SimonsTV

Our videos can also be found on YouTube.
Extroverted Sublinear Algorithms
Monday, June 17 – Friday, June 21, 2024
Playlist: 24 videos
1.
New upper and lower bounds on approximate counting
Jelani Nelson (Simons Institute)
2.
Locally Private Histograms in All Privacy Regimes
Clément Canonne (University of Sydney)
3.
Shaving logs by weighing coins
Deeparnab Chakrabarty (Dartmouth College)
4.
Optimal Quantile Estimation for Streams
Mihir Singhal (UC Berkeley)
5.
O(log s)-Approximate Nearest Neighbor Search for the Earth Mover’s Distance
Rajesh Jayaram (Google Research NYC)
6.
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree
Peilin Zhong (Google)
7.
Efficiently Computing Similarities to Private Datasets
Sandeep Silwal (MIT)
8.
ML Efficiency for Large Models: From Data Efficiency to Faster Transformers
Vahab Mirrokni (Google Research, NYC)
9.
Sub-Linear Algorithms meets Large Language Models
Anshumali Shrivastava (Rice University)
10.
Randomized Least Squares Optimization and its Incredible Utility for Large-Scale Tensor Decomp...
Tamara Kolda (MathSci.ai)
11.
Long-context Attention in Near-Linear Time
David Woodruff (Carnegie Mellon University)
12.
Sublinear-Time Algorithms in Learning
Ronitt Rubinfeld (Massachusetts Institute of Technology)
13.
Large Scale Private Learning on Data Streams, and the BLTs
Abhradeep Guha Thakurta (Google DeepMind)
14.
Private video
15.
Sublinear Insights: A Faster (Classical) Algorithm for Edge Coloring
Sepehr Assadi (University of Waterloo and Rutgers University)
16.
Linear and sublinear algorithms for graphlet sampling
Marco Bressan (University of Milan)
17.
Sublinear algorithms in social networks via core-periphery decomposition
Omri Ben-Eliezer (Simons Institute)
18.
Sublinear time algorithms for better than 1/2 approximation algorithms for max-cut on expanders
Akash Kumar (IIT Bombay)
19.
Streaming Algorithms for Connectivity Augmentation
Ali Vakilian (Toyota Technological Institute at Chicago)
20.
Talk By Ilya Mironov (Google Brain)
Ilya Mironov (Google Brain)
21.
Applications of Sub-linear Algorithms on Other Fields
Vladimir Braverman (Rice University)
22.
Hypothesis selection with computational constraints
Maryam Aliakbarpour (Rice University)
23.
Lightning Talks
24.
A Bi-metric Framework for Fast Similarity Search
Piotr Indyk (Massachusetts Institute of Technology)
Logic and Algorithms in Database Theory and AI Boot Camp
Playlist: 20 videos
1.
Query Optimization and Evaluation 1: Output Size Bounds and Information Theory
Hung Ngo (RelationalAI)
2.
Query Optimization and Evaluation 2: Worst-Case Optimal Join Algorithms
Hung Ngo (RelationalAI)
3.
Query Optimization and Evaluation 3: Variable Elimination and Tensor Decomposition
Hung Ngo (RelationalAI)
4.
Query Optimization and Evaluation 4: Instance Optimality
Mahmoud Abo Khamis (RelationalAI)
5.
Logic and Probabilistic Circuits 1
Guy Van den Broeck (UCLA)
6.
Logic and Probabilistic Circuits 2
YooJung Choi (ASU)
7.
Logic and Probabilistic Circuits 3
YooJung Choi (ASU)
8.
Logic and Probabilistic Circuits 4
Guy Van den Broeck (UCLA)
9.
Algebraic Structures in Logic and Query Evaluation 1
Val Tannen (University of Pennsylvania)
10.
Algebraic Structures in Logic and Query Evaluation 2
Val Tannen (University of Pennsylvania)
11.
Fine-Grained Complexity 1
Virginia Vassilevska Williams (MIT)
12.
Fine-Grained Complexity 2
Nicole Wein (University of Michigan)
13.
Algebraic Structures in Logic and Query Evaluation 3
Dan Suciu (University of Washington)
14.
Algebraic Structures in Logic and Query Evaluation 4
Dan Suciu (University of Washington)
15.
Fine-Grained Complexity 3
Virginia Vassilevska Williams (MIT)
16.
Fine-Grained Complexity 4
Yinzhan Xu (MIT)
17.
Incremental View Maintenance 1
Dan Olteanu (University of Zurich)
18.
Incremental View Maintenance 2
Dan Olteanu (University of Zurich)
19.
Sampling in Query Evaluation 1
Marcelo Arenas (Pontificia Universidad Católica de Chile and RelationalAI)
20.
Sampling in Query Evaluation 2
Marcelo Arenas (Pontificia Universidad Católica de Chile and RelationalAI)
The Economics of Networks
Playlist: 16 videos
1.
Learning from Viral Content
Kevin He (Penn)
2.
Social Connectedness and Information Markets
Rachel Kranton (Duke)
3.
Learning through the Grapevine
Suraj Malladi (Cornell)
4.
Protecting Privacy in New Data Markets
Asu Ozdaglar (MIT) *Presenting Virtually
5.
Games on Endogenous Networks
Evan Sadler (Columbia) *Presenting Virtually
6.
Long ties - Formation, social contagion & economic outcomes
Dean Eckles (MIT) *Presenting Virtually
7.
Market Power and Tax Interventions: A Principal Components Approach
Ben Golub (Northwestern)
8.
Persuasion in Networks: Public Signals and Cores
Ozan Candogan (U Chicago)
9.
Learning in Repeated Interactions on Networks
Omer Tamuz (Caltech)
10.
Experimental and Observational Studies in the Presence of Stochastic Networks
Alex Volfovsky (Duke)
11.
Too Much Data: Externalities and Inefficiencies in Data Markets
Azarakhsh Malekian (U. Toronto)
12.
Experimentation on Networks
Moritz Meyer-ter-Vehn (UCLA)
13.
Social Learning and Sample Herding in Networks with Homophily
Matt Jackson (Stanford)
14.
Organizing Modular Production
Bryony Reich (Northwestern)
15.
(Relaxing) Common Belief for Social Networks
Grant Schoenbeck (U. Michigan)
16.
Aggregative Efficiency of Bayesian Learning in Networks
Krishna Darasartha (Boston U. )
Dynamic Regret Minimization for Bandits without Prior Knowledge
Sep. 2022
Chen-Yu Wei (University of Southern California)
https://simons.berkeley.edu/talks/tbd-482
Quantifying Uncertainty: Stochastic, Adversarial, and Beyond

To evaluate the performance of a bandit learner in a changing environment, the standard notion of regret is insufficient. Instead, "dynamic regret" is a better measure that can evaluate the learner's ability to track the changes. How to achieve the optimal dynamic regret without prior knowledge on the number of times the environment changes had been open for a long time, and was recently resolved by Auer, Gajane, and Ortner in their COLT 2019 paper. We will discuss their consecutive sampling technique, which is rare in the bandit literature, and see how their idea can be elegantly generalized to a wide range of bandit/RL problems. Finally, we will discuss important open problems that remain in the area.
Multi-Distribution Learning, for Robustness, Fairness, and Collaboration
Aug. 2022
Nika Haghtalab (UC Berkeley)
https://simons.berkeley.edu/talks/tbd-460
Data-Driven Decision Processes Boot Camp

Social and real-world considerations such as robustness, fairness, social welfare, and multi-agent tradeoffs have given rise to multi-distribution learning paradigms. In recent years, these paradigms have been studied by several disconnected communities and under different names, including collaborative learning, distributional robustness, and fair federated learning. In this short tutorial, I will highlight the importance of multi-distribution learning paradigms in general, introduce technical tools for addressing them, and discuss how these problems relate to classical and modern consideration in data driven processes.
Computational Phase Transitions
Playlist: 21 videos
1.
Incremental Approximate Message Passing
Ahmed El Alaoui, Stanford University
2.
Counting Solutions to Random CNF Formulas
Leslie Ann Goldberg, University of Oxford
3.
Testing Correlation of Unlabeled Random Graphs
Jiaming Xu, Duke University
4.
Local Statistics, Semidefinite Programming, and Community Detection
Jess Banks, UC Berkeley
5.
Local Statistics, Semidefinite Programming, and Community Detection II
Sidhanth Mohanty, UC Berkeley
6.
Equitable Colorings
Afonso Bandeira, NYU
7.
A Positivity-First Approach to Sum-of-Squares Over the Hypercube
Tim Kunisky, NYU
8.
Optimization of Full-RSB Spherical Spin Glasses
Eliran Subag, Courant
9.
Thresholds of Descending Algorithms in Inference Problems
Stefano Sarao Mannelli, IPhT CEA-Saclay
10.
The Sum-of-Squares Approach to Clustering Non-Spherical Gaussian Mixtures
Pravesh Kothari, CMU
11.
Pedagogical Talk: Random Optimisation Problems
Amin Coja-Oghlan, Goethe University
12.
Information/Computation Gaps in Heavy-Tailed Statistics
Sam Hopkins, UC Berkeley
13.
Correlation Decay for Hard Spheres via Markov Chains
Samantha Petti, Harvard
14.
Sampling From the Random Cluster Model at All Temperatures on Random Graphs
Will Perkins, University of Illinois at Chicago
15.
The Rank of Sparse Random Matrices
Jane Gao, University of Waterloo
16.
Pedagogical Talk: Failure of Low-Degree Polynomials
Alex Wein, NYU
17.
The Overlap Gap Property in Principal Submatrix Recovery
Subhabrata Sen, Harvard
18.
Free Energy Wells and Overlap Gap Property in Sparse PCA
Ilias Zadik, NYU
19.
Reducibility and Statistical-Computational Gaps from Secret Leakage
Guy Bresler, MIT
20.
Pedagogical Talk: Frameworks for Information-Computation Tradeoffs
Tselil Schramm, Stanford University
21.
Phase Retrieval in High Dimensions: Statistical and Computational Phase Transitions
Florent Krzakala, ENS
Interpretability - now what?
Jul. 2019
Been Kim (Google Brain)
https://simons.berkeley.edu/talks/tbd-72
Frontiers of Deep Learning
Privacy and the Science of Data Analysis
Playlist: 34 videos
1.
Locally Private Bayesian Inference for Count Models
Steven Wu (University of Minnesota Twin Cities)
2.
Finite Sample Differentially Private Confidence Intervals
Vishesh Karwa (Temple University)
3.
Differential Privacy with Elliptical Stochastic Processes
Matthew Reimherr (Pennsylvania State University)
4.
Robust Statistics and Bias Correction: Ideas for Differential Privacy and Utility
Roberto Molinari (Pennsylvania State University)
5.
Differentially Private Inference for Binomial Data
Jordan Awan (Pennsylvania State University)
6.
The Structure of Optimal Private Tests for Simple Hypotheses
Audra McMillan (Boston University)
7.
Practical Difficulties in the Application of Differential Privacy for the Release of Large-Scale...
Philip LeClerc (U.S. Census Bureau)
8.
Shrinkwrap: Differentially-Private Query Processing in Private Data Federations
Johes Bater (Northwestern University)
9.
Analyzing Administrative Data with Privacy Protection in Place
Frauke Kreuter (University of Maryland)
10.
Differentially Private Inference for Regression Coefficients
Andrés Felipe Barrientos (Duke University)
11.
Node Privacy, High-dimensional Graph Summaries, and Block Models
Adam Smith (Boston University)
12.
Optimality in Locally Private Estimation and Learning
John Duchi (Stanford University)
13.
PANEL: Statistical Theory, Privacy and Data Analysis
‹ Home ‹ Programs & Events ‹ Workshops & Symposia ‹ Privacy and the Science of Data Analysis
14.
Differentially Private Change-Point Detection
Rachel Cummings (Georgia Institute of Technology)
15.
Bandit Learning under Differential Privacy
Or Sheffet (University of Alberta)
16.
Pufferfish Privacy Mechanisms for Correlated Data
Shuang Song (Google)
17.
Black-box Random Differential Privacy with Sensitivity Sampling
Ben Rubinstein (Melbourne University)
18.
The Privacy Blanket of the Shuffle Model
Borja Balle (Amazon)
19.
Open Problem Session
Open Problem Session
20.
Locally Private Statistical Inference: Communication, Samples, and Shared Randomness
Jayadev Acharya (Cornell University)
21.
Privacy Amplification by Sampling and Renyi Differential Privacy
Yu-Xiang Wang (UC Santa Barbara)
22.
Private Boosting and Noise-tolerant Learning
Mark Bun (Princeton University)
23.
Learning without Interaction Requires Separation
Vitaly Feldman (Google)
24.
Private Meta-Selection
Kunal Talwar (Google)
25.
Private Center Points and Learning of Halfspaces
Amos Beimel (Ben Gurion University)
26.
Federated Heavy Hitters Discovery with Differential Privacy
Peter Kairouz (Google AI)
27.
Private Learning with Auxiliary Public Data
Raef Bassily (The Ohio State University)
28.
Is Interaction Necessary for Distributed Private Learning?
Abhradeep Thakurta (UC Santa Cruz)
29.
Groups and Individuals in Fairness and Probability
Cynthia Dwork (Harvard University)
30.
Towards a Characterization of Differentially Private Learning
Shay Moran (Princeton University)
31.
Smooth Sensitivity, Revisited
Thomas Steinke (IBM Almaden)
32.
On the Power of Interactivity in Local Differential Privacy
Aaron Roth (University of Pennsylania)
33.
The Limits of Post-Selection Generalization
Uri Stemmer (Ben Gurion University)
34.
A Necessary and Sufficient Stability Notion for Adaptive Generalization
Moshe Shenfeld (Hebrew University)
Analog Quantum Simulations: What We Have, What We Would like to Have, and the...
Jun. 2018
Jens Eisert, Freie Universität Berlin
https://simons.berkeley.edu/talks/jens-eisert-06-11-18
Challenges in Quantum Computation
A Functional Classification of Glutamatergic Circuits in Cortex and Thalamus
Apr. 2018
S. Murray Sherman, University of Chicago
https://simons.berkeley.edu/talks/s-murray-sherman-4-18-18
Computational Theories of the Brain
Inferring Single-Trial Neural Population Dynamics Using Sequential Auto-Encoders
Mar. 2018
David Sussillo, Google
https://simons.berkeley.edu/talks/david-sussillo-3-22-18
Targeted Discovery in Brain Data
Discrete Optimization via Continuous Relaxation
Much of the progress in solving discrete optimization problems, especially in terms of approximation algorithms, has come from designing novel continuous relaxations. The primary tools in this area are linear programming and semidefinite programming. Other forms of relaxations have also been developed, such as multilinear relaxation for submodular optimization. In this workshop, we explore the state-of-the-art techniques for performing discrete optimization based on continuous relaxations of the underlying problem, as well as our current understanding of the limitations of this kind of approach. We focus on LP/SDP relaxations and techniques for rounding their solutions, as well as methods for submodular optimization, both in the offline and online setting. We also investigate the limits of such relaxations and hardness of approximation results.
Playlist: 28 videos
1.
The Power of Hierarchies for Detecting Hidden Structures
Prasad Raghavendra, UC Berkeley
2.
Simplex Transformations and Multiway Cut
Roy Schwartz, Technion - Israel Institute of Technology
3.
LPs and Convex Programming Relaxations and Rounding for Stochastic Problems
Anupam Gupta, Carnegie Mellon University
4.
Discrepancy and Approximation Algorithms
Sasho Nikolov, University of Toronto
5.
On Approximating the Covering Radius and Finding Dense Lattice Subspaces
Daniel Dadush, Centrum Wiskunde & Informatica
6.
A Strongly Polynomial Algorithm for Bimodular Integer Linear Programming
Rico Zenklusen, ETH Zürich
7.
Improved Approximation Algorithms for the TSP and S-t-path TSP
David Shmoys, Cornell University
8.
A Constant-factor Approximation Algorithm for the Asymmetric Traveling Salesman Problem
László Végh, London School of Economics
9.
Compact, Provably-good LPs for Orienteering and Regret-bounded Vehicle Routing
Chaitanya Swamy, University of Waterloo
10.
Surviving in Directed Graphs: A Quasi-polynomial-time Polylogarithmic Approximation...
Fabrizio Grandoni, IDSIA, University of Lugano
11.
Improved Approximation for Non-preemptive Scheduling via Time-indexed LP Relaxations
Shi Li, SUNY Buffalo
12.
LP Rounding for Poise
R. Ravi, Carnegie Mellon University
13.
Submodular Optimization
Niv Buchbinder, Tel Aviv University
14.
Submodular Unsplittable Flow on Trees
Anna Adamaszek, University of Copenhagen
15.
A Nearly-linear Time Algorithm for Submodular Maximization with a Knapsack Constraint
Alina Ene, Boston University
16.
Online Optimization, Smoothing, and Competitive Ratio
Maryam Fazel, University of Washington
17.
The Non-Uniform k-Center Problem
Deeparnab Chakrabarty, Dartmouth College
18.
Beyond Worst Case Analysis in Approximation
Uri Feige, Weizmann Institute of Science
19.
Algorithms for Stable and Perturbation-resilient Problems
Kostya Makarychev, Northwestern University
20.
Scheduling on Related Machines
Debmalya Panigrahi, Duke University
21.
Low Diameter Graph Decompositions and Approximating Unique Games
Lap-Chi Lau, University of Waterloo
22.
A (1+epsilon)-approximation for Makespan Scheduling with Precedence Constraints Using LP Hierarchies
Thomas Rothvoß, University of Washington
23.
LP Relaxations for Reordering Buffer Management
Yuval Rabani, The Hebrew University of Jerusalem
24.
A Survey of Dependent Rounding
Aravind Srinivasan, University of Maryland
25.
Finding Best LP Relaxations for Various Cut Problems
Euiwoong Lee, Carnegie Mellon University
26.
Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration
Nisheeth Vishnoi, École Polytechnique Fédérale de Lausanne
27.
Approximating the Permanent of Positive Semidefinite Matrices
Nima Anari, Stanford University
28.
Local Guarantees in Graph Cuts and Clustering
Moses Charikar, Stanford University
A Cost Function for Similarity-Based Hierarchical Clustering
May. 2017
Sanjoy Dasgupta, UC San Diego
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/tba-3
Brief Tutorial on Probabilistic Databases
Oct. 2016
Dan Suciu, University of Washington
https://simons.berkeley.edu/talks/dan-suciu-10-05-2016
Uncertainty in Computation
From the Inside: Economics and Computation
Dec. 2015
Richard Karp sat down with Tim Roughgarden to discuss the Fall 2015 program on Economics and Computation.

https://simons.berkeley.edu/programs/economics2015
The State of Techniques for Solving Large Imperfect-Information Games
Nov. 2015
Tuomas Sandholm, Carnegie Mellon University
Algorithmic Game Theory and Practice
https://simons.berkeley.edu/talks/tuomas-sandholm-2015-11-18
Algorithmic Game Theory and Practice
Nov. 16 – Nov. 20, 2015
Playlist: 23 videos
1.
Kidney Exchange: Algorithms and Incentives
Alvin Roth, Stanford University
2.
Designing Online Advertising Markets
Susan Athey, Stanford University
3.
Outsourcing Tasks Online: Matching Supply and Demand on Peer-to-Peer Internet
Chiara Farronato, Harvard University
4.
Business Model Analysis
Kamal Jain, Faira.com
5.
The Limits of Reputation in Platform Markets
Steve Tadelis, UC Berkeley
6.
Adverse Selection and Auction Design for Internet Display Advertising
Paul Milgrom, Stanford University
7.
Machine Learning in an Exchange Environment
R. Preston McAfee, Microsoft
8.
Online Advertisements and Online Algorithms
Nikhil Devanur, Microsoft Research
9.
On How Machine Learning and Auction Theory Power Facebook Advertising
Eric Sodomka, Facebook
10.
On-Line Systems with Long-Range Goals
Jon Kleinberg, Cornell University
11.
Decision Making at Scale: Platforms and Problems
Ashish Goel, Stanford University
12.
Strong Truthfulness in Multi-signal Peer Prediction with Overlapping Tasks
David Parkes, Harvard University
13.
The State of Techniques for Solving Large Imperfect-Information Games
Tuomas Sandholm, Carnegie Mellon University
14.
Security Games: Key Algorithmic Principles, Deployed Applications and Research Challenges(*)
Milind Tambe, University of Southern California
15.
Robust Inference for Games via Theoretical Guarantees
Denis Nekipelov, University of Virginia
16.
An Evolutionary Model of Economic Behavior, Bounded Rationality, and Intelligence
Andrew Lo, Massachusetts Institute of Technology
17.
The Design of Financial Exchanges: Some Open Questions at the Intersection of Econ and CS
Eric Budish, University of Chicago
18.
Algorithmic Trading and Machine Learning
Michael Kearns, University of Pennsylvania
19.
Incentive Auctions and Spectrum Repacking
Kevin Leyton-Brown, University of British Columbia
20.
Informational Substitutes and Complements for Prediction
Yiling Chen, Harvard University
21.
AGT and Machine Learning
Christos Papadimitriou, UC Berkeley
22.
Bitcoin's Incentive Structure
Aviv Zohar, Hebrew University of Jerusalem
23.
Dynamic Pricing in Ride-Sharing Platforms
Ramesh Johari, Stanford University
Workshop on Coding: From Practice to Theory
Feb. 9 – Feb. 13, 2015
Playlist: 24 videos
1.
Statistical Estimation on Graphs
Andrea Montanari, Stanford University
2.
Are Polar Codes Practical?
Warren Gross, McGill University
3.
The Edge Removal Problem
Mike Langberg, University at Buffalo, SUNY
4.
Multi-version Coding and Shared Memory Emulation
Viveck Cadambe, Pennsylvania State University
5.
Structural Properties of Erasure Codes for Streaming Communication
Ashish Khisti, University of Toronto
6.
New Problems in Coding Theory with Applications to Modern Data Storage and Memories
Lara Dolecek, UCLA
7.
A Theory of Polarization
Emre Telatar, École Polytechnique Fédérale de Lausanne
8.
Codes for Distributed Storage: Recent Results
P Vijay Kumar, Indian Institute of Science and USC
9.
Storage Codes and Data Retrieval
Emina Soljanin, Bell Labs
10.
STAIR Codes: A General Family of Erasure Codes for Tolerating Device and Sector Failures in ...
Patrick Pat-Ching Lee, Chinese University of Hong Kong
11.
Spatial Coupling vs. Block Coding: A Comparison
Daniel Costello, University of Notre Dame
12.
Coding for DNA-based Storage Systems
Olgica Milenkovic, University of Illinois, Urbana‑Champaign
13.
Coding Theory for Scalable Media Delivery
Michael Luby, Qualcomm Inc.
14.
Deletion Codes in the High-noise and High-rate Regimes
Carol Wang, Carnegie Mellon University
15.
Locally Recoverable Code Constructions and Some Extensions
Itzhak Tamo, Tel Aviv University
16.
List Recovery and Local Decoding of Expander Codes
Mary Wootters, Carnegie Mellon University
17.
Polar Codes over q-ary Alphabets and Polynomially Fast Convergence to Shannon Capacity
Ameya Velingker, Carnegie Mellon University
18.
A Student's View of Locally Repairable Codes
Arya Mazumdar, University of Minnesota
19.
Reed-Muller and Other Subcodes of the Sierpinski Matrix
Emmanuel Abbe, Princeton University
20.
Non-malleable Codes Against Constant Split-state Tampering
David Zuckerman, University of Texas, Austin
21.
Deterministic Rateless Codes for BSC
Guy Even, Tel Aviv University
22.
Sparse Superposition Codes for the AWGN Channel, Message Passing, Replica and Spatial Coupling
Florent Krzakala, École Normale Supérieure Paris
23.
Symmetric Product Codes
Henry Pfister, Duke University
24.
Gap to Capacity of Finite Length Polar Codes
David Burshtein, Tel Aviv University
Workshop on Tensor Networks and Simulations
Apr. 21 – Apr. 24, 2014
Playlist: 20 videos
1.
Invariants for Gapped Ground State Phases
Bruno Nachtergaele, UC Davis
2.
Wavelets, MERA, and Holography
Steve White, UC Irvine
3.
Bifurcation in Entanglement Renormalization Group Flow of a Gapped Spin Model
Jeongwan Haah, Massachusetts Institute of Technology
4.
Tensor Network States for Chiral Topological Phases
Nicholas Read, Yale University
5.
Topological Order in Projected Entangled Pair States
Norbert Schuch, RWTH Aachen
6.
Tensor Networks and Conformal Field Theory
German Sierra, Instituto de Física Teórica UAM/CSIC
7.
Einstein's Equations Starting from Qubits
Brian Swingle, Harvard University
8.
2D and 3D Topological Orders: Universal Wave Function Overlap and String Statistics
Xiao-Gang Wen, Perimeter Institute
9.
Exact Holographic Mapping and Emergent Space-time Geometry
Xiao-Liang Qi, Stanford University
10.
'Gauging' Time Reversal Symmetry in Tensor Network States
Xie Chen, UC Berkeley
11.
Efficient Classical Simulation of Many-body Systems with Local Integrals of Motion
Guifre Vidal, Perimeter Institute
12.
RG Fixed Points in PEPS
David Pérez-García, Universidad Complutense de Madrid
13.
MPO-injectivity and Topologically Ordered PEPS
Jutho Haegeman, Ghent University
14.
Walking Graph States: General Ansatz for Non-equilibrium Density Operators of Quantum Chains
Tomaz Prosen, University of Ljubljana
15.
A Polynomial-time Algorithm for Finding Ground States of Gapped Local Hamiltonians in 1D
Thomas Vidick, California Institute of Technology
16.
Tensor Networks and Quantum Algorithms
Zeph Landau, UC Berkeley
17.
Area Laws and Topological Order in a Many-body Localized State
Bela Bauer, Microsoft Research Station Q
18.
Doped Bosonic and Fermionic Resonating Valence Bond States
Didier Poilblanc, University of Toulouse
19.
The Algebraic Bethe Ansatz and Tensor Networks
Vladimir Korepin, Stony Brook University
20.
Symmetry and Topological Order in Quantum States
Frank Pollmann, Max Planck Institute, Dresden
Real Analysis Boot Camp
Sept. 9 – Sept. 13, 2013
Playlist: 13 videos
1.
Analytic Methods for Supervised Learning I
Adam Klivans, University of Texas, Austin
2.
Introduction to Analysis on the Discrete Cube I
Krzysztof Oleszkiewicz, University of Warsaw
3.
Inapproximability of Constraint Satisfaction Problems I
Johan Håstad, KTH Royal Institute of Technology
4.
Analytic Methods for Supervised Learning II
Adam Klivans, University of Texas, Austin
5.
Introduction to Analysis on the Discrete Cube II
Krzysztof Oleszkiewicz, University of Warsaw
6.
Inapproximability of Constraint Satisfaction Problems II
Johan Håstad, KTH Royal Institute of Technology
7.
Analytic Methods for Supervised Learning III
Adam Klivans, University of Texas, Austin
8.
Inapproximability of Constraint Satisfaction Problems III
Johan Håstad, KTH Royal Institute of Technology
9.
Analytic Methods for Supervised Learning IV
Adam Klivans, University of Texas, Austin
10.
Introduction to Analysis on the Discrete Cube III
Krzysztof Oleszkiewicz, University of Warsaw
11.
Inapproximability of Constraint Satisfaction Problems IV
Johan Håstad, KTH Royal Institute of Technology
12.
Inapproximability of Constraint Satisfaction Problems V
Johan Håstad, KTH Royal Institute of Technology
13.
Introduction to Analysis on the Discrete Cube IV
Krzysztof Oleszkiewicz, University of Warsaw