SimonsTV

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Quantum Advantage Without Structure
Jun. 2022
Mark Zhandry (NTT Research & Princeton University)
https://simons.berkeley.edu/talks/quantum-advantage-without-structure
Quantum and Lattices Joint Reunion Workshop
Meet the Fellows Welcome Event
Playlist: 51 videos
1.
Efficient Universal Estimators For Symmetric Property Estimation
Kirankumar Shiragur (Stanford University)
2.
Mechanism Design Via Machine Learning: Overfitting, Incentives, and Privacy
Ellen Vitercik (UC Berkeley)
3.
Fair And Reliable Machine Learning For High-Stakes Applications:approaches Using Information Theory
Sanghamitra Dutta (JP Morgan)
4.
Information, Learning, And Incentive Design For Societal Networks
Manxi Wu (UC Berkeley)
5.
Causal Inference And Data-Driven Decision-Making
Angela Zhou (UC Berkeley)
6.
Choosing Between Dags Can Be Hard
Richard Guo (University of Cambridge)
7.
Linear Predictors in Reinforcement Learning: ...
Andrea Zanette (UC Berkeley)
8.
Towards Credible and Effective Data-Driven Decision-Making
Angela Zhou (UC Berkeley)
9.
Primal--Dual Optimization and Application to Sampling
Adil Salim (Simons Institute)
10.
Data Efficient Methods for Reinforcement Learning
Xian Carrie Wu (Simons Institute)
11.
Distribution-Free Robustness
Sitan Chen (UC Berkeley)
12.
Pseudorandom Generators and Small-Space Derandomization
William Hoza (Simons Institute)
13.
Preconditioning and Locality in Algorithm Design
Jason Li (Simons and Berkeley)
14.
Query, Communication and Discrepancy
Makrand Sinha (Simons and Berkeley)
15.
Dynamic Linear Algebra
Jan van den Brand (Simons and Berkeley)
16.
Let's Sample Perfectly
Siddharth Bhandari (Simons Institute)
17.
A Complexity-Theoretic Perspective on Fairness
Michael P. Kim (UC Berkeley)
18.
Quantum Simulation of Relativistic Electronic Structure Hamiltonians
Torin Stetina (Simons Institute)
19.
Post-Quantum Succinct Arguments: Breaking the Quantum Rewinding Barrier
Fermi Ma (Simons and Berkeley)
20.
Quantum Necromancy and the Hardness of Observing Schrodinger's Cat
Yosi Atia (UC Berkeley)
21.
Short Proof of a Spectral Chernoff Bound for Local Hamiltonians
Nilin Abrahamsen (Simons Institute)
22.
Convex Programs, Computational Phase Transitions, and Robust Algorithms
Sam Hopkins (UC Berkeley)
23.
Understanding Statistical-to-Computational Gaps via Low-Degree Polynomials
Alex Wein (Simons Institute)
24.
Sharp Threshold for the Planted Matching Problem
Dana Yang (Cornell University)
25.
Correlation Adjusted Debiasing (CAD): Debiasing the Lasso with Inaccurate Covariate Model
Michael Celentano (Stanford University)
26.
On Query-to-Communication Lifting for Adversary Bounds
Anurag Anshu (UC Berkeley)
27.
One-Way Functions Imply Secure Computation in a Quantum World
Andrea Coladangelo (UC Berkeley)
28.
Quantum Algorithms for the Mean Estimation Problem
Yassine Hamoudi (Simons Institute)
29.
Distribution-Free, Risk-Controlling Prediction Sets
Stephen Bates (UC Berkeley)
30.
Learning Under Requirements
Luiz Chamon (UC Berkeley)
31.
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS
Lin Chen (Simons Institute)
32.
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent
Spencer Frei (UC Berkeley)
33.
On Implicit Regularization in Deep Learning
Wei Hu (UC Berkeley)
34.
Equilibrium Computation and the Foundations of Multi-Agent Machine Learning
Emmanouil Zampetakis (UC Berkeley)
35.
A Fast Algorithm for Optimal Transport
Matt Jacobs (Purdue)
36.
Approximating Distributions Using Well-Conditioned Normalizing Flows
Holden Lee (Duke University)
37.
Iterative Methods and High-Dimensional Statistics
Kevin Tian (Stanford University)
38.
Geometric Methods for Machine Learning and Optimization
Melanie Weber (Oxford, Mathematical Institute)
39.
Graphical Optimal Transport and Its Applications
Yongxin Chen (Georgia Institute of Technology)
40.
Quantum Information Meets Cryptography
Qipeng Liu (Simons Institute)
41.
On Implicit Regularization in Deep Learning
Wei Hu (UC Berkeley)
42.
Optimal Transport for Inverse Problems
Yunan Yang (New York University)
43.
On the Power of Preconditioning in Sparse Linear Regression
Frederic Koehler (UC Berkeley)
44.
Causal Inference With Corrupted Data: Measurement Error, Missing Values, Discretization, And...
Rahul Singh (MIT)
45.
Fundamental Limits Of Learning In Data-Driven Problems
Yanjun Han (Simons Institute)
46.
Algorithmic Advances For The Design And Analysis Of Randomized Control Trials
Christopher Harshaw (Yale)
47.
Multi-Agent Reinforcement Learning In Stochastic Games: From Alphago To Robust Control
Kaiqing Zhang (MIT)
48.
Learning In The Presence Of Strategic Agents: Dynamics, Equilibria, And Convergence
Eric Mazumdar (Caltech)
49.
Causal Inference For Socio-Economic & Engineering Systems
Anish Agarwal (MIT EECS)
50.
Teamwork Makes The Von Neumann Work: Two Team Zero Sum Games
Emmanouil Vlatakis (Columbia University)
Reinforcement Learning using Generative Models for Continuous State and Action Space Systems
Dec. 2020
Rahul Jain (USC)
https://simons.berkeley.edu/talks/tbd-241
Reinforcement Learning from Batch Data and Simulation
Mathematics of Online Decision Making
Playlist: 25 videos
1.
Online Multiserver Convex Chasing and Optimization
Yuval Rabani (Hebrew University of Jerusalem)
2.
Multi-Task Optimal Experiment Design
Steffen Grunewalder (Lancaster University)
3.
Selfish Robustness and Equilibria in Multi-Player Bandits
Vianney Perchet (ENSAE & Criteo AI Lab)
4.
Pure Exploration Problems
Wouter Koolen (Centrum Wiskunde & Informatica)
5.
Gradient Descent-Ascent Provably Converges to Strict Local Minmax Equilibria with a Finite Timescale
Lillian Ratliff (University of Washington)
6.
Learning Outcomes in Queueing Systems
Eva Tardos (Cornell)
7.
Pandora's Box with Correlations: Learning and Approximation
Shuchi Chawla (University of Wisconsin, Madison)
8.
Regret Minimization for Stochastic Shortest Paths
Yishay Mansour (Tel Aviv University)
9.
Robust Algorithms for Secretaries and Bandits
Anupam Gupta
10.
The Non-Stochastic Control Framework
Naman Agarwal (Google)
11.
Competitive Algorithms for Online Control
Yisong Yue (Caltech)
12.
A Unifying View of Optimism in Episodic Reinforcement Learning
Ciara Pike-Burke (Imperial College London)
13.
On the Complexity of Learning Good Policies With and Without Rewards
Emilie Kaufmann (CNRS & University of Lille)
14.
Model-Based Reinforcement Learning with Value-Targeted Regression
Mengdi Wang (Princeton University)
15.
A Generalization Bound for Online Variational Inference
Pierre Alquier (Riken AIP)
16.
Beating the Curse of Dimensionality in High-Dimensional Optimal Stopping
David Goldberg (Cornell ORIE)
17.
Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization
Negin Golrezaei (MIT)
18.
Country-Scale Bandit Implementation for Targeted COVID-19 Testing
Hamsa Bastani (Wharton School of the University of Pennsylvania)
19.
Multi-Player Multi-Armed Bandit: Can We Still Collaborate at Homes Without "Zoom"?
Yuanzhi Li (Carnegie Mellon University)
20.
Multiplayer Bandit Learning - From Competition to Cooperation
Simina Branzei (Purdue University)
21.
Representation Learning and Exploration in Reinforcement Learning
Akshay Krishnamurthy (Microsoft Research)
22.
Corruption Robust Exploration in Episodic Reinforcement Learning
Aleksandrs Slivkins (Microsoft Research NYC)
23.
On the Global Convergence and Approximation Benefits of Policy Gradient Methods
Daniel Russo (Columbia University)
24.
An Alternative Softmax Operator for Reinforcement Learning
Michael Littman (Brown University)
25.
What Are the Statistical Limits of Offline Reinforcement Learning With Function Approximation?
Sham Kakade (University of Washington & Microsoft Research)
Fast Reinforcement Learning With Generalized Policy Updates
Oct. 2020
Doina Precup (McGill Univeristy & MILA / DeepMind)
https://simons.berkeley.edu/talks/tbd-224
Deep Reinforcement Learning
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
Planning and Markov Decision Processes Part 1 (reupload)
Sep. 2020
Csaba Szepesvari (University of Alberta, Google DeepMind) & Mengdi Wang (Princeton University, Google DeepMind)
https://simons.berkeley.edu/talks/tbd-160
Theory of Reinforcement Learning Boot Camp
WIT: Women in Theory (of Computer Science) — I Will Survive!
May. 2020
The Women of Theory of Computer Science rock to our version of I Will Survive!
WIT: https://womenintheory.wordpress.com/


I Will Survive
Lyrics: Avi Wigderson (IAS)

At first I was afraid, I was petrified
I worried I could never fit this proof on just one slide
But then I spent so many nights thinking why it is so long
And I grew strong
And learned exactly what went wrong

A problem wor-thy, of attack
Just proves its worth by vigorously fighting back
I should have used error correction, should have sampled yet again
I should have stayed the course and found there is so much that I can gain

So do come back, problems galore
I am much more ready to attack you than I was before
I’ll fight you guiltless when at work, forget you guiltless when at home
And if you’re fun then in the pastures of the TCS we’ll roam

So I’ll survive, and I will thrive,
By Nash’s equilibrium, there must be balance to my life
I’ve got all my life to live,
And I’ve got all my Math to give
So I’ll survive,
and I will thrive, hey, hey

It took all the strength I had, I was nearly spent,
Trying hard to mend, the errors, in my argument
I put each pigeon in its hole, consulted every oracle
My upper bound
Turned up below my lower bound

Then I came up, with something new
I thought outside the blackbox, found what others never knew
That polynomials with a small degree have small number of roots
That few cryptogra-phic assumptions no one’s likely to dispute

So do come back, problems galore
I am much more ready to attack you than I was before
As I have wit and I have WIT and having both is pretty neat
Indeed a convex combination that is very hard to beat

So I’ll survive, and I will thrive,
Because (in theory, at least) this is a perfect life
You pick the problems that you love
To fit your brain just like a glove
So I’ll survive,
and I will thrive, hey, hey

Singers:
Dahlia Malkhi (Calibra, Facebook)
Elette Boyle (IDC, Israel)
Irit Dveer Dinur (Weizmann Institute, Israel)
Julia Chuzhoy (Toyota Technological Institute at Chicago, USA)
Katrina Ligett (Hebrew University, Israel)
Keren Censor-Hillel (Technion, Israel)
Lisa Zhang (Bell-Labs, USA)
Mary Wooters (Stanford University, USA)
Michal Feldman (Tel-Aviv University, Israel)
Nicole Immorlica (Microsoft Research, New England, USA)
Orna Kupferman (Hebrew University, Israel)
Rebecca Wright (Barnard College, USA)
Ronitt Rubinfeld (MIT, USA)
Shafi Goldwasser (Simons Institute at UC Berkeley, USA)
Shubhangi Saraf (Rutgers University, USA)
Shuchi Chawla (University of Wisconsin, Madison, USA)
Sofya Raskhodnikova (Boston University, USA)
Tal Malkin (Columbia University, USA)
Tal Rabin (Algorand Foundation, USA)
Yael Tauman Kalai (Microsoft Research, New England, USA)
Perception as Inference: The Brain and Computation | Theory Shorts
Apr. 2020
Theory Shorts is a documentary web series that explores topics from the Simons Institute’s research programs.

Episode 1, “Perception as Inference: The Brain and Computation,” explores the computational processes by which the brain builds visual models of the external world, based on noisy or incomplete data from patterns of light sensed on the retinae.

HOST
Bruno Olshausen

DIRECTOR
Christoph Drösser

EDITOR
Michaelle McGaraghan

PRODUCERS
Kristin Kane
Michaelle McGaraghan

SCIENTIFIC ADVISOR
Shafi Goldwasser

ANIMATORS
Caresse Haaser
Christoph Drösser
Lukas Engelhardt

GRAPHIC DESIGNER
Barry Bödeker

VIDEOGRAPHERS
Drew Mason
Omied Far
Michaelle McGaraghan
Matt Beardsley

PRODUCTION ASSISTANTS
Christine Wang
Bexia Shi
Lior Shavit

THEME MUSIC
“Plastic” by Purple Moons
Courtesy of Marmoset in Portland, Oregon

OTHER MEDIA COURTESY OF
Bruce Damonte
Arash Fazl
Anders Garm
Jean Lorenceau and Maggie Shiffrar
Beau Lotto
A. L. Yarbus
Bruno Olshausen
videocobra / Pond5
BlackBoxGuild / Pond5
nechaevkon / Pond5
DaveWeeks / Pond5
CinematicStockVideo / Pond5
BananaRepublic / Pond5
MicroStockTube / Pond5
shelllink / Pond5
AudioQuattro / Envato Market
HitsLab / Envato Market
FlossieWood / Envato Market
plaincask / Envato Market
MusicDog / Envato Market
Loopmaster / Envato Market
Ryokosan / Envato Market
Images used under license from Shutterstock.com


© Simons Institute for the Theory of Computing, 2019
Quantum Seminars
Playlist: 6 videos
1.
Secure Multi-party Quantum Computation with a Dishonest Majority
Chris Schaffner (University of Amsterdam & QuSoft)
2.
Quantum Speedup for Graph Sparsification, Cut Approximation and Laplacian Solving
Simon Apers (INRIA)
3.
Robust Polynomial Method and a Sub-Volume Law for Locally Gapped Frustration-Free Spin Systems
Anurag Anshu (IQC)
4.
Quantum Coupon Collector
Ronald de Wolf (QuSoft, CWI and University of Amsterdam)
5.
A Theory of Trotter Error
Yuan Su (UMD)
6.
Quantum Linear Algebra With Near-Optimal Complexities
Lin Lin (UC Berkeley)
Prospects for Quantum Computers in Chemistry and Materials Science
Feb. 2020
Garnet Chan (Caltech)
https://simons.berkeley.edu/talks/tbd-129
Quantum Algorithms
Theoretic Methods for Privacy
Playlist: 7 videos
1.
(Part 1) Operationally Motivating IT Leakage Measures – A Path to Maximal Leakage
Aaron Wagner (Cornell University)
2.
Unifying IT Leakage Measures via the Lens of Adversarial Learning
Lalitha Sankar (Arizona State University)
3.
Fairness: IT Approaches
Flavio du Pin Calmon (Harvard University)
4.
Generative Adversarial Models for Privacy and Fairness
Peter Kairouz (Google AI)
5.
Information Bottleneck and Privacy Funnel: IT Lens on Privacy-Utility Tradeoff (PUT) Problems
Flavio du Pin Calmon (Harvard University)
6.
(Part 2) Operationally Motivating IT Leakage Measures – A Path to Maximal Leakage (Part 2)
Aaron Wagner (Cornell University)
7.
Mechanism Design: Sample Complexity and Loss Functions
Lalitha Sankar (Arizona State University)
The Next Wave in Networking Research
Playlist: 9 videos
1.
Opening Remarks
Derya Cansevar (ARO)
2.
Incentive Compatibility in Stochastic Dynamic Systems
P.R. Kumar (Texas A&M University)
3.
Excursions of Max-weight Dynamics
John Tsitsiklis (MIT)
4.
A Surge-type Pricing in Ridesharing Systems is Stability Optimal
Costas Courcoubetis (SUTD)
5.
Interference Queuing Networks
Francois Baccelli (UT Austin)
6.
The Future Quantum Internet: Challenges
Don Towsley (UMass - Amherst)
7.
The Future of Wireless and What It Will Enable
Andrea Goldsmith (Stanford University)
8.
Emerging Trends & Applications in Communication Networks
Moderator: R. Srikant (UIUC) Panelists: Mohammad Alizadeh (MIT), Balaji Prabhakar (Stanford), and Pramod Viswanath (UIUC)
9.
Networking Theory: A Personal Perspective
Jean Walrand (UC Berkeley)
Targeted Discovery in Brain Data
This workshop will focus on the problem of inferring structure from neuroscience data, including the following specific themes:
Playlist: 13 videos
1.
Three Principles of Data Science: Predictability, Stability, and Computability
Bin Yu, UC Berkeley
2.
Private video
3.
Decoding and Modeling the Brain’s Navigational Circuits
Ila Fiete, University of Texas at Austin
4.
Cortical Travelling Waves: Mechanisms and Computational Principles
Terry Sejnowski, Salk Institute
5.
Computational Geometry of Cortical Networks: Manifold-Embedding Analysis of Multi-Site Recordings...
Clay Reid, Allen Institute for Brain Science
6.
Private video
7.
Automated Reconstruction of Synaptic-Resolution Neural Wiring Diagrams Using...
Viren Jain, Google
8.
Private video
9.
The Information Bottleneck Theory of Deep Neural Networks...
Naftali Tishby, Hebrew University of Jerusalem
10.
Cortical Areas Interact through a Communication Subspace
Christian Machens, Champalimaud Research
11.
Similarity Alignment: A Missing Link Between Structure, Function and Algorithms
Mitya Chklovskii, Simons Foundation
12.
Tracing, Intercepting and Modeling Communication in the Brain
Fritz Sommer, UC Berkeley
13.
Inferring Single-Trial Neural Population Dynamics Using Sequential Auto-Encoders
David Sussillo, Google
Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference
May. 2017
David Duvenaud, University of Toronto
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/david-duvenaud-2017-5-1
Spotlight Talk: Convolutional Dictionary Learning through Tensor Factorization
Mar. 2017
Furong Huang, UC Irvine
Representation Learning
https://simons.berkeley.edu/talks/furong-huang-2017-03-29
Evaluating Neural Network Representations Against Human Cognition
Mar. 2017
Tom Griffiths, UC Berkeley
Representation Learning
https://simons.berkeley.edu/talks/tom-griffiths-2017-3-29
Network Biology
Apr. 11 – Apr. 15, 2016
Playlist: 28 videos
1.
Using Molecular Networks to Seed Predictive Hierarchies of Cell Systems
Trey Ideker, UC San Diego
2.
Uncertainty in Biological Networks; Challenges and Opportunities
Tamer Kahveci, University of Florida
3.
Diffusion-Based Metrics for Biological Network Analysis
Lenore Cowen, Tufts University
4.
Sampling the Low End of Affinity
Marcus Noyes, New York University
5.
Systematic Functional Annotation of the 2016 Yeast Genetic Interaction Network
Anastasia Baryshnikova, Princeton University
6.
Pathways, Centrality, and Regulation in Developmental Disorders
Donna Slonim, Tufts University
7.
On Beyond Genome: Opportunities and Challenges Using the Other Omics for Precision Medicine
Ernest Fraenkel, Massachusetts Institute of Technology
8.
Algorithms for Combinatorial Problems Arising in the Analysis of Cancer Mutation Networks
Fabio Vandin, University of Padova
9.
Network Analysis of Cancer Mutations
Ben Raphael, Brown University
10.
Fathoming Cancer by Interrogating Integrated Interactomes
Igor Jurisica, Princess Margaret Cancer Centre
11.
Network Data Integration Enables Precision Medicine
Natasa Przulj, University College London
12.
Transcriptional Enhancers - Looking out for the Genes and Each Other
Sridhar Hannenhalli, University of Maryland
13.
Dynamics of 3D Enhancer-Gene Associations Across Diverse Human Cell Types and Tissues
Anshul Kundaje, Stanford University
14.
Profiling Cell/Tissue Specific Gene Regulatory Networks
Louxin Zhang, National University of Singapore
15.
Variation of Regulatory Networks Across Organisms
Mona Singh, Princeton University
16.
Spatial Gene Expression Patterns in Drosophila
Bin Yu, UC Berkeley
17.
Network Perspective on Expression Changes and Expression Noise in D. melanogaster Chromosomal Deleti
Teresa Przytycka, National Institutes of Health
18.
Cross-Species Interactome Mapping Reveals Network Evolution Principles from Yeasts to Human
Haiyuan Yu, Cornell University
19.
Moving Beyond Local Patterns: New Ways to Reason about Biological Network Comparisons
Ben Hescott, Tufts University
20.
Data Driven "Pathway" Analysis with ADAGE
Casey Greene, University of Pennsylvania
21.
Synthesis, Simplification and Analysis of Biological Networks Using Higher Order Topological Connect
Bhaskar DasGupta, University of Illinois at Chicago
22.
Network Propagation as a Tool for Deciphering Disease Mechanisms
Roded Sharan, Tel-Aviv University
23.
Dynamic and Integrative Biological Network Research of Aging
Tijana Milenkovic, University of Notre Dame
24.
Efficiently Enumerating All Connected Induced Subgraphs of a Large Molecular Network
Mehmet Koyuturk, Case Western Reserve University
25.
Identifying Disease Mechanisms Using Gene Networks as Priors
Anna Goldberg, University of Toronto
26.
Simple Models and Exact Algorithms for Computing Network Modules
Gunnar Klau, CWI, Amsterdam
27.
Local Similarity Analysis (LSA) of Time Series Data and Applications to Metagenomics
Fengzhu Sun, University of Southern California
28.
Module Discovery in 3-way Data
Ron Shamir, Tel-Aviv University
Quantum Hamiltonian Complexity Reunion Workshop
May 4 – May 8, 2015
Playlist: 6 videos
1.
Spacetime, Entropy, and Quantum Information
Patrick Hayden, Stanford University
2.
Black Holes, Firewalls, and Chaos
Stephen Shenker, Stanford University
3.
Tensor Networks and Gravity
Mike Zaletel, Microsoft Research, Station Q
4.
MERA and Holography
Shinsei Ryu, University of Illinois, Urbana‑Champaign
5.
Quantum Error Correction
Daniel Gottesman, Perimeter Institute
6.
QEC in AdS/CFT
Daniel Harlow, Princeton University
Workshop on Tensors in Computer Science and Geometry
Nov. 10 – Nov. 14, 2014
Playlist: 24 videos
1.
The Average Number of Critical Rank-One Approximations to a Tensor
Jan Draisma, Technische Universiteit Eindhoven
2.
The Geometry of the Identifiability of Tensors
Luca Chiantini, University of Siena
3.
Elementary Results on Maximal, Generic and Typical Ranks
Grigoriy Blekherman, Georgia Institute of Technology
4.
Characters of Equivariant D-modules on Veronese Cones
Claudiu Raicu, University of Notre Dame
5.
Siegel's Theorem, Edge Coloring, and a Holant Dichotomy
Jin-Yi Cai, University of Wisconsin-Madison
6.
NP-hardness of Nuclear Norms for Tensors
Shmuel Friedland, University of Illinois at Chicago
7.
Matrix Multiplication: Coppersmith-Winograd and Beyond
Virginia Vassilevska Williams, Stanford University
8.
Towards a Complete Classification of Multipartite Entanglement
Gilad Gour, University of Calgary
9.
Entanglement Polytopes
David Gross, University of Freiburg
10.
Tensors and Complexity in Quantum Information Science
Akimasa Miyake, University of New Mexico
11.
Geometry and Upper Bounds for the Complexity of Matrix Multiplication
Joseph Landsberg, Texas A&M University
12.
Approaches to Bounding the Exponent of Matrix Multiplication
Chris Umans, California Institute of Technology
13.
Powers of Tensors and Fast Matrix Multiplication
Francois Le Gall, University of Tokyo
14.
Progress on Strassen's Conjecture
Enrico Carlini, Monash University and Poltiecnico di Torino
15.
On Strassen's Conjecture
Elisa Postinghel, University of Leuven
16.
Rank, Border Rank, Smoothable Rank and Cactus Rank
Jaroslaw Buczynksi, Polish Academy of Sciences
17.
Cactus Varieties of Cubic Forms: Apolar Local Artinian Gorenstein Rings
Alessandra Bernardi, Università di Bologna
18.
Orthogonal Tensor Decomposition
Elina Robeva, UC Berkeley
19.
Tensor Decomposition, Sparse Representations and Applications
Bernard Mourrain, INRIA Sophia Antipolis
20.
Tensor Decompositions for Estimating Latent Variable Models
Sham Kakade, Microsoft Research New England
21.
Learning Mixture of Gaussians using Moment Tensor
Rong Ge, Microsoft Research
22.
Uniqueness of Tensor Decomposition and Identifiability in Mixture Models
Aditya Bhaskara, Google Research New York
23.
Smoothed Analysis of Tensor Decompositions
Aravindan Vijayaraghavan, Courant Institute NYU
24.
Generalized Singular Values
Harm Derksen, University of Michigan