Spring 2022

Feb 4th – Dan Mikulincer, (MIT)
The Brownian transport map

Feb 18th – Ilias Zadik, (MIT)
On the power of Lenstra-Lenstra-Lovasz in noiseless inference

Feb 25th – *POSTPONED* Yue Lu, (Harvard University)

Mar 4th – Edgar Dobriban, (University of Pennsylvania)
Optimal Testing for Calibration of Predictive Models

Mar 11th – Isaiah Andrews, (Harvard University)
Inference on Winners

Mar 18th – Subhabrata Sen (Harvard University)
Mean-field approximations for high-dimensional Bayesian Regression

Apr 8th – Li-Yang Tan, (Stanford University)
The query complexity of certification

April 15th – Caroline Uhler, (MIT)

Apr 22nd – Yue M. Lu, (Harvard University)
Learning with Random Features and Kernels: Sharp Asymptotics and Universality Laws

Apr 29th – Giedre Lideikyte-Huber and Marta Pittavino (University of Geneva)
Is quantile regression a suitable method to understand tax incentives for charitable giving? Case study from the Canton of Geneva, Switzerland

May 6th – Jonathan Weare (New York University)
Sampling rare events in Earth and planetary science

Fall 2021

Sept 17       Lorenzo Rosasco (MIT/Universita’ di Genova) –
                    Interpolation and Learning with Scale Dependent Kernels

Sept 24       Boaz Barak (Harvard University)
                    Representation and generalization 

Oct 1           Devavrat Shah (MIT)
                    Causal Matrix Completion

Oct 8           Yihong Wu (Yale University)
                    Recent Results in Planted Assignment Problems

Oct 15         Yuting Wei (Wharton School at the University of Pennsyvania)
                     Breaking the Sample Size Barrier in Reinforcement Learning

Oct 22         Kevin Jamieson (University of Washington)
                     Instance Dependent PAC Bounds for Bandits and Reinforcement Learning

Oct 29         Ronen Eldan (Weizmann Institute of Science/Princeton University)
                    Revealing the Simplicity of High-Dimensional Objects via Pathwise Analysis

Nov 5          Morgane Austern (Harvard University)
                   Asymptotics of learning on dependent and structured random objects

Nov 12        Cynthia Rush (Columbia University)
                    Characterizing the Type 1-Type 2 Error Trade-off for SLOPE

Nov 19        Pragya Sur (Harvard)
                    Precise high-dimensional asymptotics for AdaBoost via max-margins & min-norm

Dec 3          Jesse Thaler (MIT)
                   The Geometry of Particle Collisions: Hidden in Plain Sight

Spring 2021

Feb 19        Jerry Li (Microsoft Research) – Faster and Simpler Algorithms for List Learning

Feb 26        Yury Polyanskiy (MIT) – Self-regularizing Property of Nonparametric
                    Maximum Likelihood Estimator in Mixture Models

Mar 5        Bhaswar B. Bhattacharya (University of Pennsylvania – Wharton School) –
                   Detection Thresholds for Distribution-Free Non-Parametric Tests:
                    The Curious Case of Dimension 8

Mar 12       James Robins (Harvard) – On nearly assumption-free tests of nominal
                   confidence interval coverage for causal parameters estimated by machine learning

Mar 19      Daniel Roy (University of Toronto) – Relaxing the I.I.D. Assumption: Adaptively
                  Minimax Optimal Regret via Root-Entropic Regularization

Mar 26      Vladimir Vovk (Royal Holloway, University of London) – Testing the I.I.D.  
                   assumption online

Apr 2        Thibaut Le Gouic (MIT) – Sampler for the Wasserstein barycenter

Apr 9        Suriya Gunasekar (Microsoft Research) – Functions space view of linear multi-
                  channel convolution networks with bounded weight norm

Apr 16      Eric Laber (Duke University) – Sample size considerations in precision medicine

Apr 23      Hilary Finucane (Broad Institute) – Prioritizing genes from genome-wide
                  association studies

May 14     Ann Lee (Carnegie Mellon University)Likelihood-Free Frequentist Inference


Fall 2020

Sep 11
Gesine Reinert (University of Oxford)

Stein’s method for multivariate continuous distributions and applications

Sep 18
Caroline Uhler (MIT)

Causal Inference and Overparameterized Autoencoders in the Light of Drug Repurposing for SARS-CoV-2

Sep 25
Dylan Foster (MIT)

Separating Estimation from Decision Making in Contextual Bandits

Oct 2
Richard Nickl (University of Cambridge)

Bayesian inverse problems, Gaussian processes, and partial differential equations

Oct 9
Gábor Lugosi (Pompeu Fabra University)

On Estimating the Mean of a Random Vector

Oct 16
Carola-Bibiane Schönlieb (University of Cambridge)

Data driven variational models for solving inverse problems

Oct 23
Jose Blanchet (Stanford University)

Statistical Aspects of Wasserstein Distributionally Robust Optimization Estimators

Oct 30
Alessandro Rinaldo (Carnegie Mellon University) POSTPONED
Nov 6
Daniela Witten (University of Washington)

Valid hypothesis testing after hierarchical clustering

Nov 13
Mary Wootters (Stanford University)

Sharp Thresholds for Random Subspaces, and Applications

Nov 20
Arnaud Doucet (University of Oxford)

Perfect Simulation for Feynman-Kac Models using Ensemble Rejection Sampling

Dec 4
Rong Ge (Duke University)

A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Net


Spring 2020


Feb 7 Weijie Su (University of Pennsylvania)

Gaussian Differential Privacy, with Applications to Deep Learning

Feb 14 Xiaohui Chen (University of Illinois at Urbana-Champaign)

Diffusion K-means Clustering on Manifolds: provable exact recovery via semidefinite relaxations

Feb 21 Rina Foygel Barber (University of Chicago)

Predictive Inference with the Jackknife+

Feb 28 Kavita Ramanan (Brown University)

Tales of Random Projections

Mar 6
Suriya Gunasekar (Microsoft Research) POSTPONED
Mar 13 Dan Spielman (Yale University) POSTPONED
Mar 20 Hilary Finucane (Broad Institute of MIT) POSTPONED
Apr 10 Alessandro Rinaldo (Carnegie Mellon University) POSTPONED
Apr 10 Jonathan Niles-Weed (New York University)

Matrix Concentration for Products

Apr 17 Ery Arias-Castro (University of California, San Diego)

On the Estimation of Distances Using Graph Distances

Apr 24 Jon Wellner (University of Washington) POSTPONED
Apr 24 Sebastien Bubeck (Microsoft Research) online
May 1 Annie Liang (University of Pennsylvania)
May 1 Alexandre d’Aspremont (ENS, CNRS)

Naive Feature Selection: Sparsity in Naive Bayes

May 8 Gerard Ben Arous (New York University, Courant Institute) POSTPONED

Fall 2019

IDS.190 – Topics in Bayesian Modeling and Computation

Slides related to this course are available for MIT students and faculty here.

Fall 2019

Sep 6 Tengyuan Liang (University of Chicago)
GANs, Optimal Transport, and Implicit Density Estimation
Sep 20 Samory Kpotufe (Columbia)
Some New Insights On Transfer Learning
Sep 27 Adam Klivans (UT Austin)
Frontiers of Efficient Neural-Network Learnability
Oct 11 Christopher Moore (Santa Fe Institute)
The Planted Matching Problem
Oct 18 Stanislav Minsker (USC)
Towards Robust Statistical Learning Theory
Oct 25 Maria-Pia Victoria-Feser (University of Geneva)
Accurate Simulation-Based Parametric Inference in High Dimensional Settings
Nov 8 Yudong Chen (Cornell)
SDP Relaxation for Learning Discrete Structures: Optimal Rates, Hidden Integrality, and Semirandom Robustness
Nov 15 Lenka Zdeborova (Institut de Physique Théorique, CNRS)
Understanding Machine Learning with Statistical Physics
Nov 22 Tamara Broderick (MIT)
Automated Data Summarization for Scalability in Bayesian Inference
Dec 6 Simon Tavaré (Columbia)
Inferring the Evolutionary History of Tumors

Spring 2019

Feb 1 Andrea Montanari (Stanford)
Optimization of the Sherrington-Kirkpatrick Hamiltonian
Feb 8 Polina Golland (MIT CSAIL)
Medical Image Imputation
Feb 15 Zhou Fan (Yale)
TAP free energy, spin glasses, and variational inference
Feb 22 Nike Sun (MIT)
Capacity lower bound for the Ising perceptron
Mar 1 Eric Kolaczyk (Boston University)
Why Aren’t Network Statistics Accompanied By Uncertainty Statements?
Mar 8 Aditya Guntuboyina (UC Berkeley)
Univariate total variation denoising, trend filtering and multivariate Hardy-Krause variation denoising
Mar 15 Alex Belloni (Duke University)
Subvector Inference in Partially Identified Models with Many Moment Inequalities
Mar 22 Eliran Subag (New York University)
Optimization of random polynomials on the sphere in the full-RSB regime
Apr 12 Aaditya Ramdas (Carnegie Mellon University)
Exponential line-crossing inequalities
Apr 19 Dylan Foster (MIT)
Logistic Regression: The Importance of Being Improper
Apr 26 Chao Gao (University of Chicago)
Robust Estimation: Optimal Rates, Computation and Adaptation
May 3 Tracy Ke (Harvard)
Optimal Adaptivity of Signed-Polygon Statistics for Network Testing (Tracy Ke, Harvard University)
May 10 Will Perkins (University of Illinois at Chicago)
Counting and sampling at low temperatures

Fall 2018

Sep 7 Dejan Slepcev (MIT)
Varational problems on random structures and their continuum limits
E18-304, 11am – 12pm
Sep 14 Gregory Wornell (MIT)
An information-Geometric View of Learning in High Dimensions
E18-304, 11am – 12pm
Sep 21 Boaz Nadler (Weizmann Institute)
Unsupervised Ensemble Learning
E18-304, 11am – 12pm
Sep 28 Jingbo Liu (MIT)
Reverse hypercontractivity beats measure concentration for information theoretic converses
E18-304, 11am – 12pm
Oct 5 Tselil Schramm (Harvard University)
Efficient Algorithms for the Graph Matching Problem in Correlated Random Graphs
E18-304, 11am – 12pm
Oct 12 John Duchi (Stanford University)
Locally private estimation, learning, inference, and optimality
E18-304, 11am – 12pm
Oct 19 Aukosh Jagannath (Harvard)
Algorithmic thresholds for tensor principle component analysis
E18-304, 11am – 12pm
Oct 26 Alan Frieze (Carnegie Mellon University)
On the cover time of two classes of graph
E18-304, 11am – 12pm
Nov 2 Sumit Mukherjee (Columbia University)
Joint estimation of parameters in Ising Model
E18-304, 11am – 12pm
Nov 9 Zongming Ma (University of Pennsylvania)
Optimal hypothesis testing for stochastic block models with growing degrees
E18-304, 11am – 12pm
Nov 16 Lucas Janson (Harvard University)
Model-X knockoffs for controlled variable selection in high dimensional nonlinear regression
E18-304, 11am – 12pm
Nov 30 Vladimir Koltchinskiib (Georgia Tech)
Bias Reduction and Asymptotic Efficiency in Estimation of Smooth Functionals of High-Dimensional Covariance
E18-304, 11am – 12pm
Dec 7 Guy Breslerb (MIT)
Reducibility and Computational Lower Bounds for Some High-dimensional Statistics Problems
E18-304, 11am – 12pm
Dec 14 Lutz Warnke (Georgia Tech)
Large girth approximate Steiner triple systems
E18-304, 11am – 12pm

Spring 2018

Feb 2 Sahand Negahban (Yale)
Connections between structured estimation and weak submodularity
E18-304, 11am – 12pm
Feb 9 Garvesh Raskutti (Wisconsin)
Variable selection using presence-only data with applications to biochemistry
E18-304, 11am – 12pm
Feb 16 Arnak Dalalyan CREST (Paris)
User-friendly guarantees for the Langevin Monte Carlo
E18-304, 11am – 12pm
Feb 23 Nathan Srebro-Bartom (TTI-Chicago)
Optimization’s Implicit Gift to Learning: Understanding Optimization Bias as a Key to Generalization
E18-304, 11am – 12pm
Mar 2 Alexandra Carpentier (Potsdam)
One and two sided composite-composite tests in Gaussian mixture models
E18-304, 11am – 12pm
Mar 9 Afonso Bandeira (NYU)
Statistical estimation under group actions: The Sample Complexity of Multi-Reference Alignment
E18-304, 11am – 12pm
Mar 16 David Sontag (MIT)
When Inference is tractable
E18-304, 11am – 12pm
Mar 23 Johannes Schmidt Hieber (Leiden)
Statistical theory for deep neural networks with ReLU activation function
E18-304, 11am – 12pm
Apr 6 Jianqing Fan (Princeton)
Optimality of Spectral Methods for Ranking, Community Detections and Beyond
E18-304, 11am – 12pm
Apr 13 Subrabatha Sen (Microsoft)
Testing degree corrections in Stochastic Block Models
E18-304, 11am – 12pm
Apr 27 Genevera Allen (Rice)
Inference, Computation, and Visualization for Convex Clustering and Biclustering
E18-304, 11am – 12pm
May 4 Ohad Shamir (Weizman)
Size-Independent Sample Complexity of Neural Networks
E18-304, 11am – 12pm
May 11 Adel Javanmard (USC)
Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions
E18-304, 11am – 12pm
May 25 Hariharan Narayanan (MIT/UW)
Fitting a putative manifold to noisy data
E18-304, 11am – 12pm

Fall 2017

Sep 8 Andrej Risteski (Princeton University)
New provable techniques for learning and inference in probabilistic graphical models
E18-304, 11am – 12pm
Sep 15 Yury Polyanskiy (MIT)
Sample complexity of population recovery
E18-304, 11am – 12pm
Sep 22 Amir Dembo (Stanford University)
Walking within growing domains: recurrence versus transience
E18-304, 11am – 12pm
Sep 29 Jelani Nelson (Harvard University)
Optimal lower bounds for universal relation, and for samplers and finding duplicates in streams
E18-304, 11am – 12pm
Oct 6 Youssef Marzouk (MIT)
Transport maps for Bayesian computation
E18-304, 11am – 12pm
Oct 13 Galen Reeves (Duke University)
Additivity of Information in Deep Generative Networks: The I-MMSE Transform Method
E18-304, 11am – 12pm
Oct 19 John Cunningham (Columbia)
Structure in multi-index tensor data: a trivial byproduct of simpler phenomena?
E18-304, 11am – 12pm
Oct 20 Sayan Mukherjee (Duke)
Inference in dynamical systems and the geometry of learning group actions
E18-304, 11am – 12pm
Oct 27 Amit Daniely (Google)
On Learning Theory and Neural Networks
E18-304, 11am – 12pm
Nov 1 Pierre Jacob (Harvard)
Unbiased Markov chain Monte Carlo with couplings
E18-304, 11am – 12pm
Nov 3 Joan Bruna Estrach (NYU)
Statistics, Computation and Learning with Graph Neural Networks
E18-304, 11am – 12pm
Nov 17 Alex Dimakis (University of Texas at Austin)
Generative Models and Compressed Sensing
E18-304, 11am – 12pm
Dec 1 Susan Murphy (Harvard University)
Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time
E18-304, 11am – 12pm
Dec 8 Alex Bloemendal (Broad Institute)
Genome-wide association, phenotype prediction, and population structure: a review and some open problems
E18-304, 11am – 12pm

Spring 2017

Feb 3 Mayya Zhilova (Georgia Tech)
Non-classical Berry-Esseen inequality and accuracy of the weighted bootstrap
E18-304, 11am – 12pm
Feb 10 Pierre Bellec (Rutgers)
Slope meets Lasso in sparse linear regression
E18-304, 11am – 12pm
Feb 17 Frederick Eberhardt (CalTech)
Causal Discovery in Systems with Feedback Cycles
E18-304, 11am – 12pm
Feb 24 Yihong Wu (Yale)
Estimating the number of connected components of large graphs based on subgraph sampling
E18-304, 11am – 12pm
Mar 3 Alexander Barvinok (University of Michigan)
Computing partition functions by interpolation
E18-304, 11am – 12pm
Mar 10 Ankur Moitra (MIT)
Robust Statistics, Revisited
E18-304, 11am – 12pm
Mar 17 David Dunson (Duke)
Probabilistic factorizations of big tables and networks
32-141, 11am – 12pm
Mar 24 Shankar Bhamidi (UNC)
Jagers-Nerman stable age distribution theory, change point detection and power of two choices in evolving networks
E18-304, 11am – 12pm
Apr 7 David Steurer (Cornell)
Sample-optimal inference, computational thresholds, and the methods of moments
E18-304, 11am – 12pm
Apr 14 Daniel Hsu (Columbia)
Active learning with seed examples and search queries
E18-304, 11am – 12pm
Apr 28 Ronitt Rubinfeld (MIT)
Testing properties of distributions over big domains
32-141, 11am – 12pm
May 5 Sebastien Roch (Wisconsin)
Some related phase transitions in phylogenetics and social network analysis
E18-304, 11am – 12pm
May 12 Jonas Peters (University of Copenhagen)
Invariance and Causality
E18-304, 11am – 12pm
May 19 Vianney Perchet (ENS Paris-Saclay)
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
E18-304, 11am – 12pm

Fall 2016

Sep 9 Pierre Jacob (Harvard)
Couplings of Particle Filters
E18-304, 11am – 12pm
Sep 16 Lorenzo Rosasco (University of Genoa)
Less is more: optimal learning by subsampling and regularization
E18-304, 11am – 12pm
Sep 30 Bin Yu (UC Berkeley)
Theory to gain insight and inform practice: re-run of IMS Rietz Lecture, 2016
E18-304, 11am – 12pm
Oct 7 Mark Rudelson (University of Michigan)
Invertibility and Condition Number of Sparse Random Matrices
E18-304, 11am – 12pm
Oct 14 Mikhail Belkin (Ohio State University)
Eigenvectors of Orthogonally Decomposable Functions and Applications
E18-304, 11am – 12pm
Oct 21 Arian Maleki (Columbia)
On The Asymptotic Performance of fq-regularized Least Squares
E18-304, 11am – 12pm
Oct 28 Sourav Chatterjee (Stanford)
Matrix estimation by Universal Singular Value Thresholding
E18-304, 11am – 12pm
Nov 4 Po-Ling Loh (University of Pennsylvania)
Influence maximization in stochastic and adversarial settings
E18-304, 11am – 12pm
Nov 18 Liza Levina (University of Michigan)
Interpretable prediction models for network-linked data
E18-304, 11am – 12pm
Dec 2 Elchanan Mossel (MIT)
Shotgun Assembly of Graphs
E18-304, 11am – 12pm
Dec 16 Yash Deshpande (Microsoft Research)
Sparse PCA via covariance thresholding
E18-304, 11am – 12pm

Spring 2016

Feb 5 Andrew Nobel (UNC)
Large Average Submatrices of a Gaussian Random Matrix: Landscapes and Local Optima
32-123, 11am – 12pm
Feb 12 David Donoho (Stanford)
Incremental Methods for Additive Convex Cost Optimization
32-123, 11am – 12pm
Feb 19 Asu Ozdaglar (MIT)
Overcoming Overfitting with Algorithmic Stability
32-123, 11am – 12pm
Feb 26 John Lafferty (U Chicago)
On Shape Constrained Estimation
E18-304, 11am – 12pm
Mar 4 Shivani Agarwal (Indian Institute of Science/Radcliffe)
On Complex Supervised Learning Problems, and On Ranking and Choice Models
32-123, 11am – 12pm
Mar 18 Martin Wainwright (UC Berkeley)
Pairwise Comparison Models for High-Dimensional Ranking
32-123, 11am – 12pm
Apr 1 Roberto Oliveira (IMPA)
Sub-Gaussian Mean Estimators
32-123, 11am – 12pm
Apr 8 Tony Cai (U Penn)
Confidence Intervals for High-Dimensional Linear Regression: Minimax Rates and Adaptivity
32-123, 11am – 12pm
Apr 15 Gabor Szekely (NSF)
The Energy of Data
32-123, 11am – 12pm
Apr 22 Ryan Tibshirani (Carnegie Mellon)
Recent Advances in Trend Filtering
32-123, 11am – 12pm
Apr 29 Victor Chernozhukov (MIT)
Double Machine Learning: Improved Point and Interval Estimation of Treatment and Causal Parameters
32-123, 11am – 12pm
May 6 Rachel Ward (UT Austin)
Extracting Governing Equations in Chaotic Systems From Highly Corrupted Data
32-123, 11am – 12pm
May 13 David Blei (Columbia)
Scaling and Generalizing Variational Inference
32-123, 11am – 12pm

Fall 2015

Sep 11 Rob Freund (MIT Sloan)
An Extended Frank-Wolfe Method with Application to Low-Rank Matrix Completion
32-141, 11am-12pm
Sep 18 Mustazee Rahman (MIT Mathematics)
Independent sets, local algorithms and random regular graphs
32-141, 11am-12pm
Sep 25 Edo Airoldi (Harvard University)
Some Fundamental Ideas for Causal Inference on Large Networks
32-141, 11am-12pm
Oct 2 Constantine Caramanis (University of Texas at Austin)
Fast algorithms and (other) min-max optimal algorithms for mixed regression
32-141, 11am-12pm
Oct 9 Roman Vershynin (University of Michigan)
Discovering hidden structures in complex networks
32-141, 11am-12pm
Oct 16 Stefan Wager (Stanford University)
Causal Inference with Random Forests
32-141, 11am-12pm
Oct 23 Robert Nowak (University of Wisconsin)
Ranking and Embedding From Pairwise Comparisons
32-141, 11am-12pm
Oct 30 Rina Foygel Barber (University of Chicago)
MOCCA: a primal/dual algorithm for nonconvex composite functions with applications to CT imaging
32-141, 11am-12pm
Nov 6 Gábor Lugosi (Pompeu Fabra University)
On a High-Dimensional Random Graph Process
32-141, 11am-12pm
Nov 13 Jun Liu (Harvard University)
Expansion of biological pathways by integrative Genomics
32-141, 11am-12pm
Nov 20 James Robins (Harvard University)
Minimax Estimation of Nonlinear Functionals with Higher Order Influence Functions: Results and Applications
32-141, 11am-12pm
Dec 4 Eric Tchetgen Tchetgen (Harvard University)
Next Generation Missing Data Methodology
32-141, 11am-12pm
Dec 11 Peter Bartlett (UC Berkeley)
Efficient Optimal Strategies for Universal Prediction
32-141, 11am-12pm

Spring 2015

Feb 6 Denis Chetverikov (UCLA)
Central Limit Theorems and Bootstrap in High Dimensions
E62-450, 11am-12pm
Feb 13 Victor-Emmanuel Brunel (Yale)
Random polytopes and estimation of convex bodies
E17-133, 11am-12pm
Feb 27 Nike Sun (MSR New England and MIT Mathematics)
The exact k-SAT threshold for large k
E62-450, 11am-12pm
Apr 10 Moritz Hardt (IBM Almaden)
How good is your model? Guilt-free interactive data analysis
E62-450, 11am-12pm
Apr 17 Vianney Perchet (Université Paris Diderot)
From Bandits to Ethical Clinical Trials. Optimal Sample Size for Multi-Phases Problems
E62-450, 11am-12pm
Apr 24 Ankur Moitra (MIT CSAIL)
Tensor Prediction, Rademacher Complexity and Random 3-XOR
E62-450, 11am-12pm
May 1 Han Liu (Princeton)
Nonparametric Graph Estimation
E62-450, 11am-12pm
May 8 Lester Mackey (Stanford)
Measuring Sample Quality with Stein’s Method
E62-450, 11am-12pm

Fall 2014

Aug 15 Lenka Zdeborova (CEA)
Clustering of sparse networks: Phase transitions and optimal algorithms
E62-587, 3:15pm-4:15pm
Aug 15 Florent Krzakala (Université Pierre et Marie)
Superposition codes and approximate-message-passing decoder
E62-587, 2pm-3pm
Sep 23 Richard Nickl (University of Cambridge)
Uncertainty quantification and confidence sets in high-dimensional models
E62-587, 12pm – 1pm
Oct 10 Vladimir Koltchinskii (Georgia Tech)
Asymptotics and concentration for sample covariance
E62-650, 11am – 12pm
Oct 24 Anna Mikusheva (MIT Economics)
A Geometric Approach to Weakly Identified Econometric Models
E62-687, 11am – 12pm
Oct 31 Yuan Liao (University of Maryland)
High Dimensional Covariance Matrix Estimations and Factor Models
E62-587, 11am-12pm
Nov 7 Constantinos Daskalakis (MIT EECS)
Beyond Berry Esseen: Structure and Learning of Sums of Random Variables
E62-587, 11am-12pm
Nov 21 Alfred Galichon (Sciences Po, Paris)
Optimal stochastic transport
E62-587, 11am-12pm
Dec 5 Harrison Huibin Zhou (Yale University)
Sparse Canonical Correlation Analysis: Minimaxity and Adaptivity
E62-587, 11am-12pm
Dec 12 Whitney Newey (MIT Economics)
Linear Regression with Many Included Covariates
E62-587, 11am-12pm

Spring 2014

Feb 7 Michael Brautbar (MIT)
On the Power of Adversarial Infections in Networks
E62-587, 11am-12pm
Mar 7 Karthekeyan Chandrasekaran (Harvard)
Integer Feasibility of Random Polytopes
E62-587, 11am-12pm
Mar 21 Alexandre Tsybakov (CREST-ENSAE)
Linear and Conic Programming Approaches to High-Dimensional Errors-in-variables Models
E62-587, 11am-12pm
Apr 11 Alexander Rakhlin (University of Pennsylvania, The Wharton School)
Learning and estimation: separated at birth, reunited at last
E62-587, 11am-12pm
Apr 18 Sébastien Bubeck (Princeton University)
On the influence of the seed graph in the preferential attachment model
E62-587, 11am-12pm
Apr 25 Joel Spencer (Courant Institute, New York University)
Avoiding Outliers
E62-587, 11am-12pm
May 2 Sahand Negahban (Yale University)
Computationally and Statistically Efficient Estimation in High-Dimensions
E62-587, 2pm-3pm
May 16 Antar Bandyopadhyay (University of California, Berkeley)
De-Preferential Attachment Random Graphs
E62-587, 11am-12pm
May 23 Alex Belloni (Duke University)
Uniform Post Selection Inference for Z-estimation problems
E62-587, 11am-12pm
May 30 Nathan Kallus (MIT)
Regression-Robust Designs of Controlled Experiments
E62-587, 11am-12pm

Fall 2013

Sep 20 Elad Hazan (Technion)
Sublinear Optimization
Sep 27 Jim Dai (Cornell University)
Semimartingale reflecting Brownian motions: tail asymptotics for stationary distributions
E62-587, 11am-12pm
Nov 8 David Choi (Heinz College, Carnegie Mellon University)
Consistency of Co-clustering exchangeable graph data
E62-587, 11am-12pm
Nov 13 Lie Wang (MIT)
Multivariate Regression with Calibration
E62-587, 4pm-5pm
Nov 15 Ramon van Handel (Princeton University)
Conditional Phenomena in Time and Space
E62-587, 11am-12pm
Dec 13 Nelly Litvak (University of Twente)
Degree-degree dependencies in random graphs with heavy-tailed degrees
E62-587, 11am-12pm

Spring 2013

Feb 8 Rahul Jain (University of Southern California)
The Art of Gambling in a Team: Multi-Player Multi-Armed Bandits
Apr 12 Yashodhan Kanoria (MSR New England and Columbia University)
Which side chooses in large random matching markets?
May 3 Rahul Jain (University of Southern California)
Transitory Queueing Systems

Fall 2012

Nov 30th Rahul Mazumder (MIT)
Low-rank Matrix Completion: Statistical Models and Large Scale Algorithms
Nov 16th Kuang Xu (MIT)
Queueing system topologies with limited flexibility
Oct 26th Philippe Rigollet (Princeton University)
Optimal detection of a sparse principal component

Spring 2012

Mar 30 Dmitry Shabanov (Yandex and Moscow Institute of Physics and Technology)
Van der Warden Number and Coloring of Hypergraphs with Large Girth
Mar 29 Liudmila Ostroumova (Yandex and Moscow Institute of Physics and Technology)
An Application of Talagrand’s Inequality to Prove a Concentration of Second Degrees in Buckley-Osthus Random Graph Model
Mar 28 Daniil Musatov (Yandex and Moscow Institute of Physics and Technology)
Conditional Coding with Limiting Computational Resources
Mar 27 Andrei Raigorodksii (Yandex and Moscow Institute of Physics and Technology)
Web Graph Models and Their Applications
Mar 26 Andrei Raigorodksii (Yandex and Moscow Institute of Physics and Technology)
Research Groups at Yandex and Moscow Institute of Physics and Technology
Apr 20 Guy Bresler (University of California, Berkeley)
Information theory of DNA sequencing
Apr 27 Alexander Rybko (Institute for Information Transmission Problems, Russia)
Mean-field Limit for General Queueing Networks on Infinite Graphs
Apr 27 Semen Shlosman (CNRS, France and Institute for Information Transmission Problems, Russia)
The Coherence Phase Transition
May 18 Alexei Borodin (Massachusetts Institute of Technology)
Growth of random surfaces

Fall 2011

Oct 7 Erol Peköz (Boston University)
Asymptotics for preferential attachment random graphs via Stein’s method
Oct 21 Eitan Bachmat (Ben-Gurion University)
Does god play dice? An I/O scheduling and airplane boarding perspective
Dec 16 Yuan Zhong (MIT ORC)
Delay optimality in switched networks

Spring 2009

Mar 13 Mohsen Bayati (Microsoft Research New England)
Sequential algorithms for generating random graphs
Apr 3 Mokshay Madiman (Yale University)
A New Look at the Compound Poisson Distribution and Compound Poisson Approximation using Entropy
Apr 10 Scott Sheffield (MIT Math)
Fractional simple random walk
Apr 17 Vivek F. Farias (MIT Sloan)
The Smoothed Linear Program for Approximate Dynamic Programming
May 1 Vivek Goyal (MIT EECS)
On Resolution, Sparse Signal Recovery, and Random Access Communication

Fall 2008

Sep 23 Benoît Collins (University of Ottawa) Convergence of unitary matrix integrals

Spring 2008

Feb 29 Victor Chernozhukov (MIT Econ & ORC)
Quantile and Probability Curves without Crossing
Mar 21 Daron Acemoglu (MIT Economics)
Fragility of Asymptotic Agreement under Bayesian Learning
Apr 10 Peter Glynn(Stanford MS&E)
Bounds on Stationary Expectations for Markov Processes>
Apr 18 Ton Dieker(Georgia Tech I&SE)
Large deviations for random walks under subexponentiality: the big-jump domain
Apr 25 Edward Farhi (MIT Physics)
Quantum Computation by Adiabatic Evolution
May 23 Johan van Leeuwaarden (Eindhoven University of Technology, EURANDOM, NYU)
The Gaussian random walk, sampling Brownian motion, and the Riemann zeta function

Fall 2007

Nov 16 David Forney (MIT LIDS)
Exponential Error Bounds for Random Codes on the BSC

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