DIMACS / TRIPODS / MOPTA
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Conference program

Book of abstracts

Book of abstracts can be downloaded here.

Monday 13th of August 2018

8:00 8:45 Registration and breakfast
8:45 9:00 Opening remarks
9:00 10:00 Plenary talk
Tractable nonconvex optimization via geometry
Suvrit Sra abstract
10:00 11:00
DIMACS
Uniform Convergence of Gradients for Non-convex Learning and Optimization
Karthik Sridharan abstract
Logistic Regression: The Importance of Being Improper
Satyen Kale abstract
11:00 11:30 Coffee break
11:30 12:30
DIMACS
Building Algorithms by Playing Games
Jake Abernethy abstract
Risk Bounds for Classification and Regression Models that Interpolate
Daniel Hsu abstract
12:30 13:30 Lunch
13:30 14:30 Plenary talk
Representation, optimization and generalization properties of deep neural networks
Peter Bartlett abstract
14:30 15:30
DIMACS
Parameter-free Nonsmooth Convex Stochastic Optimization through Coin Betting
Francesco Orabona abstract
Data-dependent Hashing via Nonlinear Spectral Gaps
Alexandr Andoni abstract
15:30 16:00 Coffee break
16:00 17:30
DIMACS
Stochastic Optimization for AUC Maximization
Yiming Ying abstract
The Power of Interpolation: Machine Learning without Loss Functions and Regularization
Mikhail Belkin abstract
Contextual Reinforcement Learning
John Langford abstract
18:30 20:00 Happy Hour Social (Comfort Suites bar)

Tuesday 14th of August 2018

8:00 9:00 Registration and breakfast
9:00 10:30
DIMACS
Maximizing Submodular Functions Exponentially Faster
Yaron Singer abstract
Robustness and Submodularity
Stefanie Jegelka abstract
Nonconvex Sparse Deconvolution: Geometry and Efficient Methods
John Wright abstract
10:30 11:00 Coffee break
11:00 12:30
DIMACS
Learning Over-Parameterized Models with Gradient Descent: An Average-Case Analysis over Quadratic Loss Functions
Hossein Mobahi abstract
Statistical Properties of Stochastic Gradient Descent
Panos Toulis abstract
Direct Runge-Kutta Discretization Achieves Acceleration
Aryan Mokhtari abstract
12:30 13:30 Lunch
13:30 14:30 Plenary talk
Better models in optimization
John Duchi abstract
14:30 15:30
DIMACS
Frank-Wolfe Splitting via Augmented Lagrangian Method
Simon Lacoste-Julien abstract
Second Order Optimization and Non-convex Machine Learning
Michael Mahoney abstract
15:30 16:00 Coffee break
16:00 17:00
DIMACS
Optimization over Nonnegative Polynomials
Amir Ali Ahmadi abstract
Stochastic Quasi-gradient Methods: Variance Reduction via Jacobian Sketching
Peter Richtarik abstract
17:00 19:00 Poster session and cocktail reception
Posters
Feasible Level-set Methods for Optimization with Stochastic or Data-driven Constraints
Qihang Lin abstract
Bounding and Counting Linear Regions of Deep Neural Networks
Thiago Serra abstract
Level-set Methods for Finite-sum Constrained Convex Optimization
Runchao Ma abstract
A Machine Learning Approximation Algorithm for Fast Prediction of Solutions to Discrete Optimization Problems
Sébastien Lachapelle abstract
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes
Xiaoyu Li abstract
Distributed First-order Algorithms with Gradient Tracking Converge to Second-order Stationary Solutions
Amir Daneshmand abstract
Estimation of Individualized Decision Rules Based on an Optimized Covariate-dependent Equivalent of Random Outcomes
Zhengling Qi abstract
A Stochastic Trust Region Algorithm Based on Careful Step Normalization
Rui Shi abstract
Revisiting the Foundations of Randomized Gossip Algorithms
Nicolas Loizou abstract
Underestimate Sequences via Quadratic Averaging
Majid Jahani abstract
A Unifying Scheme Of Primal-Dual Algorithms for Distributed Optimization
Fatemeh Mansoori abstract
Extrapolation of Finite Element Simulation with Graph Convolutional Lstm
Yue Niu abstract
Expected Risk and Auc Optimization without Dependence on the Data Set Size
Minhan Li abstract
Robust Learning of Trimmed Estimators via Manifold Sampling
Matt Menickelly abstract
Projective Splitting with Forward Steps: Asynchronous and Block-iterative Operator Splitting
Patrick Johnstone abstract
Inexact SARAH for Solving Stochastic Optimization Problems
Lam Nguyen abstract
Kernel Methods: Optimal Online Compressions, and Controlling Error Variance
Alec Koppel abstract
Towards Fast Computation of Certified Robustness for ReLU Networks
Tsui-Wei (Lily) Weng abstract

Wednesday 15th of August 2018

8:00 9:00 Registration and breakfast
9:00 10:00 Plenary talk
Deep Learning with Dense Connectivity
Kilian Weinberger abstract
10:00 10:15 Coffee break
10:15 11:45 Parallel technical sessions
Mopta Competition DIMACS
Team Opti Mice (Colombia)
Daniel Cifuentes Daza abstract
A Positive Outlook on Negative Curvature
Daniel P. Robinson abstract
Team Sparkles (USA)
Rakesh Pandian Thangaraju abstract
How to Characterize Worst-case Performance of Algorithms for Nonconvex Optimization
Frank Curtis abstract
Team ZIB (Germany)
Mats Olthoff abstract
A Convex Lens for Non-convex Problems
Benjamin Haeffele abstract
11:45 12:00 Coffee break
12:00 13:00 Plenary talk
Practical Conditional Gradient Algorithms
Steve Wright abstract
13:00 14:00 Lunch
14:00 15:30 Parallel technical sessions
DIMACS Novel Directions in Optimization Metaheuristics and Their Applications
Dimensionality Reduction Techniques for Global Optimization
Coralia Cartis abstract
On the Complexity of Testing Attainment of the Optimal Value in Nonlinear Optimization
Jeffrey Zhang abstract
How to Create a Elo-based Sports Ranking and Prediction Model
Eric Landquist abstract
“Active-set Complexity” of Proximal Gradient: How Long Does It Take to Find the Sparsity Pattern?
Mark Schmidt abstract
A Combinatorial Approach for Optimal Coloring of Perfect Graphs
Cemil Dibek abstract
A Population-based Metaheuristic Approach for Solving the Multi-demand Multidimensional Knapsack Problem
Yun Lu abstract
Stochastic Methods for Non-smooth Non-convex Optimization
Damek Davis abstract
Fast Fourier Linear Optimization
Elahesadat Naghib abstract
Analysis of Parameterless Metaheuristics as Applied to the MDMMKP
Dylan Gaspar abstract
15:30 16:00 Coffee break
16:00 17:30 Parallel technical sessions
DIMACS Large Scale Mixed Integer Optimization Modeling and Optimizing Energy Systems
New Framework For Convergence Analysis Of Stochastic Optimization Methods (Part 1)
Katya Scheinberg abstract
Representabillity of Mixed-integer Bilevel Problem
Sriram Sankaranarayanan abstract
Design of Time-delay Directed Dynamical Networks
Shima Dezfulian abstract
New Framework For Convergence Analysis Of Stochastic Optimization Methods (Part 2)
Courtney Paquette abstract
Red-Blue-Partitioned Network Optimization
Matthew Johnson abstract
A Three-level Optimization Model for Fuel-supply Strategies of Natural Gas-fired Units
Bining Zhao abstract
Do We Need 2nd Order Methods in Machine Learning?
Martin Takac abstract
Generalized Benders' Algorithm for Mixed Integer Bilevel Linear Optimization
Suresh Bolusani abstract
On the Value of Dual-firing Power Generation Under Uncertain Gas Network Access
Boris Defourny abstract
18:00 19:00 Cocktail Reception (University Center, Asa Packer Campus)
19:00 21:00 Banquet (University Center, Asa Packer Campus)

Thursday 16th of August 2018

8:00 9:00 Registration and breakfast
9:00 10:00 Plenary talk
On big data, optimization and learning
Andrea Lodi abstract
10:00 10:15 Coffee break
10:15 11:45 Parallel technical sessions
New Directions in Optimization and Dynamical Systems Stochastic Optimization Iterative Methods Applications in Healthcare
Sparsity Still Matters
Robert Vanderbei abstract
LNG Supply Chain Planning Under Demand and Supply Uncertainty : A Stochastic Programming Formulation and Solution Strategy.
Armando Guarnaschelli abstract
On Monotone Non-expansive Mapping and Their Approximation Fixed Point Results
Buthinah Bin Dehaish abstract
Kinetic Parameter Identification Based on Spectroscopic Data - advancements Illustrated by Case Studies
Christina Schenk abstract
Optimization over Invariant Sets of Dynamical Systems
Amir Ali Ahmadi abstract
Distribution Systems Hardening against Natural Disasters
Yushi Tan abstract
Tropical Optimization Problems: Recent Results and Applications Examples
Nikolai Krivulin abstract
A Further Study on the Opioid Epidemic Dynamical Model with Random Perturbation
Getachew Befekadu abstract
Motion Planning for Autonomous Vehicles Using MINLP
Hande Benson abstract
Convergence Rate of Stochastic Mirror Descent for Non-smooth Non-convex Optimization
Siqi Zhang abstract
Applying the Fractional Natural Decomposition Method to Solve Fractional Differential Equations in Multi-dimensional Space
Mahmoud Rawashdeh abstract
Dynamic Appointment Scheduling Problem with Patient Preferences
Secil Sozuer abstract
11:45 12:00 Coffee break
12:00 13:00 Plenary talk
Variability-aware power operations
Dan Bienstock abstract
13:00 14:00 Lunch
14:00 15:30 Parallel technical sessions
Optimization in Energy Methods for Nonlinear Optimization Recent Progress in Stochastic/Robust Optimization and Applications Derivative Free and Black-Box Optimization
Phase Transitions for Optimality Gaps in Optimal Power Flows
Pascal Van Hentenryck abstract
A Feasible Level-set Method for Optimization with Stochastic or Data-driven Constraints
Qihang Lin abstract
A Copositive Approach for Multi-stage Robust Optimization Problems
Grani A. Hanasusanto abstract
Efficient Optimal Design of Latent Energy Storage Systems
Chunjian Pan abstract
Statistical Learning for (Power System) Optimization: An Active Set Approach
Line Roald abstract
Level-set Methods for Finite-sum Constrained Convex Optimization
Runchao Ma abstract
Nurse Staffing under Uncertain Demand and Absenteeism
Minseok Ryu abstract
A New local Parallelization for Particle Swarm Optimization
Abd AlRahman R. AlMomani Ahmad Almomani abstract
From Power System State Estimation to Low Rank Tensor Completion
Cédric Josz abstract
An Inexact Penalty Sequential Linear Optimization Method for Constrained Nonlinear Optimization
Yuyang Rong abstract
A Data-driven Distributionally Robust Optimization Approach for Appointment Scheduling With Random Service Durations and No-shows
Guanglin Xu abstract
Derivative-free Optimization of Noisy Functions via Quasi-Newton Methods
Albert Berahas abstract
Optimal Power Flow with Robust Feasibility Guarantees
Daniel Molzahn abstract



15:30 16:00 Coffee break
16:00 17:30 Parallel technical sessions
Learning and Energy Nonlinear Optimization Stochastic and Robust Optimization Algorithms and Applications Reinforcement Learning for Supply Chain
Data Recovery and Event Identification from Highly Quantized Measurements
Meng Wang abstract
Revisiting the Foundations of Randomized Gossip Algorithms
Nicolas Loizou abstract
SUNlayer: Stable Denoising with Generative Networks
Soledad Villar abstract
Concise Fuzzy Representation of Big Graphs: A Dimensionality Reduction Approach
Faisal Abu-Khzam abstract
Renewable Scenario Generation Using Adversarial Networks
Baosen Zhang abstract
Convergence Rates of Proximal Gradient Methods via the Convex Conjugate
David Gutman abstract
Stochastic ADMM Frameworks for Resolving Structured Stochastic Convex Programs
Yue Xie abstract
RL for Inventory Optimization: Case on Beer Game
Afshin Oroojlooy abstract
Learning Power Flows with Support Vector Machines
Ram Rajagopal abstract
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy
Majid Jahani abstract
Exploiting Problem Structure in Optimization under Uncertainty via Online Convex Optimization
Nam Ho-Nguyen abstract
Reinforcement Learning for Solving the Vehicle Routing Problem
MohammadReza Nazari abstract
Learning and Control in Distribution Grids
Michael Chertkov abstract



19:00 21:00 Graduate Student Social - Packer House, 217 West Packer Avenue, Bethlehem

Friday 17th of August 2018

8:00 9:00 Registration and breakfast
9:00 10:00 Plenary talk
Optimizing Prioritized and Nested Solutions
David Morton abstract
10:00 10:15 Coffee break
10:15 11:45 Parallel technical sessions
Resilience in Power Systems Optimization and Learning Conic Optimization and Integer Programming Sparse Optimization
Communication-constrained Expansion Planning for Resilient Distribution Systems
Pascal Van Hentenryck abstract
A Trust-Region Method for Minimizing Regularized Non-convex Loss Functions
Dimitri Papadimitriou abstract
Determine the Maximum Permissible Perturbation Set of SDP Problem With Unknown Perturbations
Tingting Tang abstract
L0-regularized Sparsity for Probabilistic Mixture Models
Dzung Phan abstract
Proactive and reactive operations paradigms for improving power system resilience to extreme weather events
J.P. Watson abstract
Bregman-divergence for Legendre Exponential Families
Hyenkyun Woo abstract
Optimal Cutting Planes from the Group Relaxations
Amitabh Basu abstract
The CCP Selector: Scalable Algorithms for Sparse Ridge Regression from Chance-constrained Programming
Weijun Xie abstract
Probabilistic N-k Failure-identification for Power Systems
Harsha Nagarajan abstract
Learning-based Robust Optimization: Procedures and Statistical Guarantees
Zhiyuan Huang abstract
Covering Problem via Nonlinear Semidefinite Programming
Walter Gomez abstract
Accelerated Preconditioned Alternating Direction Methods of Multipliers with Non-ergodic Optimal Rates
Quoc Tran-Dinh abstract
Designing Resilient Distribution Systems under Natural Disasters
Ruiwei Jiang abstract



11:45 12:00 Coffee break
12:00 13:00 Plenary talk
New Energy Space Modeling and Implications on Complexity of Decision Making and Control in Electric Energy Systems
Marija Ilic abstract
13:00 14:00 Lunch
14:00 15:30 Parallel technical sessions
Equilibrium and Complementarity Modeling in Energy Markets Stochastic Gradient Decent and Conve Optimization Optimization in Machine Learning
Efficiency and Welfare Distribution Effects From the Norwegian-Swedish Tradable Green Certificate Market
Asgeir Tomasgard abstract
Reliable Machine Learning Using Unreliable Components: Error-runtime Trade-offs in Distributed SGD
Sanghamitra Dutta abstract
A Machine Learning Technique for Quadcopter State Estimation
Arash Amini abstract
A Column-and-constraint Decomposition Approach for Solving EPECs
David Pozo abstract
Complexity Bounds for Structured Convex Optimization
Yuyuan Ouyang abstract
Hyperparameter Tuning of Neural Networks(NNs) via Derivative Free Optimization(DFO)
Mertcan Yetkin abstract
Market Integration of HVDC
Spyros Chatzivasileiadis abstract
Optimal Diminishing Stepsizes in SGD for Strongly Convex Objective Functions
Phuong Ha Nguyen abstract
Bidirectional LSTM Ensemble Structures for Multi-step Forecasting Of Ocean Wave Elevation
Mohammad Pirhooshyaran abstract
Bi-level Network Planning with Generation-market Equilibria Subject to Transmission Costs Recovery
Pengcheng Ding abstract