Shujin Jiang	Chi-Hua Wang and Viplove Arora
	03/05/2019	An Optimal First Order Method Based On Optimal Quadratic Averaging Quadratic Averaging	Tian Ye	Gourav Lalitkumar Jhanwar and Tejaskumar Pradipbhai Tamboli
	03/07/2019	NEWTON SKETCH: A NEAR LINEAR-TIME OPTIMIZATION ALGORITHM WITH LINEAR-QUADRATIC CONVERGENCE (Slide)	Chi-Hua Wang	David Newton and Daniel Vasquez
	03/19/2019	The Landscape of Empirical Risk for Non-convex Losses	Ruixin Wang	Prateek Jaiwal and Kent Gauen
	03/21/2019	Optimality guarantees for distributed statistical estimation	Larissa Mori	Bingjing Tang and Shujin Jiang
Cancelled
	03/26/2019	Confidence Intervals and Hypothesis Testing for High-Dimensional Regression	Zhanyu Wang	Tian Ye and Prateek Jaiswal
	04/02/2019	Balancing Communication and Computation in Distributed Optimization	Bin Du	Daniel Vasquez
	04/04/2019	Talk by John Birge, Univ. of Chicago, (9:00 — 10:30, RAWL 3082), Dynamic Learning in Strategic Pricing Games
	04/09/2019	VARIATIONAL CALCULUS IN THE SPACE OF MEASURES AND OPTIMAL DESIGN	Prateek Jaiswal	Gourav Lalitkumar Jhanwar and David Newton
	04/11/2019	Simulation for American Options: Regression Now or Regression Later?	Yang Xie	Tejaskumar Tamboli and Ruixin Wang
	04/16/2019	EXTRA: AN EXACT FIRST-ORDER ALGORITHM FOR DECENTRALIZED CONSENSUS OPTIMIZATION	Mustafa Lokhandwala	Bingjing Tang and Shujin Jiang
	04/18/2019	STOCHASTIC FIRST- AND ZEROTH-ORDER METHODS FOR NONCONVEX STOCHASTIC PROGRAMMING (CHF 4/9/2019 : Train faster, generalize better: Stability of stochastic gradient descent; ON LARGE-BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA)	Chih-hao Fang	Kent Gauen and Zhanyu Wang
	04/23/2019	On Stochastic Subgradient Mirror-Descent Algorithm with Weighted Averaging	Nimish Awalgaonkar	Gourav Lalitkumar Jhanwar
	04/25/2019	ACHIEVING GEOMETRIC CONVERGENCE FOR DISTRIBUTEDOPTIMIZATION OVER TIME-VARYING GRAPHS	Viplove Arora	Bingjing Tang and Larissa Mori

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Train faster, generalize better

ON LARGE-BATCH TRAINING FOR DEEP LEARNING- GENERALIZATION GAP AND SHARP MINIMA