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Recent Trends in Theoretical Computer Science

The workshop will bring together leading researchers from different areas of Theoretical Computer Science with the goal of presenting the latest research to a broad TCS audience from Chicago and surrounding areas, and increasing the interaction between different areas of Theoretical Computer Science.

When: January 31st, 2020
Where: Toyota Technological Institute at Chicago (TTIC)
6045 S Kenwood Ave,
Chicago, IL 60637
Organizers: Arturs Backurs and Sepideh Mahabadi (TTIC)
Register
All researchers interested in theoretical computer science are invited to attend. Attending the workshop is free, but please register via this form before January 15th.
Poster
We also call for posters to be presented at the workshop. Please fill out this form by December 31st to present your poster.
The posters on works submitted to STOC 2020 are not allowed.
Confirmed Speakers:
• Sebastien Bubeck (Microsoft Research Redmond)
Non-Stochastic Multi-Player Multi-Armed Bandits

I will motivate a cooperative multi-player version of the classical multi-armed bandit problem. I will give a low-communication strategy for the players that achieves the optimal sqrt(T) regret. Finally I will present some tantalizing open problems for the scenario with *no* communication at all.

• Arkadev Chattopadhyay (Tata Institute of Fundamental Research)
• Ilias Diakonikolas (University of Wisconsin-Madison)
Distribution-Independent PAC Learning of Halfspaces with Massart Noise

I will describe recent algorithmic progress on the problem of learning halfspaces in the distribution-independent PAC model, when the labels of the examples have been corrupted by Massart noise.

• Sebastian Forster (University of Salzburg)
Computing and Testing Small Connectivity in Near-Linear Time and Queries via Fast Local Cut Algorithms

Consider the following "local" cut-detection problem in a directed graph: We are given a seed vertex x and need to remove at most k edges so that at most \nu edges can be reached from x (a "local" cut) or output \bot to indicate that no such cut exists. If we are given query access to the input graph, then this problem can in principle be solved without reading the whole graph and with query complexity depending on k and \nu. In this paper we consider a slack variant of this problem where, when such a cut exists, we can output a cut with up to O(k\nu) edges reachable from x.

We present a simple randomized algorithm spending O(k^2\nu) time and O(k\nu) queries for the above variant, improving in particular a previous time bound of (k^{O(k)}\nu) by Chechik et al. [SODA '17]. We also extend our algorithm to handle an approximate variant. We demonstrate that these local algorithms are versatile primitives for designing substantially improved algorithms for classic graph problems by providing the following three applications. (1) A randomized algorithm for the classic k-vertex connectivity problem that takes near-linear time when k=polylog(n), namely \tilde O(m+nk^3) time in undirected graphs and \tilde O(mk^2) time in directed graphs. Our techniques also yield an improved approximation scheme. (2) Property testing algorithms for k-edge and -vertex connectivity with query complexities that are near-linear in k, exponentially improving the state of the art. This resolves two open problems, one by Goldreich and Ron [STOC '97] and one by Orenstein and Ron [Theor. Comput Sci. '11]. (3) A faster algorithm for computing the maximal k-edge connected subgraphs, improving prior work of Chechik et al. [SODA '17].

• Ankit Garg (Microsoft Research India)
Learning arithmetic circuits in the average case via lower bounds

The problem of learning arithmetic circuits is the following: given a polynomial as a black box that is promised to have a small arithmetic circuit computing it, can we find this arithmetic circuit? This problem is hard in the worst case and so previous works have focused on regimes where the NP-hardness does not kick in (e.g. constant top fan-in etc.). We focus on the average case where one assumes certain non-degeneracy assumptions on the circuit (formally these amount to assuming the circuit parameters lie outside a certain variety and hence if they are chosen randomly according to any reasonable distribution, the non-degeneracy condition is satisfied whp). Kayal and Saha gave a meta framework for designing these algorithms for circuit classes where we have lower bounds. They carried this out for depth 3 circuits (sums of products of linear forms) and we (in joint work with Kayal and Saha) carry it out for depth 4 powering circuits (sums of powers of low degree polynomials). I will talk about the meta framework and then about our specific results. I will also talk about a potential application to learning mixtures of general Gaussians.

• Nika Haghtalab (Cornell University)
• Prahladh Harsha (Tata Institute of Fundamental Research)
• Piotr Indyk (Massachusetts Institute of Technology)
• Sanjeev Khanna (University of Pennsylvania)
• Pravesh Kothari (Carnegie Mellon University)
• Marvin Künnemann (Max Planck Institute for Informatics)
• Dakshita Khurana (Columbia University)
New Techniques for Zero-Knowledge Proofs

Zero-knowledge is one of the central areas of research in cryptography. A zero-knowledge proof allows a prover to convince a verifier about the truth of an NP statement while revealing no information beyond the truth of the statement. In the first part of this talk, I will give an overview of how zero-knowledge is defined, and of different techniques that we know, that help us build zero-knowledge proof systems. In the second part of the talk, I will give an overview of a new non-black-box simulation technique. I will describe how this gives us the first constructions of witness hiding proof systems requiring only a single message each from the verifier and the prover. Part 2 of the talk is based on joint work with Nir Bitansky and Omer Paneth.

• Tengyu Ma (Stanford University)
Designing Explicit Regularizers for Deep Models

I will outline some new results on designing explicit regularizers to improve the generalization performances of deep neural networks. We derive data-dependent generalization bounds for deep neural networks. We empirically regularize the bounds and obtain improved generalization performance, especially when we lack other sources of regularization, such as implicit/algorithmic regularization, data augmentation, etc. I will also touch on recent results on applying these techniques to imbalanced datasets.

Based on joint work with Colin Wei, Kaidi Cao, Adrien Gaidon, and Nikos Arechiga

• Rafael Oliveira (University of Toronto and University of Waterloo)
• Sofya Raskhodnikova (Boston University)
Approximating the Distance to Monotonicity of Boolean Functions

We study the problem of approximating the distance to monotonicity of Boolean functions over the hypercube domain. For a function f:{0,1}^n -> {0,1}, the distance to monotonicity of f is the minimum fraction of points at which f has to be modified to make it monotone. We give a nonadaptive algorithm that, given f:{0,1}^n -> {0,1} whose distance to monotonicity is at least \alpha, makes poly(n, 1/\alpha) queries and returns an O(\sqrt{n} polylog n)-multiplicative approximation to the distance to monotonicity of f. Furthermore, we show that for any constant k > 0, approximating the distance to monotonicity up to n^{0.5 - k}-factor requires 2^{n^k} nonadaptive queries, thereby ruling out a poly(n, 1/\alpha)-query nonadaptive algorithm for such approximations. This answers a question of Seshadhri (Property Testing Review '14) for the case of nonadaptive algorithms. Approximating the distance to a property is closely related to tolerantly testing that property. Our lower bound stands in contrast to standard (non-tolerant) testing of monotonicity that can be done nonadaptively with O(\sqrt{n} polylog n) queries.

We obtain our lower bound by proving an analogous bound for erasure-resilient testers. Our method yields the same lower bounds for other properties (unateness and being a k-junta). These lower bounds improve exponentially on the existing lower bounds for these properties.

Joint work with Ramesh Krishnan Pallavoor and Erik Waingarten

• Noga Ron-Zewi (University of Haifa)
Local Proofs Approaching the Witness Length

Interactive oracle proofs are a hybrid between interactive proofs and probabilistically-checkable proofs, where the prover is allowed to interact with a verifier by sending relatively long messages to the verifier, who in turn is only allowed to query a few of the bits that were sent.

In this work we construct, for any NP relation for which membership can be decided in polynomial-time and bounded polynomial space (e.g., SAT, Hamiltonicity, Clique, Vertex-Cover, etc.), interactive oracle proofs in which the proof length approaches the witness length. The number of rounds, as well as the number of queries made by the verifier, are constant. Our proof leverages recent constructions of high-rate locally testable tensor codes. In particular, we bypass the barrier imposed by the low rate of multiplication codes (e.g., Reed-Solomon, Reed-Muller or AG codes) - a core component in prior constructions.

Joint work with Ron Rothblum.

• Thatchaphol Saranurak (Toyota Technological Institute at Chicago)
• Balasubramanian Sivan (Google Research)
Strategizing against No-regret Learners

How should a player who repeatedly plays a game against a no-regret learner strategize to maximize his utility? We study this question and show that under some mild assumptions, the player can always guarantee himself a utility of at least what he would get in a Stackelberg equilibrium of the game. When the no-regret learner has only two actions, we show that the player cannot get any higher utility than the Stackelberg equilibrium utility. But when the no-regret learner has more than two actions and plays a mean-based no-regret strategy, we show that the player can get strictly higher than the Stackelberg equilibrium utility. We provide a characterization of the optimal game-play for the player against a mean-based no-regret learner as a solution to a control problem. When the no-regret learner's strategy also guarantees him a no-swap regret, we show that the player cannot get anything higher than a Stackelberg equilibrium utility.

Joint work with Yuan Deng, Jon Schneider

• Omri Weinstein (Columbia University)
An Adaptive Step Toward the Multiphase Conjecture

In 2010, Patrascu proposed the Multiphase problem as a candidate for proving *polynomial* lower bounds on the operational time of dynamic data structures, and showed it would imply unconditional lower bounds on many important dynamic data structure problems. After 10 years, there has been almost no progress on this conjecture. We show an ~\Omega(\sqrt{n}) cell-probe lower bound on the Multiphase problem for data structures with general (adaptive) updates, and queries with unbounded but "layered" adaptivity. This captures all known set-intersection data structures and significantly strengthens previous Multiphase lower bounds, which only captured non-adaptive data structures. Our main technical result is a communication lower bound on a 4-party variant of Patrascu's Number-On-Forehead game, using information complexity techniques.

Joint work with Young Kun Ko.

• Henry Yuen (University of Toronto)
Schedule
TBD