List of all recorded talks

Efficient Reconstruction of Random Multilinear FormulasIn the reconstruction problem for a multivariate polynomial $f$, we have blackbox access to $f$ and the goal is to efficiently reconstruct a representation of $f$ in a suitable model of computation. We give a polynomial time randomized algorithm for reconstructing random multilinear formulas. Our algorithm succeeds with high probability when given blackbox access to the polynomial computed by a random multilinear formula according to a natural distribution. This is the strongest model of computation for which a reconstruction algorithm is presently known, albeit efficient in a distributional sense rather than in the worstcase. Previous results on this problem considered much weaker models such as depth3 circuits with various restrictions or readonce formulas. Our proof uses ranks of partial derivative matrices as a key ingredient and combines it with analysis of the algebraic structure of random multilinear formulas. Partial derivative matrices have earlier been used to prove lower bounds in a number of models of arithmetic complexity, including multilinear formulas and constant depth circuits. As such, our results give supporting evidence to the general thesis that mathematical properties that capture efficient computation in a model should also enable learning algorithms for functions efficiently computable in that model.

New extension of the Weil bound for character sums with applications to codingThe Weil bound for character sums is a deep result in Algebraic Geometry with many applications both in mathematics and in the theoretical computer science. The Weil bound states that for any polynomial $f(x)$ over a finite field $\mathbb{F}$ and any additive character $\chi:\mathbb{F} \to \mathbb{C}$, either $\chi(f(x))$ is a constant function or it is distributed close to uniform. The Weil bound is quite effective as long as $\deg(f) \ll \sqrt{\mathbb{F}}$, but it breaks down when the degree of $f$ exceeds $\sqrt{\mathbb{F}}$. As the Weil bound plays a central role in many areas, finding extensions for polynomials of larger degree is an important problem with many possible applications. In this work we develop such an extension over finite fields $\mathbb{F}_{p^n}$ of small characteristic: we prove that if $f(x)=g(x)+h(x)$ where $\deg(g) \ll \sqrt{\mathbb{F}}$ and $h(x)$ is a sparse polynomial of arbitrary degree but bounded weight degree, then the same conclusion of the classical Weil bound still holds: either $\chi(f(x))$ is constant or its distribution is close to uniform. In particular, this shows that the subcode of ReedMuller codes of degree $\omega(1)$ generated by traces of sparse polynomials is a code with near optimal distance, while ReedMuller of such a degree has no distance (i.e. $o(1)$ distance) ; this is one of the few examples where one can prove that sparse polynomials behave differently from nonsparse polynomials of the same degree. As an application we prove new general results for affine invariant codes. We prove that any affineinvariant subspace of quasipolynomial size is (1) indeed a code (i.e. has good distance) and (2) is locally testable. Previous results for general affine invariant codes were known only for codes of polynomial size, and of length $2^n$ where $n$ needed to be a prime. Thus, our techniques are the first to extend to general families of such codes of superpolynomial size, where we also remove the requirement from $n$ to be a prime. The proof is based on two main ingredients: the extension of the Weil bound for character sums, and a new Fourieranalytic approach for estimating the weight distribution of general codes with large dual distance, which may be of independent interest.

Maximizing Expected Utility for Stochastic Combinatorial Optimization ProblemsWe study the stochastic versions of a broad class of combinatorial problems where the weights of the elements in the input dataset are uncertain. The class of problems that we study includes shortest paths, minimum weight spanning trees, and minimum weight matchings over probabilistic graphs, and other combinatorial problems like knapsack. We observe that the expected value is inadequate in capturing different types of {\em riskaverse} or {\em riskprone} behaviors, and instead we consider a more general objective which is to maximize the {\em expected utility} of the solution for some given utility function, rather than the expected weight (expected weight becomes a special case). We show that we can obtain a polynomial time approximation algorithm with {\em additive error} $\epsilon$ for any $\epsilon>0$, if there is a pseudopolynomial time algorithm for the {\em exact} version of the problem.(This is true for the problems mentioned above). Our result generalizes several prior results on stochastic shortest path, stochastic spanning tree, and stochastic knapsack.Our algorithm for utility maximization makes use of the separability of exponential utility and a technique to decompose a general utility function into exponential utility functions, which may be useful in other stochastic optimization problems.

Approximation Algorithms for Submodular Multiway PartitionWe study algorithms for the {\sc Submodular Multiway Partition} problem (\SubMP). An instance of \SubMP consists of a finite ground set $V$, a subset of $k$ elements $S = \{s_1,s_2,\ldots,s_k\}$ called terminals, and a nonnegative submodular set function $f:2^V\rightarrow \mathbb{R}_+$ on $V$ provided as a value oracle. The goal is to partition $V$ into $k$ sets $A_1,\ldots,A_k$ such that for $1 \le i \le k$, $s_i \in A_i$ and $\sum_{i=1}^k f(A_i)$ is minimized. \SubMP generalizes some wellknown problems such as the {\sc Multiway Cut} problem in graphs and hypergraphs, and the {\sc Nodeweighed Multiway Cut} problem in graphs. \SubMP for arbitrary submodular functions (instead of just symmetric functions) was considered by Zhao, Nagamochi and Ibaraki \cite{ZhaoNI05}. Previous algorithms were based on greedy splitting and divide and conquer strategies. In very recent work \cite{ChekuriE11} we proposed a convexprogramming relaxation for \SubMP based on the Lov\'aszextension of a submodular function and showed its applicability for some special cases. In this paper we obtain the following results for arbitrary submodular functions via this relaxation. \begin{itemize} \item A $2$approximation for \SubMP. This improves the $(k1)$approximation from \cite{ZhaoNI05}. \item A $(1.51/k)$approximation for \SubMP when $f$ is {\em symmetric}. This improves the $2(11/k)$approximation from \cite{Queyranne99,ZhaoNI05}. \end{itemize}

An FPTAS for #Knapsack and Related Counting ProblemsGiven n elements with nonnegative integer weights w_1,...,w_n and an integer capacity C, we consider the counting version of the classic knapsack problem: find the number of distinct subsets whose weights add up to at most C. We give the first deterministic, fully polynomialtime approximation scheme (FPTAS) for estimating the number of solutions to any knapsack constraint (our estimate has relative error $1\pm\epsilon$). Our algorithm is based on dynamic programming. Previously, randomized polynomialtime approximation schemes (FPRAS) were known first by Morris and Sinclair via Markov chain Monte Carlo techniques, and subsequently by Dyer via dynamic programming and rejection sampling. In addition, we present a new method for deterministic approximate counting using readonce branching programs. Our approach yields an FPTAS for several other counting problems, including counting solutions for the multidimensional knapsack problem with a constant number of constraints, the general integer knapsack problem, and the contingency tables problem with a constant number of rows. 40.

Approximation Algorithms for Correlated Knaspacks and NonMartingale BanditsIn the stochastic knapsack problem, we are given a knapsack of size B, and a set of items whose sizes and rewards are drawn from a known probability distribution. However, the only way to know the actual size and reward is to schedule the itemâ€”when it completes, we get to know these values. The goal is to schedule these items (possibly making adaptive decisions based on the sizes seen thus far) to maximize the expected total reward of items which successfully pack into the knapsack. We know constantfactor approximations when (i) the rewards and sizes are independent of each other, and (ii) we cannot prematurely cancel items after we schedule them. What can we say when either or both of these assumptions are relaxed? Related to this are other stochastic packing problems like the multiarmed bandit (and budgeted learning) problems; here one is given several arms which evolve in a specified stochastic fashion with each pull, and the goal is to (adaptively) decide which arms to pull, in order to maximize the expected reward obtained after B pulls in total. Much recent work on this problem focus on the case when the evolution of each arm follows a martingale, i.e., when the expected reward from one pull of an arm is the same as the reward at the current state. What can we say when the rewards do not form a martingale? In this paper, we give constantfactor approximation algorithms for the stochastic knapsack problem with correlations and/or cancellations. Extending ideas we develop for this problem, we also give constantfactor approximations for MAB problems without the martingale assumption. Indeed, we can show that previously proposed linear programming relaxations for these problems have large integrality gaps. So we propose new timeindexed LP relaxations; using a decomposition and â€œgapfillingâ€ approach, we convert these fractional solutions to distributions over strategies, and then use the LP values and the time ordering information from these strategies to devise randomized adaptive scheduling algorithms. We hope our LP formulation and decomposition methods may provide a new way to address other stochastic optimization problems with more general contexts.

Evolution with RecombinationValiant (2007) introduced a computational model of evolution and suggested that Darwinian evolution be studied in the framework of computational learning theory. Valiant describes evolution as a restricted form of learning where exploration is limited to a set of possible mutations and feedback is received by the survival of the fittest mutant. In subsequent work Feldman (2008) showed that evolvability in Valiant's model is equivalent to learning in the correlational statistical query (CSQ) model. We extend Valiant's model to include genetic recombination and show that in certain cases, recombination can significantly speedup the process of evolution in terms of the number of generations, though at the expense of population size. This follows by a reduction from parallel CSQ algorithms to evolution with recombination. This gives an exponential speedup (in terms of the number of generations) over previous known results for evolving conjunctions and halfspaces with respect to restricted distributions.

Applied Machine Learning: Algorithms, Practice and Theory  Predicting & Naive Bayes (Part 1)
 Decision theory
 Classification

Generative and discriminative models
 Naive Bayes, logistic regression, support vector machines (SVMs)
 Online learning: perceptron and passiveaggressive algorithms

Applied Machine Learning: Algorithms, Practice and Theory  Predicting & Naive Bayes (Part 2)Applied Machine Learning continued.

Applied Machine Learning: Algorithms, Practice and Theory  Predicting & Naive Bayes (Part 3)Applied Machine Learning continued.
 All Talks
 Efficient Reconstruction of Random Multilinear Formulas
 New extension of the Weil bound for character sums with applications to coding
 Maximizing Expected Utility for Stochastic Combinatorial Optimization Problems
 Approximation Algorithms for Submodular Multiway Partition
 An FPTAS for #Knapsack and Related Counting Problems
 Approximation Algorithms for Correlated Knaspacks and NonMartingale Bandits
 Evolution with Recombination
 Applied Machine Learning: Algorithms, Practice and Theory  Predicting & Naive Bayes (Part 1)
 Applied Machine Learning: Algorithms, Practice and Theory  Predicting & Naive Bayes (Part 2)
 Applied Machine Learning: Algorithms, Practice and Theory  Predicting & Naive Bayes (Part 3)